Understanding the Role of Humanistic Factors in Trade Network Evolution across the Belt and Road Initiative Countries Using the Exponential Random Graph Model
Table 3
The estimated results of the cross-sectional ERGM (2019).
M(1)
M(2)
M(3)
M(4)
M(5)
M(6)
Edges
−2.9976 (0.0911)
−9.7759 (0.0518)
−9.8578 (0.0487)
−9.8726 (0.0499)
−11.5684 (0.0531)
−10.4221 (0.0497)
Mutual
4.7130 (0.1856)
3.9987 (0.0305)
4.0140 (0.0165)
4.0022 (0.1905)
3.9846 (0.0289)
3.9773 (0.1936)
Main(tradefree)
0.0353 (0.0016)
0.0361 (0.0016)
0.0341 (0.0018)
0.0465 (0.0018)
0.0394 (0.0017)
Main(invfree)
−0.0016 (0.0027)
−0.0030 (0.0027)
−0.0022 (0.0029)
−0.0018 (0.0028)
−0.0061 (0.0030)
Maim(finfree)
0.0037 (0.0033)
0.0047 (0.0031)
0.0056 (0.0035)
0.0026 (0.0034)
0.0073 (0.0034)
Diff(popd)
0.0003 (0.0002)
0.0003 (0.0002)
0.0004 (0.0002)
0.0005 (0.0002)
0.0002 (0.0002)
Diff(gdpd)
0.0002 (0.0000)
0.0002 (0.0000)
0.0002 (0.0000)
0.0002 (0.0000)
0.0002 (0.0000)
Diff(entry_cost)
−0.0047 (0.0041)
−0.0043 (0.0039)
−0.0020 (0.0036)
−0.0030 (0.0041)
−0.0023 (0.0037)
Homo(gdphigh)
0.1179 (0.0817)
0.1502 (0.0839)
0.1166 (0.0789)
0.1416 (0.0850)
0.0980 (0.0819)
Homo(pophigh)
0.1746 (0.0869)
0.1515 (0.0814)
0.1541 (0.0854)
0.1613 (0.0823)
0.1515 (0.0845)
Send(gdphigh)
0.5787 (0.1080)
0.5378 (0.0997)
0.6030 (0.1091)
0.6165 (0.1069)
0.6321 (0.1001)
Send(pophigh)
0.6110 (0.0937)
0.6473 (0.0967)
0.5883 (0.0980)
0.5880 (0.0989)
0.6442 (0.1006)
Recv(gdphigh)
0.9457 (0.1073)
0.9050 (0.1025)
0.9837 (0.1018)
0.9649 (0.1067)
0.9990 (0.1024)
Recv(pophigh)
0.4949 (0.0968)
0.5120 (0.0969)
0.4345 (0.1007)
0.4677 (0.0998)
0.4889 (0.0990)
Netc(comlang_off)
0.6127 (0.1129)
1.0474 (0.1347)
Netc(comlang_ethno)
0.7464 (0.1172)
Netc(comleg)
0.5236 (0.0770)
Netc(comrelig)
1.0175 (0.1212)
Netc(sibling_ever)
0.7326 (0.0889)
AIC
2961.5634
2261.1183
2278.3272
2268.6251
2253.0626
2257.5920
BIC
2980.1791
2354.1965
2371.4055
2361.7034
2346.1409
2350.6703
Note.;;; the values in parentheses are standard errors.
