In this study, we mainly focused on investigating the extent of integration of the IOR intra-regional trade network, identifying and analyzing the evolution of the trade community structures, and then evaluating the impact of the determinant factors on the formation of trade community structures of the IOR.
3.1. Extent of Integration of the IOR Intra-Regional Trade
We begin by investigating the evolution of the extent of trade integration within the IOR, based on its intra-regional trade networks from 1996 to 2017. The results of network density, clustering coefficient, average tie strength (i.e., average amount of trade flows), and trade share from 1996 to 2017 are presented in Figure 2
. For each year, the number of edges (i.e., the number of trade links), the average degree (i.e., the average number of trade partners, including the sum of exports and imports), and the intra-regional trade value are also reported in Table 3
The IOR intra-regional trade network grew rapidly, and the extent of trade integration improved markedly from 1996 to 2014. First, the IOR trade network became denser during this period as the number of trade flows grew from 926 in 1996 to 1707 in 2014. As a result, each country’s average number of trade partners increased from 41 to 70. Thus, the trade network density rose from 0.466 to 0.726. Second, the average amount of trade flows intensified, as indicated by the fact that the average tie strength rose from USD 17.14 million to USD 45.62 million. Third, the extent of multilateralism was fairly high and depicted an increasing trend, as suggested by the growth in the clustering coefficient (Figure 2
b). Finally, the IOR’s intra-regional trade dependency also increased, as shown by the upward trend in the intra-regional trade share in Figure 2
b. These results confirm that the extent of integration of the IOR intra-regional trade increased over this period.
In contrast, from 2015 to 2017, the IOR trade network shrank and the extent of regional trade integration generally declined. Due to the shrinking IOR trade network, the number of ties, the network density, the average tie strength, and the clustering coefficient decreased, which may be related to the decline in global bulk commodity prices, especially the sharp drop in global energy prices. Furthermore, the IOR intra-regional trade share generally showed a decreasing trend. These results indicate that, in general, the extent of IOR trade integration declined in this period.
It should also be noted that although the intra-regional trade network of the IOR shrank dramatically in 2009, along with the recession in the global trade network hit by the 2008 global financial crisis, it recovered robustly and almost all the statistics shown in Table 2
returned to the pre-crisis levels rapidly. These increasing trends indicated impressive resilience in the intra-regional trade in the IOR when affected by the global crisis in 2008. Although we can observe that the total trade value increased in 2017, it had not yet recovered to the 2014 level.
In general, the extent of the IOR intra-regional trade integration thus showed a strengthening trend during 1996–2017. This strengthening trend is combined with an in-crease in the number of ties, network density, average tie strength, clustering coefficient, and intra-regional trade share (Figure 2
By this point, we have already acquired an understanding of the evolution of the extent of trade integration and the general characteristics of the intra-regional trade flows within the IOR. We have found that the IOR intra-regional trade network changed dramatically over the period 1996–2017. However, how these changes in the IOR intra-regional trade networks translated into the dynamics of its trade community structures remains unclear. Therefore, in the next section, we investigate the evolution of the community structures of the intra-regional trade network within the IOR.
3.2. Spatiotemporal Evolution of the Community Structures of the IOR Intra-Regional Trade
This section investigates the community structures of the IOR intra-regional trade networks produced by the Louvain algorithm of the community detection method described in Section 2.3.2
. An efficient way to address the trade community structures is to adopt the visualization approach of the trade network, wherein countries within the same community are assigned the same color, the number of trade flows is depicted by lines with different thicknesses, and the size of the ISO3 code for each country represents the strength centrality (i.e., the sum of the value of exports and imports). This type of visualization with all elements helps identify the most prominent countries in the trade network and the most valuable bilateral trade flows and illustrates the trade pattern and depicts country clusters, providing us with insights into the community structures of the IOR intra-regional trade networks. Furthermore, we plot choropleth maps to visualize more intuitively and vividly the trade community structures of the IOR.
The number of annual trade networks is 22, but analyzing each network is somewhat redundant. To address this problem, we selected trade networks that can capture the main changes and main trends during the period 1996–2017. First, we selected the first and the last year (i.e., 1996 and 2017). Second, we selected 2009, the year wherein the IOR trade network shrank substantially, given the sharp drop in average tie strength shown in Figure 2
b. Third, we selected 2014, which was approximately the end year of the upward trend of the number of ties, network density, clustering coefficient, and value of intra-regional trade shown in Figure 2
and the modularity coefficient shown in Figure 3
f. Finally, we selected 2000, as the IOR intra-regional trade fluctuated prior to this year. Thus, we selected the trade networks for the years of 1996, 2000, 2009, 2014, and 2017, which capture the main changes and trends in the IOR intra-regional trade networks during the entire period. The trade community structures of the IOR and the statistics of each community for each corresponding year are depicted in Figure 3
, Figure 4
and Figure 5
At the beginning of the time series, the trade network of the IOR was dominated by the three largest trading countries, namely Singapore, Malaysia, and Thailand, characterized by the trade flows between Singapore and Malaysia and those between Singapore and Thailand. As shown in Figure 3
a and Figure 4
a, the trade network was partitioned into two clusters. One cluster comprised five major traders in East Asia and the Pacific region (i.e., Singapore, Malaysia, Thailand, Indonesia, and Australia) and four countries with relatively lower trade values belonging to other areas (Figure 3
a and Figure 4
a). Thus, we called this cluster the East Asia and Pacific cluster (the EAP cluster). Although there were only nine countries in the EAP cluster, it accounted for 55% of the IOR intra-regional trade (Figure 5
a), owing to the large value of trade flows, especially those between the five major traders within this cluster (Figure 3
a). Another cluster, called the large cluster (hereinafter the SWMS cluster), was composed of a large number of countries located in South Asia, the Middle East, and sub-Saharan Africa. These included India, United Arab Emirates (ARE was used for simplicity and consistency with the IOS3 code listed in Table 2
), Saudi Arabia (SAU), and South Africa as four leading countries (Figure 3
a and Figure 5
b). However, this large cluster’s total trade value accounted for only 16.78% of the IOR intra-regional trade due to the cluster’s relatively less connected trade network density(see Figure 5
a) and low value of trade flows (Figure 3
By 2000, the trade network had produced three trade communities (Figure 3
b and Figure 4
b) due to the rapid increase in the intra-regional trade links of the IOR. Two newly produced clusters, (1) the South Asia and the Middle East cluster (i.e., the SAE cluster), centered in India, ARE, and SAU (Figure 3
b and Figure 4
b), and (2) the sub-Saharan Africa cluster (i.e., the SSA cluster), centered in South Africa (Figure 3
b and Figure 4
b), were roughly split from the SWMS cluster in 1996. The EAP cluster, dominated by Singapore, Malaysia, Thailand, Indonesia, and Australia, still existed. However, the composition of countries changed, with Iran, Yemen, and Qatar leaving this cluster and Myanmar joining it (see Figure 3
b and Figure 4
In 2009 and 2014, although the IOR intra-regional trade networks still formed three trade communities that were generally similar to those in 2000 on a simple visual inspection (Figure 3
c,d), the positions of several countries showed some impressive changes. In 2009, India and ARE rose to be the second- and third-largest traders, respectively, with their more intensive trade links to trading partners (Figure 3
c). Furthermore, although Singapore remained the largest trading country within this region, the trade gap between India and Singapore was extremely low. In 2014, India rose to be the largest trader in the entire trade network, while Singapore fell to being the second-largest (Figure 3
d). Although ARE was still the third-largest, it became the largest exporter in the IOR intra-regional trade network. During this process, the rise in the positions of India and ARE was quite impressive. Additionally, we observe that the connectivity of trade links, and the dominant country’s trade share in each cluster, changed drastically (Figure 5
). The SAE cluster’s trade network density and trade value and the three dominant countries’ trade share showed an increasing trend. The SAE cluster’s trade share in the IOR intra-regional trade increased from 15.6% in 2000 to 37.02% in 2014 and exceeded that of the EAP cluster in 2014, suggesting the rising strength of the SAE cluster in the IOR intra-regional trade. In contrast, the EAP cluster’s trade share declined from 46.82% in 2000 to 28.75% in 2014. Although the SSF cluster’s trade network density and trade value increased, the SSF cluster constantly maintained a fairly low share, between 2.91% and 4.45%, which was probably related to the lack of more dominant trading countries with strong economic forces.
Despite the apparent complexity, there was an enhancement in the community structures of the IOR intra-regional trade networks from 1996 to 2014, as indicated by the upward trend in the modularity coefficient shown in Figure 3
f. Thus, the trade flows within each community of the IOR trade networks were becoming increasingly organized with the increasing trend in the network density (Figure 5
a). Each community in each year was determined by the dominant trading countries, whose strength centrality (i.e., the total value of imports and exports within each cluster) accounted for a high share of each community’s total trade (Figure 5
By 2017, the IOR trade network was reduced to two communities, along with a decline in trade links (Figure 3
e). The EAP community centered in Singapore, Malaysia, Thailand, Indonesia, and Australia existed robustly due to its denser and stronger trade ties within this community, which varied between 0.76 and 0.92 (Figure 3
e and Figure 5
a). In contrast, as shown in Figure 3
e and Figure 5
b, the SAE and SSA communities disappeared because of a decline in trade links within each cluster, and they were regrouped into a large cluster. The decreasing trend in the modularity coefficient from 2015 to 2017 (Figure 3
f) also indicated a weakening trend in the trade community structures within the IOR.
The community divisions in the IOR trade networks showed that the trade communities corresponded to geographical regions to some extent (Figure 4
). This indicates that geographical factors played an essential role in explaining the formation of the IOR trade community structures. However, almost all communities also consisted of countries located in different sub-regions of the IOR. Therefore, the formation of communities may not solely rely on geographical factors but may also rely on other economic conditions, cultural factors, and institutional factors. In the next section, the IOR’s trade community structures and community structures produced from several determinant variables will be compared to determine whether and how the formation of the IOR’s trade community structures is linked to these variables.
Finally, we applied the one-year NMI between the community structures of the IOR intra-regional trade in two consecutive years (Figure 6
) to measure the degree of dependence in the trade community structures. Although the one-year NMI varied between the lowest value of 0.35 and the highest value of 0.93, it generally remained relatively high, with an average value of 0.68 over the study period. This suggests that changes in the community structures of the IOR intra-regional trade networks occurred over more than one year.
3.3. Impact of the Determinant Factors on the Community Structures of the IOR Intra-Regional Trade
In this section, we employ the NMI to quantitatively evaluate the extent to which the IOR trade communities correlate with communities based on several determinant variables. The macro-area geographical partitions are shown in Figure 1
, and the corresponding community divisions of the other six variables are visualized in the choropleth maps in Figure 7
. The values of the NMI between the IOR trade communities and the communities produced by these determinant variables are depicted in Figure 8
The NMI values for comparison of communities produced by the three types of geographical proximity factors with the IOR trade communities are relatively close to each other, especially the curves of distance closeness and contiguity across all years, as shown in Figure 7
a. On average, the NMI value of macro-area geographical partitions and the IOR trade communities was the largest, and close to 0.48. In contrast, the NMI values for comparing contiguity network communities and distance closeness network communities with the corresponding trade communities were relatively lower, with 0.43 and 0.46, respectively, as average values. These results suggest that, despite the advances and development in transportation and communication technology, geography remained a friction in the IOR intra-regional trade, which may be largely due to the impediment of the Indian Ocean. Thus, countries within the IOR engaged in the intra-regional trade still tended to select geographically proximate trading partners. Thus, geographical proximity remains a concern in determining the formation of the trade communities of the IOR.
The average value of NMI between the IOR trade communities and common civilization communities was 0.45 and approached approximately that between communities of the IOR trade networks and geography-based communities. However, compared with partitions based on geographical proximity factors and common civilization, the average NMI value for comparing communities based on the common official language network and the IOR trade communities was relatively lower, merely 0.26. These results suggest that countries within the IOR were more likely to transact commodities with traders sharing a common civilization and a common official language; this is because they have similar lifestyles and tastes, generated by their similar social values [60
] as well as the ease of communication in a common language [56
]. Thus, cultural proximity factors play an important role in explaining the formation of the IOR trade community structures.
Comparing the IOR trade communities with the communities of regional organization membership using the NMI, the average value of NMI (0.42) indicates that countries’ decision on joining a common regional organization or not affected their decision to participate in the IOR intra-regional trade to some extent. This is because the regional organization provides the institutional foundation for more effective cooperation among members in specific areas, especially in trade promotion and investment. Therefore, more intra-regional institutional arrangements and cooperation (especially open trade policies and regional trade agreements) among members help the countries within the IOR advance their intra-regional trade and form more intensely connected trade communities.
Compared with average NMI values of the other six determinant variables, the average NMI value of 0.49 between the IOR trade communities and the communities of economic size obtained using a gravity model when controlling the distance effect was the largest among all the determinant variables, illustrating that the driver of the economic market size explains the formation of the IOR trade community structures more than the other six variables. Therefore, driven by the economic growth of the countries within the IOR, the possibility of a natural enhancement in the trade community structures of the IOR countries is feasible.
Overall, the general rising trend in the NMI values between 1996 and 2014 (Figure 8
) indicates that the IOR intra-regional trade increasingly relied on geographical proximity, cultural proximity, regional organization membership between trade partners, and their economic forces. However, the explanatory power of these variables dropped, as indicated by the declining trend in the NMI values from 2015 to 2017. A possible explanation might be the decline in imports and exports in all regions of the world due to the decrease in bulk commodity prices, especially the energy price. Thus, this result may suggest that the community structures of the IOR’s intra-regional trade were affected not only by the internal determinant factors but also, to some extent, by the external international market.