Impact of the global mineral trade structure on national economies based on complex network and panel quantile regression analyses

Resources, Conservation & Recycling 154 (2020) 104637

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Impact of the global mineral trade structure on national economies based on complex network and panel quantile regression analyses

T

Xian Xia,b, Jinsheng Zhoua,*, Xiangyun Gaoa,b,*, Ze Wanga,b, Jingjian Sia,b a b

School of Economics and Management, China University of Geosciences, Beijing, 100083, China Key Laboratory of Carrying Capacity Assessment for Resource and Environment, Ministry of Natural Resources, Beijing, 100083, China

ARTICLE INFO

ABSTRACT

Keywords: Mineral resources Global trade Complex network Panel quantile regression

Mineral resources are considered the upstream raw materials of the industrial chain and represent an important material basis for economic sustainable development. Countries have different mineral resource endowments promote trade development. According to the theory of resource dependence and interdependence, countries have close trade relations, through which they can exchange resources to meet domestic needs and economic development. Different countries have different trading partners, media capabilities and importance in the networks, which make them different in the ability to obtain and provide resources, thus leading to different roles of countries and different influences on their GDP. Therefore, we construct global mineral trade networks and analyze their overall characteristics; we use network parameters to represent the roles of the countries and apply the panel quantile regression method to test the relationship between the roles of the countries and the GDPs. We find that a country's media capacity is beneficial to economic growth, and when a country's resources are readily accessible, it is adverse to the economic development; export is conducive to economic growth, while high dependence on external resources is not conducive to economic growth and it is good for a country's economy to have many important trading partners. In addition, countries with a high economic level should attach more importance to the strategic reserve of mineral resources, while countries with a low economic level should attach more importance to the development of an international market.

1. Introduction Sustainable development is a common goal worldwide (Zhang et al., 2019). Mining is one of the main activities for the world economy and creating social welfare (Amirshenava and Osanloo, 2019) and there is potential for mining, and the raw materials thereby accessed, to play a constructive role to help countries meet the defined sustainable development goals (Nansai et al., 2019). As the upstream raw materials of industrial chain, mineral resources are the basis of economic and social development, have given a strong impetus to the increase of China's GDP (Lei et al., 2013). In China, the imports and exports of mineral products account for 3.7 % of the GDP, making a significant contribution to its economy. Therefore, we should strengthen the rational use and optimized allocation of mineral resources, which need to better understand the current supply and demand situation of mineral resources (Liu et al., 2019) and the trade structure (Zhong et al., 2018). International trade solves the problem of different supply and demand of mineral resources, enabling countries to exchange to ensure domestic economic development. Most scholars have confirmed that trade and

⁎

the economy are inextricably linked (Ahmad et al., 2019; Dedeoglu and Kaya, 2013; Michelis and Zestos, 2004) (Chen and Dong, 2012; Dedeoglu and Kaya, 2013) and some scholars have found that a twoway relationship exists between international trade and economic growth (Shahbaz et al., 2013). However, these studies analyzed the relationship between trade and the economy from the perspective of independent countries. According to resource dependence theory (Pfeffer and Salancik, 2003) and interdependence theory (Allen, 2018), countries have close trade relations, and these complex relations often form a network structure. Countries differ in their ability to access and provide resources in the network, resulting in different roles in the networks. Therefore, the change in the trade roles between countries likely has an important impact on the national economy. Hence, it is imperative to study the impacts of the structures of global mineral trade networks on countries’ economies. Many factors affect economic development, including industrialization, urbanization, energy consumption (Nasreen et al., 2018; Ouyang and Li, 2018; Shahbaz et al., 2018), CO2 emissions (Ahmad and Zhao, 2018), financial development (Charfeddine and

Corresponding authors at: School of Economics and Management, China University of Geosciences, Beijing, 100083, China. E-mail addresses: [email protected] (J. Zhou), [email protected] (X. Gao).

https://doi.org/10.1016/j.resconrec.2019.104637 Received 22 July 2019; Received in revised form 2 December 2019; Accepted 4 December 2019 0921-3449/ © 2019 Elsevier B.V. All rights reserved.

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Kahia, 2019; Wang and Ran, 2019) and trade (Omri et al., 2015). Previous studies have adopted econometric methods, such as the GMM panel VAR approach, heterogeneous dynamic panel data analysis and PVAR, which are beneficial tools for studying the relationship between variables. Many scholars have also used econometric methods to study the relationship between trade and the economy. Some scholars have used the panel data model to study the relationship among export trade, import trade and the GDP (Chen and Dong, 2012), and some researchers have used heterogeneous dynamic panel data to study the influencing mechanisms of international trade, economic growth, etc. (Ahmad et al., 2019). These studies provide methodological support for our research. However, most existing studies have adopted the traditional OLS method for testing, but this approach only provides the conditional expectation (mean value) of the dependent variable and fails to describe the whole picture of the conditional distribution (Lin and Xu, 2017). The quantile regression method can more accurately describe the influence of an independent variable on the variation range and the shape of the conditional distribution of the dependent variable (Apergis et al., 2018; Wang et al., 2019), which can provide more information. Therefore, we choose the panel quantile regression method to study the relationship between the trade structure and economy. Previous studies have solely focused on the relationship between countries’ trade volume and their economy without systematically considering the impact of the countries’ role in the trade network on their economy. Countries have different degrees of influence in the trade network, different trade scopes, different abilities to obtain resources, etc.; thus, countries have different levels of influence on the economy. The ability to acquire resources and control resources introduces different risk levels (Yang et al., 2014), and thus, countries play different roles in trade. By accurately identifying the roles of countries in mineral trade structures, we can formulate better trade strategies to ensure the healthy operation of the national economy and reduce risk. Therefore, it is necessary to describe the trade relationships between countries and quantify the economic impact of the structures formed by these relationships. Complex network theory can well define the structural relationships in real networks (Du et al., 2017).Many scholars have used complex networks to reveal the essential structural characteristics of financial networks (Gao et al., 2017, 2018b; Ji et al., 2018; Xi and An, 2018), social networks (Luo et al., 2017), carbon emissions networks (Jiang et al., 2019), energy and ecological networks (Chen et al., 2018; Duan and Jiang, 2018; Gao et al., 2018a), etc. Especially in trade networks, scholars have explored the structural features (Gao et al., 2015; Zhang et al., 2019; Zhou et al., 2016), community characteristics (Zhong et al., 2017a) and national roles (Guan et al., 2016; Zhong et al., 2017b) of the networks. In addition, some scholars have examined the structure of mineral trade (Dong et al., 2018; Gao et al., 2015; Hou et al., 2018), thus providing a useful tool for our systematic study concerning the impact of the global mineral trade network structure on the national economy. Therefore, this paper combines complex network and panel quantile regression analyses to study the relationship between trade and the economy from a new perspective. In this paper, our aim is to quantify the impacts of the roles of countries in global mineral trade networks on the economy. First, we selected and processed trade data from the 2008–2017 period to obtain the final data. Then, we built global mineral trade networks and analyzed the network structures to identify the roles of different countries. Freeman proposed centrality measurements (Freeman, 1979) as the centralities of the countries in the networks can reflect the different roles of the countries; thus, we chose central indicators to characterize their roles. Finally, we used a panel quantile regression model to estimate the impacts of countries’ roles on the national economy.

2008 to 2017 from the UN Comtrade database. We aimed to study the network structure of mineral trade and its relationship with the economy during the stable period of economic development after the 2008 financial crisis and hoped to obtain universal results. The HS code is 26 and each country has an international ISO country code. The GDP and population data were downloaded from the World Bank. The GDP reflects the current prices in US dollars and is nominal GDP. The population refers to the actual number of people. The mining, manufacturing and utility data were downloaded from the United Nations Data Retrieval System. These data represent classes C–E in the international standard industrial classification, which is the sum of these three categories of output values. The Index of Economic Freedom data, which has 12 economic freedoms, were downloaded from the Heritage Foundation, and each country’s overall score was derived by averaging these twelve economic freedoms with an equal weight assigned to each freedom. 3. Methods 3.1. Global mineral trade network (GMTN) model Complex network models provide a perspective for exploring and analyzing the structural characteristics based on the many-to-many node-edge relationships. A complex network is composed of nodes and edges between nodes as follows: G = (V, E)

(1)

where V=(v1, v2, v3,…,vn), and n is the number of nodes; E = (e1, e2, e3,…, em), and m is the number of edges. We consider the countries nodes and the import and export relationships between countries edges. The trade value is defined as the weight of the edges. Thus, we obtain the weighted and directed networks each year from 2008 to 2017, which are called global mineral trade networks in this paper. The number of countries differs by year; thus, we use the number of countries that account for 99.9 % of the weight and the intersection to obtain 138 countries. Countries with missing data are deleted, and the final empirical test includes 133 countries. In addition, we calculate the structure indicators reflecting the roles of the countries in the network for the analysis. 3.2. Structural characteristics of the GMTN (1) Degree Degree refers to the number of edges connected to country i , i.e., the size of a country's trade. A directed digraph can be completely represented in terms of directed-adjacency matrix A, and the degree can be divided into in-degree and out-degree. The calculation formula is shown in Eqs. 2 and 3. N

kiin =

aji j=1

(2)

N

kiout =

aij j=1

(3)

where aji and aij indicate the elements of the directed-adjacency matrix A. (2) Weighted degree The weighted degree is the sum of trade value of all the edges of the node i. A directed-weighted digraph can be completely represented in terms of its weight matrix W. The weighted degree can be divided into the weighted in-degree and the weighted out-degree, the formula is:

2. Data

N

siin =

We downloaded mineral product trade data over 10 years from

wji j =1

2

(4)

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Table 1 Descriptive statistics of the variables.

N

siout =

wij

(5)

j =1

Variables

where wji and wij denote the element of the directed-weighted-adjacent matrix W. (3) Betweenness centrality Betweenness centrality measures the countries’ roles as mediators. If a country occupies an important position among two other countries in their contact path numerous times, the betweenness centrality of the country is high. The formula is as follows:

Bi =

(N

1 1)(N

Dependent variable GDP 1330 Independent variables ID 1330 OD 1330 WID 1330 WOD 1330 BC 1330 CC 1330 EC 1330 Control variables PP 1330 EEU 1330 EFI 1330

gjk (i ) 2)

j
gjk

(6)

where gjk is the number of shortest paths from country j to k, gjk (i ) is the number of the shortest paths between nodes j and k that pass node i , and N is the number of countries in the network. (4) Closeness centrality Closeness centrality considers the average length of the shortest path from each country to the other. This variable can examine the extent to which a country does not rely on other countries to propagate information. The formula is shown in Eq. 5:

CCi =

1 N 1 = N di d j = 1 ij

1

Ai, j ECj j Ni

Max.

Min.

Mean.

SD

1.95E+13

1.10E+09

5.37E+11

1.76E+12

109.000 71.000 8.63E+10 4.65E+10 5154.418 4.285 1.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000

13.389 13.333 1.51E+09 1.51E+09 197.242 2.162 0.219

17.862 14.067 7.79E+09 5.50E+09 521.125 0.621 0.248

1.41E+09 4.14E+12 90.143

3.11E+05 7.70E+07 1

5.07E+07 1.18E+11 61.752

1.65E+08 3.83E+11 11.936

centrality (EC), as the independent variables. In addition, we also selected other different network indicators as independent variables, such as weighted in-degree and weighted out-degree, for modeling analysis (see the appendix tables for details). The population reflects the humanity factors of a country and has an impact on economic development. Many scholars have confirmed that the population has some relations with the economy (Ahmed et al., 2016; Cruz and Ahmed, 2018). The variable mining, manufacturing and utilities (MMU) represents a country's mining and manufacturing development, and some scholars have shown that the manufacturing industry has an impact on economic growth (Gabriel and Ribeiro, 2019). We study mineral trade in our paper; thus, we selected these data. The Index of Economic Freedom (EFI) reflects the degree of foreign trade of a country. Economic freedom is an important factor influencing economic growth (De Haan et al., 2006; Spruk and Keseljevic, 2018). Therefore, we choose these factors as control variables. The descriptive statistics of these variables are shown in Table 1.

(7)

where N is the total number of countries in a network and dij is the distance between country i and country j . (5) Eigenvector centrality The eigenvector centrality of a node is usually used to measure the importance of its adjacent nodes. In trade networks, the eigenvector centrality indicates that countries have many important trade partners. Eigenvector centrality is calculated as follows (Bonacich and Lloyd, 2001):

ECi =

Obs.

(8)

3.4. Panel quantile regression

where is a constant, Ni is the set of nodes connected to node i , and Ai, j is the adjacent matrix of the network. If node j connects to node i , Ai, j = 1; otherwise, Ai, j = 0 (Fig. 1).

Since the data are not in the same order of magnitude, we carried out logarithmic processing of the data. We also carried out a unit root test of the variables, and both tests yielded passing results. Therefore, we directly established a regression equation after the logarithmic processing of the original data. In addition, we applied the Hausman test to the model and finally chose a random-effects panel regression model. Therefore, we used the correlated-random-effects (CRE) method first proposed by Abrevaya and Dahl (Abrevaya and Dahl, 2008) and elaborated upon by Bache (Bache et al., 2013). In order to avoid the problem of multicollinearity of independent

3.3. Variable selection To quantify the impacts of the network structure characteristics on the national economy, we build a panel data model. We use the GDP as the dependent variable and the network indicators representing the centrality of the nodes, such as the in-degree (ID), out-degree (OD), betweenness centrality (BC), closeness centrality (CC) and eigenvector

Fig. 1. Schematic of the selected network structure indicators. (a) Node A with two in-degree and a higher weighted in-degree; (b) Node B with two out-degrees and a higher weighted out-degree; (c) Node C with a higher betweenness centrality; (d) Node D with a higher closeness centrality; and (e) Node E with a higher eigenvector centrality.

3

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variables, we constructed 7 models (see the appendix tables for details). In this paper, we focused on the analysis of the indicators: ID, OD, BC, CC and EC. Based on the above analysis, we proposed the general panel regression model shown in Eq. 7 as follows:

+

lnGDPit = ait + 1 lnIDit + 2 lnODit + 3 lnBCit lnCC it + 5 lnECit + 6 lnPPit + 7 lnMMUit + 8 lnEFIit + eit 4

number of countries trading minerals was small, and the number of mineral trade relations (the number of edges) between the countries was only nearly 1700 largely because of the US subprime mortgage crisis in 2008, which led to the depression of mineral trade. Then, the trade relationships between countries gradually increased from 2009 to 2017 but declined in 2012 largely due to the European debt crisis in 2012, which led to a decrease in the number of mineral trading countries and further reduced the number of trade relations and the scale of the global mineral trade network. These two crises caused some exportoriented countries to expand their domestic demand and reduce their dependence on foreign trade. Fig. 2(b) shows the clustering characteristics and path of the network. These features are usually used to measure the small-world property of networks. Over time, the clustering coefficient shows a trend of volatility growth, and the path length is greatly reduced. Both trends slowed since 2014. This finding suggests that global mineral trade networks have gradually assumed the characteristics of a small world since 2008, improving the flow efficiency of minerals in the global network and confirming the view of trade globalization. We ranked the edges in the global mineral trade networks from 2008 to 2017 and found that less than 20 % of the edges have already occupied nearly 99 % of the total volume of the trade value as shown in Fig. 3. Less than 40 % of the edges have already occupied nearly 99.9 % of the total volume of the trade value. The other 60 % of the edges represent extremely weak connections and are relatively unimportant. The networks of trade relations between countries was almost fullyconnected networks, we could hardly find its essential characteristics. Therefore, we remained the edges which occupied 99.9 % of the total volume of the trade value in the final networks.

(9)

where i denotes the country, and t denotes the time; 1 9 denote the corresponding elastic coefficients, and eit is a random error term. The quantile regression allows for the coefficients to vary with multiple quantiles (Xu et al., 2017). Moreover, the quantile regression approach is useful for addressing problems that may severely affect the accuracy of the estimation, including heteroscedasticity, outliers, and unobserved heterogeneity (Koenker and K.F., 2001). This method has better applicability to our data; therefore, we adopt the quantile regression method to investigate the relationships between the trade network parameters and the national economy at different quantiles. The econometric model addresses the conditional quantile function of the panel data as follows:

Q yit ( x it ) = xit ( ) +

i

+

(10)

it

where Q yit ( x it ) indicates the dependent variable of the τth quantile; x it denotes the vector of the explanatory variables; ai refers to the individual effect; τ denotes the quantile; and ( ) represents the regression parameter of the τth quantile and can be computed through the following formula: q

T

N

( ) = arg min ( , )

(|yit

ai

x it ( )| wit )

k =1 t =1 i=1

(11)

where q denotes the number of quantiles; T refers to the number of years; N indicates the number of countries; and wit refers to the weight of the ith country in the tth year. The weight can be defined as follows: if yit ai xit ( ) 0 , if yit

Fig. 4 shows the changing role of the top 10 countries between 2008 and 2017. In Fig. 4(a), the ID in China is very high, indicating that China's demand for mineral products is much higher than that of other countries, followed by Germany. India has a growing number of import trade partners, while the number of mineral import partners has declined in the United States in recent years, which is related to the surge of the domestic mining industry and the increase in the domestic mineral supply. The number of importing partners of other countries has remained essentially unchanged. Compared with the ODs, as shown in Fig. 4(b), China has the fewest export partners, indicating that China is still a major importer of mineral products with a strong external dependence. The number of German export partners has tended to increase in recent years, which is related to the foreign trade growth policy in the EU. The number of export partners of the United States has sharply decreased largely because of the country’s “mineral reserve system”.

(12)

wit = wit = 1

4.2. Correlations between the network characteristics and GDP

ai

xit ( )

0,

,

(13)

which is consistent with the piecewise linear quantile loss function proposed by Koenker (Koenker, 2004). We assign the values of 0.1, 0.25, 0.5, 0.75 and 0.9 to the quantiles of τ. 4. Results and discussion 4.1. Overall structural characteristics of the GMTN Fig. 2 presents the overall structural properties of the global mineral trade networks by year. As clearly shown in Fig. 2(a), in 2008, the

Fig. 2. Overall structural characteristics of the networks. (a) Number of countries and trade relationships. (b) Average path length and average clustering coefficient of the networks. 4

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Fig. 3. Cumulative distribution of the trade value between countries in 2008–2017.

Fig. 4(c) and (d) show that the BC and CC significantly fluctuated from 2008 to 2017, indicating that the media capability and resource acquisition capacity of countries has changed over the past 16 years. China has the highest betweenness capacity, suggesting that China plays an important role as a bridge in global trade mineral networks. The intermediary capacity of the United States shows a downward trend. The top 10 countries in terms of closeness capability to resources

are mostly central European countries depending on their geographic location. These countries are small countries that are close to other countries and have easy to access to resources. In addition, these countries can spread information without relying on other countries. These capabilities may have implications for the national economies of these countries. Fig. 4(e) shows that the EC tended to fluctuate upward from 2008 to

Fig. 4. Changes in the roles of the top 10 countries in 2008–2017. 5

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Fig. 5. The cumulative distribution of weighted in (out)-degree in 2008 and 2017.

2017, indicating the increased importance of these countries. Similar to the IDs, China and Germany are ranked 1 and 2, respectively, indicating their great importance in the mineral trade network. This finding suggests that the countries that trade with China and Germany are also important in the mineral trade network. In general, China, the United States, Germany, Japan, Italy, the Netherlands and other countries play important roles in GMTNs. Fig. 5 shows the cumulative distributions of the WID and WOD in 2008 and 2017. From Fig. 5(a), we can see the total import value of the top 40 % countries is nearly 100 %, and from Fig. 5(b), the total export value of the top 47 % countries is about 100 %. These results indicate that a small number of countries account for a large proportion of trade volume. Moreover, our statistics show that in the past five years, the import value of minerals of CHN, JAN, KOR, DEU and IND ranked top five respectively, while the export value of mineral resources of AUS, BRA, CHL, PER and ZAF ranked top five in the world. It shows that these countries play important roles in the import and export trade of mineral resources. The correlations among the various variables are presented in Table 2. It was found that ID, OD, BC, EC, PP and MMU are strongly correlated with GDP, indicating that their changes can largely explain the changes in GDP. In addition, CC and EFI are significantly correlated with GDP. The indicators in the network represent the different roles of countries and show that the role of each trade country has an impact on its national economy. The variables PP, EFI and MMU reflect the human, economic and mining development of the countries; thus, we consider these variables. Therefore, we selected these eight variables for inclusion in the panel quantile regression. The weghted degree (WD), WID and WOD are also important indicators, and they have important impacts on GDPs, we consider these indicators in other models.

4.3. Impacts of the network structure on the GDP We selected five methods to conduct stationarity tests of the variables in the model, and all five methods passed, indicating that there was no unit root and that the sequence was stationary; thus, these variables could be directly used in the regression. The results are shown in Table 3. In general, we focus on comprehensively analyzing the impact of the network structure on the GDP. The results show that the impact of the network structure on the GDP differs across the quantiles. Additionally, we compared the QRA and OLS estimation results and found that QRA can describe the statistical distribution of the variables in more detail than OLS as shown in Table 4. 4.3.1. Low quantiles (0.1, 0.25) Under a lower economic condition corresponding to the 10th and 25th quantiles in Table 4, OD, EC and three control variables (PP, MMU and EFI) have significant positive impacts on the economy. The ID, BC and CC of a country have no effect on the economy, suggesting that when a country’s economic development level is relatively low, more export trade partners are needed to drive economic growth using foreign trade because exports can expand the international market, increase economies of scale, introduce technology and capital, and promote economic development. Furthermore, such countries should seek influential countries in the network, such as China, Germany and the United States, to improve their trade status and promote economic growth. Moreover, currently, PP, MMU and EFI play a very significant role in promoting the economy. Therefore, countries with low economic levels should attach importance to these aspects, increase their labor force, promote the development of mining, manufacturing and public utilities, and improve the level of opening up to drive economic growth.

Table 2 Correlations among various variables. 　

lnGDP

lnID

lnOD

lnDG

lnWID

lnWOD

lnWD

lnBC

lnCC

lnEC

lnPP

lnEFI

lnMIP

lnGDP lnID lnOD lnDG lnWID lnWOD lnWD lnBC lnCC lnEC lnPP lnEFI lnMIP

1 0.717** 0.464** 0.746** 0.619** 0.298** 0.597** 0.707** 0.089** 0.833** 0.664** 0.194** 0.961**

1 0.300** 0.665** 0.971** 0.142** 0.428** 0.786** 0.016 0.712** 0.336** 0.170** 0.703**

1 0.810** 0.200** 0.938** 0.744** 0.660** 0.784** 0.429** 0.400** 0.128** 0.444**

1 0.543** 0.626** 0.835** 0.855** 0.353** 0.790** 0.507** 0.210** 0.724**

1 0.069* 0.366** 0.701** −0.037 0.813** 0.268** 0.124** 0.616**

1 0.693** 0.483** 0.881** 0.255** 0.335** −0.009 0.296**

1 0.651** 0.331** 0.553** 0.516** 0.044 0.612**

1 0.281** 0.794** 0.441** 0.251** 0.676**

1 0.087** 0.132** −0.019 0.084**

1 0.427** 0.221** 0.811**

1 −0.161** 0.656**

1 0.128**

1

Note: ** indicates a significant correlation at the 0.01 level (bilateral). 6

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Table 3 Stationary tests of the variables. Variables

lnGDP lnID lnOD lnWID lnWOD lnBC lnCC lnEC lnPP lnMMU lnEFI

Test methods

Results

LLC

Breitung

IPS

ADF

PP

−49.0165*** −48.6266*** −50.318*** −45.582*** −49.954*** −50.216*** −48.0655*** −49.1998*** −48.549*** −50.1205*** −46.6138***

−27.9168*** −26.0121*** −11.4821*** −24.894*** −11.077*** −19.616*** −9.6076*** −21.9522*** −32.9285*** −29.2723*** −14.2173***

−40.0976*** −26.0121*** −42.8295*** −39.292*** −45.242*** −40.1068*** −43.6557*** −40.3391*** −39.327*** −38.3081*** −37.6064***

745.275*** 738.789*** 783.492*** 731.518*** 811.981*** 745.014*** 793.974*** 748.044*** 733.397*** 716.836*** 704.910***

745.180*** 747.011*** 784.963*** 733.576*** 817.078*** 748.827*** 806.867*** 750.795*** 733.221*** 718.301*** 705.646***

stationary stationary stationary stationary stationary stationary stationary stationary stationary stationary stationary

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

The OLS estimate results shown in Table 4 reveal that ID and CC have significant negative impacts on the GDP, which differs from the regression results of the 0.1 and 0.25 quantiles. EC has a high economic impact.

of influence of the control variables on the economy is smaller than that based on the estimation results of the 0.5 quantile. 4.3.3. High quantiles (0.75, 0.9) Under a higher economic condition that corresponds to the 75th and 90th quantiles, as shown in Table 4, ID still has a significant negative impact on the economy, illustrating that such countries still need to reduce their dependence on foreign minerals and increase their development of domestic mineral resources. There is no significant effect of the mediation capability on the economy. The impact of OD on the economy becomes negative when the quantile ranges from 0.75 to 0.9, suggesting that when economic development reaches a certain level, countries should increase their mineral reserves, expand the domestic market and reduce export trade partners and exports. When the quantile is 0.9, CC and EC have no impact on the economy, indicating that the country does not need to establish trade relations with more important countries, and has sufficient economic development capacity. The impacts of PP, MMU and EFI on the economy are also reduced. Based on the OLS estimation results shown in Table 4, the impacts of ID and BC are similar to the above estimation results, while the other variables have different impacts. In general, the OLS estimation results are accurate to a certain extent, but the QRA estimation is more robust than the OLS regression coefficient estimation. In general, among countries with different economic levels, the number of mineral import and export partners, the media’s role, the ability to obtain resources and the trade relationships with influential countries have different impacts on their economies. Countries with

4.3.2. Middle quantile (0.5) The 0.5 quantile indicates that under average economic conditions, as shown in Table 4, ID and CC have significant negative impacts on the economy, while OD, BC and EC have significant positive impacts on the economy. The stronger the dependence on the outside world, the more unfavorable the economic development of the country. Domestic resources are easily used by other countries, which is not good for national economic development. In the 0.5 quantile, OD and EC have higher impacts on the economy, indicating that countries with a medium economic level should pay more attention to expanding foreign trade and establishing trade partnerships with large countries. BC has a positive impact on the national economy, and countries at a medium economic level should also attach importance to their own media roles and enhance their roles bridges in the networks. These findings indicate that on the basis of steady economic development, middle economic level countries should actively expand foreign trade and reduce dependence on foreign resources. PP, MMU and EFI also have significant positive impacts on the economy, and currently, more attention should be paid to the development of MMU. Based on the OLS estimation results shown in Table 4, the impacts of ID, CC and EC are similar to the above estimation results, but OD and BC have no significant impact on the economy. In addition, the degree Table 4 Regression results of Model 1: QRA and OLS estimation statistical results. Variables

q10

q25

q50

q75

q90

OLS

lnID

−0.007 (-0.831) 0.020 (1.580) 0.004 (0.708) 0.002 (0.106) 0.092*** (4.439) 0.234*** (24.522) 0.698** (54.645) 1.179*** (12.680) −1.965** (-2.052)

−0.010 (-2.557) 0.019* (1.785) 0.003 (0.571) 0.003 (0.284) 0.150*** (3.688) 0.215*** (34.976) 0.686*** (41.513) 0.930*** (17.306) 0.824 (1.039)

−0.031** (-2.228) 0.041*** (4.192) 0.011* (1.895) −0.045*** (-3.467) 0.149*** (6.232) 0.110*** (29.058) 0.750*** (62.427) 0.425*** (7.392) 3.282*** (9.182)

−0.040** (-2.042) 0.049*** (2.866) 0.006 (1.242) −0.008** (-2.436) 0.180*** (4.720) 0.068*** (10.287) 0.770*** (81.023) 0.258*** (9.880) 3.911*** (5.461)

−0.043* (-1.800) −0.049** (-1.987) 0.009 (1.127) 0.022 (0.333) 0.348 (6.218) 0.114*** (8.997) 0.672*** (30.138) 0.336*** (11.460) 4.525*** (3.332)

−0.030** (-2.042) 0.020 (1.109) 0.006 (0.825) −0.032* (-1.689) 0.253*** (9.242) 0.137*** (12.145) 0.675*** (51.684) 0.389*** (10.446) 6.007*** (16.863)

lnOD lnBC lnCC lnEC lnPP lnMMU lnEFI C

Note: ***, ** and * indicate statistical significance at the 1 %, 5 % and 10 % levels, respectively. 7

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Fig. 6. Scheme of the quantile regression.

lower and middle economic levels should promote the growth of the domestic economy with more exports and establish trade network relationships with large countries. Countries with higher economic conditions need to increase their strategic reserves and expand their domestic markets. Countries should accurately identify their role in the networks, timely adjust the countermeasures of mineral trade, and promote economic growth. For example, China should reduce its dependence on foreign trade in mineral products, increase its export partners and drive economic growth. The United States needs to drive the development of neighboring countries and explore its domestic mineral resources. Fig. 6 represents plots of the regression estimates of all explanatory variables and control variables for quantiles ranging from 0.10 to 0.90. In each plot, the horizontal axis indicates the quantile scale, and the vertical axis indicates the relationship between the specific variable and the GDP (coefficient values). For each explanatory variable, the quantile estimation results are represented with a continuous line accompanied by the associated 95 % confidence interval, which is represented in the graphs between the two red lines. In addition, we also analyzed the impact of the indicators of weighted in-degree and weighted out-degree on GDP, as shown in the table. Because of the multicollinearity problem, we chose CC, PP and EFI for regression. As can be seen from Table 5, both the WID and the WOD have significant positive impacts on GDP. Therefore, countries at all economic levels should promote economic growth by exporting minerals and importing minerals needed by countries. Especially for countries

with high economic level, they need to increase import volume to ensure domestic economic needs, while for countries with medium economic level, they need to increase the export of mineral products to drive economic growth. In this model, it is found that CC has a significant negative impact on GDP, which indicates that when a country's resources are easily accessible, it is adverse to the economic development. PP and EFI have significant positive impacts on economic development, especially for countries with low economic level, which indicates that these countries still need to promote economic development through population increase and improve the level of external development. 5. Conclusions We mainly focus on quantifying the impacts of the structures of global mineral trade networks on the national economy. We construct global mineral trade networks based on complex network theory and analyze the structural characteristics and use network indicators to characterize the roles of the countries. And then we use a panel quantile regression model to estimate the impacts of the countries’ roles on the GDP. First, through an analysis of the overall characteristics of global mineral trade networks, we found that the scale of mineral trade is increasing and that the trade relationship between countries is becoming increasingly closer, indicating that global mineral trade is continuously developing. This finding provides a resource base for the development of the world economy and is conducive to resource

Table 5 Regression results of Modle 2: QRA and OLS estimation statistical results. Variables

q10

q25

q50

q75

q90

OLS

lnWID

0.068*** (24.394) 0.087*** (9.592) −0.381*** (-7.745) 0.779*** (85.580) 3.277*** (12.680) −8.593*** (-2.052)

0.085*** (29.034) 0.037*** (3.369) −0.103** (-1.801) 0.688*** (34.689) 2.885*** (9.622) −4.725*** (-2.691)

0.094*** (30.514) 0.111*** (13.088) −0.377*** (-10.263) 0.564*** (37.200) 1.925*** (7.085) 3.169** (2.200)

0.106*** (39.329) 0.120*** (10.714) −0.393*** (-8.110) 0.519*** (22.157) 0.953*** (8.374) 6.618*** (11.455)

0.110*** (59.618) 0.051*** (5.734) −0.121*** (-2.752) 0.657*** (63.651) 0.979*** (22.080) 5.834*** (6.845)

0.098*** (27.328) 0.083*** (7.622) −0.283*** (-5.859) 0.604*** (29.843) 1.125*** (14.735) 7.966*** (17.419)

lnWOD lnCC lnPP lnEFI lnC

Note: ***, ** and * indicate statistical significance at the 1 %, 5 % and 10 % levels, respectively. 8

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sharing and economic development. In addition, the clustering coefficient of the network is enhanced, and the path length is reduced, indicating that mineral trade networks could have small-world network characteristics and that the mineral trade between countries is more direct. The trade cost of mineral resources could be reduced, and information could spread faster. We also found that the top 40 percent of countries account for 99 percent of the total mineral trade, indicating that only a small number of countries play important roles in global mineral trade networks. Second, we used network indicators to characterize the different national roles, and over time, the roles of the countries had changed. Among these countries, China, Germany, the United States, Japan, Italy, India, Australia, etc., hold import power, media power, influence power and have other roles in the networks. Some countries play the role of export power and resource control, such as AUS, BRA, ZAF, BIH and BEL, which could have different impacts on the economic growth of various countries. Moreover, we analyzed the correlation between various variables and the GDP and found that the number of import partners, import value, intermediary capacity and importance of the countries are strongly correlated with the economy. Therefore, countries that need to expand imports should diversify import channels, and countries that export more should shift to domestic demand, stimulate domestic consumption and reduce external dependence. Countries with a greater influence, such as China and the United States, should drive the economic development of neighboring countries. Each country should accurately recognize its roles and adjust its current trade policy accordingly. Third, the panel quantile regression results showed that the different roles of the countries had significant impacts on the GDP. The estimated results of the panel quantile regression are more comprehensive and robust than those of OLS. In the different quantiles, the roles of the countries had different effects on the economy. The number of export partners, the trade value of export and import and importance of the countries can drive economic growth. Therefore, it is necessary to increase the number of export partners and further develop diversified export markets. And increase exports to drive economic growth, expand imports of domestic needed resources to ensure economic development. Dependence on foreign resources and easy access to resources by other countries had a negative economic impact. Hence, countries should reduce their external dependence, actively develop domestic mineral resources, and improve the utilization rate of resources. In addition, countries with different economic levels could have different impacts on GDP. Countries with a high economic level should attach more importance to the strategic reserve of mineral resources and expand domestic consumption, while countries with a low economic level should attach more importance to the development of the international market to promote economic development driven by exports. This study provides a new perspective for studies investigating the relationship between the mineral trade structure and the national economy that combine network and panel quantile regression analyses. However, the factors considered in our current study are not comprehensive and could thus be improved in future studies. Authors’ contributions: X.X., X.Y.G., and J.S.Z. designed the research; X.X.and X.Y.G. performed the research; X.X. collected and analyzed the data; Z.W. and J.J.S. reviewed the manuscript and proposed suggestions; X.X. wrote the paper. All authors discussed the results and revised the manuscript.

71991485), the Beijing Natural Science Foundation (Grant No. 9202013), the fund from Key Laboratory of Carrying Capacity Assessment for Resource and Environment, Ministry of Natural Resources (Grant No. CCA2019.01), the Humanities and Social Sciences planning funds project under the Ministry of Education of the PRC (Grant No. 17YJCZH047), the Fundamental Research Funds for the Central Universities (Grant No. 265208247) and Scientific Research Program Funded by Shaanxi Provincial Education Department (Program No.17JZ039). Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi: https://doi.org/10.1016/j.resconrec.2019. 104637. References Abrevaya, J., Dahl, C.M., 2008. The effects of birth inputs on birthweight: evidence from quantile estimation on panel data. J. Bus. Econ. Stat. 26 (4), 379–397. Ahmad, M., Zhao, Z.-Y., Irfan, M., Mukeshimana, M.C., 2019. Empirics on influencing mechanisms among energy, finance, trade, environment, and economic growth: a heterogeneous dynamic panel data analysis of China. Environ. Sci. Pollut. Res. Int. 26 (14), 14148–14170. Ahmad, M., Zhao, Z.Y., 2018. Empirics on linkages among industrialization, urbanization, energy consumption, CO2 emissions and economic growth: a heterogeneous panel study of China. Environ. Sci. Pollut. Res. 25 (30), 30617–30632. Ahmed, S.A., Cruz, M., Go, D.S., Maliszewska, M., Osorio-Rodarte, I., 2016. How significant is Sub-Saharan Africa’s demographic dividend for its future growth and poverty reduction? Rev. Dev. Econ. 20 (4), 762–793. Allen, M.A., 2018. The influence of regional power distributions on interdependence. J. Confl. Resolut. 62 (5), 1072–1099. Amirshenava, S., Osanloo, M., 2019. A hybrid semi-quantitative approach for impact assessment of mining activities on sustainable development indexes. J. Clean. Prod. 218, 823–834. Apergis, N., Can, M., Gozgor, G., Lau, C.K.M., 2018. Effects of export concentration on CO2 emissions in developed countries: an empirical analysis. Environ. Sci. Pollut. Res. 25 (14), 14106–14116. Bache, S.H.M., Dahl, C.M., Kristensen, J.T., 2013. Headlights on tobacco road to low birthweight outcomes Evidence from a battery of quantile regression estimators and a heterogeneous panel. Empir. Econ. 44 (3), 1593–1633. Bonacich, P., Lloyd, P., 2001. Eigenvector-like measures of centrality for asymmetric relations. Soc. Networks 23 (3), 191–201. Charfeddine, L., Kahia, M., 2019. Impact of renewable energy consumption and financial development on CO2 emissions and economic growth in the MENA region: a panel vector autoregressive (PVAR) analysis. Renew. Energy 139, 198–213. Chen, B., Li, J.S., Wu, X.F., Han, M.Y., Zeng, L., Li, Z., Chen, G.Q., 2018. Global energy flows embodied in international trade: a combination of environmentally extended input-output analysis and complex network analysis. Appl. Energy 210, 98–107. Chen, J.B., Dong, B., 2012. A nonparametric estimation on the effects of import and export trade to economic growth in China. In: Guo, H. (Ed.), 2012 International Workshop on Information and Electronics Engineering. Elsevier Science Bv, Amsterdam, pp. 952–956. Cruz, M., Ahmed, S.A., 2018. On the impact of demographic change on economic growth and poverty. World Dev. 105, 95–106. De Haan, J., Lundstrom, S., Sturm, J.E., 2006. Market-oriented institutions and policies and economic growth: a critical survey. J. Econ. Surv. 20 (2), 157–191. Dedeoglu, D., Kaya, H., 2013. Energy use, exports, imports and GDP: new evidence from the OECD countries. Energy Policy 57, 469–476. Dong, D., Gao, X.Y., Sun, X.Q., Liu, X.Y., 2018. Factors affecting the formation of copper international trade community: based on resource dependence and network theory. Resour. Policy 57, 167–185. Du, R.J., Wang, Y., Dong, G.G., Tian, L.X., Liu, Y.X., Wang, M.G., Fang, G.C., 2017. A complex network perspective on interrelations and evolution features crossMark of international oil trade, 2002-2013. Appl. Energy 196, 142–151. Duan, Y.W., Jiang, X.M., 2018. Visualizing the change of embodied CO2 emissions along global production chains. J. Clean. Prod. 194, 499–514. Gabriel, L.F., Ribeiro, L.C.D., 2019. Economic growth and manufacturing: an analysis using Panel VAR and intersectoral linkages. Struct. Change and Econ. Dyn. 49, 43–61. Gao, C.X., Su, B., Sun, M., Zhang, X.L., Zhang, Z.H., 2018a. Interprovincial transfer of embodied primary energy in China: a complex network approach. Appl. Energy 215, 792–807. Gao, C.X., Sun, M., Shen, B., 2015. Features and evolution of international fossil energy trade relationships: a weighted multilayer network analysis. Appl. Energy 156, 542–554. Gao, X.Y., Fang, W., An, F., Wang, Y., 2017. Detecting method for crude oil price fluctuation mechanism under different periodic time series. Appl. Energy 192, 201–212. Gao, X.Y., Huang, S.P., Sun, X.Q., Hao, X.Q., An, F., 2018b. Modelling cointegration and Granger causality network to detect long-term equilibrium and diffusion paths in the financial system. R. Soc. Open Sci. 5 (3), 16.

Declaration of Competing Interest There is no conflict of interest. Acknowledgments This research is supported by the National Natural Science Foundation of China (Grant No. 71991481, 41871202, 71991480, 9

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X. Xi, et al. Guan, Q., An, H.Z., Hao, X.Q., Jia, X.L., 2016. The impact of countries’ roles on the international photovoltaic trade pattern: the complex networks analysis. Sustainability 8 (4), 16. Hou, W.Y., Liu, H.F., Wang, H., Wu, F.Y., 2018. Structure and patterns of the international rare earths trade: a complex network analysis. Resour. Policy 55, 133–142. Ji, Q., Bouri, E., Roubaud, D., 2018. Dynamic network of implied volatility transmission among US equities, strategic commodities, and BRICS equities. Int. Rev. Financ. Anal. 57, 1–12. Jiang, M.H., An, H.Z., Gao, X.Y., Liu, S.Y., Xi, X., 2019. Factors driving global carbon emissions: a complex network perspective. Resour. Conserv. Recycl. 146, 431–440. Koenker, R., 2004. Quantile regression for longitudinal data. J. Multivar. Anal. 91 (1), 74–89. Koenker, R., K.F, H., 2001. Quantile regression. J. Econ. Perspect. 15 (4), 143–156. Lei, Y.L., Cui, N., Pan, D.Y., 2013. Economic and social effects analysis of mineral development in China and policy implications. Resour. Policy 38 (4), 448–457. Lin, B.Q., Xu, B., 2017. Which provinces should pay more attention to CO2 emissions? Using the quantile regression to investigate China’s manufacturing industry. J. Clean. Prod. 164, 980–993. Liu, D.H., Gao, X.Y., An, H.Z., Qi, Y.B., Sun, X.Q., Wang, Z., Chen, Z.H., An, F., Jia, N.F., 2019. Supply and demand response trends of lithium resources driven by the demand of emerging renewable energy technologies in China. Resour. Conserv. Recycl. 145, 311–321. Luo, S., Morone, F., Sarraute, C., Travizano, M., Makse, H.A., 2017. Inferring personal economic status from social network location. Nat. Commun. 8, 7. Michelis, L., Zestos, G.K., 2004. Exports, imports and GDP growth: causal relations in six european union countries. J. Econ. Asymmetries 1 (2), 71–85. Nansai, K., Kondo, Y., Giurco, D., Sussman, D., Nakajima, K., Kagawa, S., Takayanagi, W., Shigetomi, Y., Tohno, S., 2019. Nexus between economy-wide metal inputs and the deterioration of sustainable development goals. Resour. Conserv. Recycl. 149, 12–19. Nasreen, S., Saidi, S., Ozturk, I., 2018. Assessing links between energy consumption, freight transport, and economic growth: evidence from dynamic simultaneous equation models. Environ. Sci. Pollut. Res. 25 (17), 16825–16841. Omri, A., Daly, S., Rault, C., Chaibi, A., 2015. Financial development, environmental quality, trade and economic growth: what causes what in MENA countries. Energy Econ. 48, 242–252. Ouyang, Y.F., Li, P., 2018. On the nexus of financial development, economic growth, and energy consumption in China: new perspective from a GMM panel VAR approach.

Energy Econ. 71, 238–252. Pfeffer, J., Salancik, G.R., 2003. The external control of organizations: a resource dependence perspective. Social Science Electronic Publishing 23 (2), 123–133. Shahbaz, M., Khan, S., Tahir, M.I., 2013. The dynamic links between energy consumption, economic growth, financial development and trade in China: fresh evidence from multivariate framework analysis. Energy Econ. 40, 8–21. Shahbaz, M., Zakaria, M., Shahzad, S.J.H., Mahalik, M.K., 2018. The energy consumption and economic growth nexus in top ten energy-consuming countries: fresh evidence from using the quantile-on-quantile approach. Energy Econ. 71, 282–301. Spruk, R., Keseljevic, A., 2018. Economic freedom and growth across German districts. J. Inst. Econ. 14 (4), 739–765. Wang, F.Y., Ran, G.H., 2019. Excessive financial support, real estate development and macroeconomic growth: evidence from China. Emerg. Mark. Financ. Trade 55 (11), 2437–2447. Wang, S.J., Zeng, J.Y., Liu, X.P., 2019. Examining the multiple impacts of technological progress on CO2 emissions in China: a panel quantile regression approach. Renew. Sust. Energ. Rev. 103, 140–150. Xi, X., An, H.Z., 2018. Research on energy stock market associated network structure based on financial indicators. Physica A 490, 1309–1323. Xu, R.J., Xu, L., Xu, B., 2017. Assessing CO2 emissions in China’s iron and steel industry: evidence from quantile regression approach. J. Clean. Prod. 152, 259–270. Yang, Y.Y., Li, J.P., Sun, X.L., Chen, J.M., 2014. Measuring external oil supply risk: a modified diversification index with country risk and potential oil exports. Energy 68, 930–938. Zhang, C., Fu, J.S., Pu, Z.N., 2019. A study of the petroleum trade network of countries along "The Belt and Road Initiative". J. Clean. Prod. 222, 593–605. Zhong, W.Q., An, H.Z., Shen, L., Dai, T., Fang, W., Gao, X.Y., Dong, D., 2017a. Global pattern of the international fossil fuel trade: the evolution of communities. Energy 123, 260–270. Zhong, W.Q., An, H.Z., Shen, L., Fang, W., Gao, X.Y., Dong, D., 2017b. The roles of countries in the international fossil fuel trade: an emergy and network analysis. Energy Policy 100, 365–376. Zhong, W.Q., Dai, T., Wang, G.S., Li, Q.F., Li, D., Liang, L., Sun, X.Q., Hao, X.Q., Jiang, M.H., 2018. Structure of international iron flow: based on substance flow analysis and complex network. Resour. Conserv. Recycl. 136, 345–354. Zhou, M., Wu, G., Xu, H.L., 2016. Structure and formation of top networks in international trade, 2001-2010. Soc. Networks 44, 9–21.

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