Understanding the Role of Humanistic Factors in Trade Network Evolution across the Belt and Road Initiative Countries Using the Exponential Random Graph Model

Table 1

Endogenous and exogenous network structure variables of directed network in the ERGM.

Category	Variable	Configuration	Variable meaning
Endogenous network structural variables	Edges		Similar to the intercept term in the regression model
	Mutual		Is it more likely that countries with reciprocal structures will trade with each other?
Exogenous network covariates	Netc(lang_off); Netc(lang_theno); Netc(comleg); Netc(comrelig); Netc(sibling_ever)		Will the two countries with common relationships (official language, spoken language, legal origin, religious belief, and sibling relationship) increase the probability of international trade?
Node attribute covariates	Main(tradefree); Main(invfree); Main(finfree)		Does the node with the attribute (liberalization of trade, investment, and finance) have high trade network expansibility?
	Diff(gdp); Diff(pop); Diff(entry_cost)		Are countries with different attributes (GDP, population, and entry cost) more inclined to trade?
	Homp(gdphigh); Homp(pophigh)		Are countries with the similar attribute (higher GDP and larger population) more likely to have trade relations?
	Send(gdphigh); Send(pophigh)		Are the countries with higher GDP or larger population more active and have more out-links?
	Recv(gdphigh); Recv(pophigh)		Are the countries with higher GDP or larger population more popular and have more in-links?

Note. The structural schematic diagram is obtained according to Lusher et al. [22].