Internet use and gender wage gap: evidence from China

Journal for Labour Market Research

Table 2 The gender differences in return to Internet use

	(1) OLS		(2) IV		(3) LV		(4) RE		(5) FE
	Coef	t	Coef	t	Coef	t	Coef	t	Coef	t
Internet use	0.603***	6.68	14.900***	5.59	0.107	0.98	0.606***	6.65	0.405**	2.26
Female	− 0.849***	− 11.29	4.054***	4.40	− 0.697***	− 5.76	− 0.844***	− 10.90	–
Internet use × Female	0.540***	5.19	− 8.881***	− 5.04	0.360**	2.51	0.531***	5.02	− 0.106	− 0.42
Control variables	Yes		Yes		Yes		Yes		Yes
First stage estimation
IV1			0.083***	3.47
IV2			− 0.243***	− 11.58
Observations	18,381		18,381		7777		18,381		18,381
R-squared	0.476				0.367		12,876		12,876
R-sq. Between							0.190		0.197
Within							0.574		0.017
Overall							0.476		0.008
F-test (Prob > F)									0.000
BP test (Prob > chibar2)							66.51 (p = 0.000)
Hausman test (Prob > chi2)									2308.67 (p = 0.000)
Endogeneity test (DWH)			69.216 (p = 0.000)
Hansen J statistic			p = 0.224
Cragg-Donald Wald F statistic			26.393

Source: Authors’ Calculations Based on the data from CFPS of 2014, 2016 and 2018
Control variables, including years of schooling, years of work experience and its squared term, party membership, urban, occupation, industry sector, workplace ownership, region, and survey year variables, have been calculated, but the results are not listed in the table owing to space limit constraints
OLS: ordinary least squares; IV: instrumental variable method; LV: lagged variable model; RE: random effects model; FE: fixed effects model; BP test: Breusch–Pagan Lagrange multiplier test; DWH: Durbin–Wu–Hausman test; IV1: the provincial optical cable circuit in 1999; IV2: the provincial long-distance cable line length in 1999
***p < 0.01; **p < 0.05; *p < 0.1