Can Highway Networks Promote Productivity? Evidence from China

Table 14

Result of sample regression according to the relative centrality of Chinese cities.

Variables	(1)	(2)	(3)
	TFP	TFP	TFP
	1–100	100–200	>200
MP	2.3060	2.6203	−0.2615
	(17.0725)	(11.7148)	(−2.4581)
Age	0.0577	0.0811	0.0684
	(26.6294)	(22.4496)	(12.4437)
Scale	0.2386	0.2063	0.1652
	(145.5082)	(76.5766)	(35.1294)
Export	−0.0011	0.0545	0.0313
	(−0.3906)	(7.4845)	(2.7186)
Own state	−0.0898	−0.0661	−0.0899
	(−10.7700)	(−4.7287)	(−4.7088)
GDP	0.2209	0.2024	0.0003
	(21.7702)	(18.7106)	(0.0157)
R&D	0.0248	0.0240	0.0043
	(15.3662)	(7.4162)	(1.0621)
POP	0.0047	0.0150	−0.0111
	(3.8057)	(7.1742)	(−3.1779)
FDI	0.0363	−0.0130	0.0128
	(21.3190)	(−6.0469)	(4.4254)
Railway	−0.1590	0.0589	0.0542
	(−20.9864)	(6.2748)	(5.7164)
Constant	−26.7496	−30.3104	3.7034
	(−17.5739)	(−11.9787)	(3.0528)
Firm fixed effect	Yes	Yes	Yes
Time fixed effect	Yes	Yes	Yes
Observations	2,159,736	615,938	251,372
R-squared	0.0890	0.1265	0.0877

Note. Standard errors are stated in parentheses below point estimates. 1%, 5%, and 10% significance levels.