Internet use and gender wage gap: evidence from China

Journal for Labour Market Research

Table 5 The gender disparity in return to Internet use by age cohort

	(1)Born before 1969		(2) Born from 1970–1989		(3) Born after1990
	IV	FE	IV	FE	IV	FE
Internet use	28.375*** (3.41)	− 0.822 (− 1.32)	12.618*** (4.88)	0.341* (1.75)	− 24.696 (− 0.90)	1.322 (2.62)
Female	0.077 (0.28)		3.510*** (3.78)		− 22.009 (− 0.97)
Internet use × Female	− 23.690*** (− 3.25)	1.404 (1.14)	− 8.217*** (− 4.45)	0.011 (0.04)	24.273 (0.96)	− 1.207 (− 1.25)
First stage estimation
IV1	0.290*** (5.09)		0.088*** (3.32)		− 0.032 (− 0.44)
IV2	− 0.383*** (− 11.13)		− 0.236*** (− 9.99)		− 0.155* (− 1.91)
Control variables	Yes	Yes	Yes	Yes	Yes	Yes
Observations	3.512	3.512	12,701	12,701	2.168	2.168
Groups		2256		9229		1.676
R-sq. Between		0.230		0.198		0.200
Within		0.002		0.030		0.004
Overall		0.000		0.033		0.001
F test (Prob > F)		0.999		0.000		0.000
BP test (Prob > chibar2)		0.000 (p = 1.000)		49.00 (p = 0.000)		1.63 (p = 0.100)
Hausman test		67.74 (p = 0.000)		2736.48 (p = 0.000)		57.86 (p = 0.000)
Endogeneity test (DWH)	p = 0.000		p = 0.000		p = 0.083
Hansen J statistic	p = 0.554		p = 0.604		p = 0.625
Cragg-Donald Wald F statistic	11.590		20.405		11.201

Source: Author's 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 are not listed in the table owing to space limit constraints
IV: Instrumental variable method; FE: Fixed effects model; BP test: Breusch–Pagan Lagrange multiplier test; DWH: Durbin–Wu–Hausman test
***p < 0.01; **p < 0.05; *p < 0.1. t-values are shown in parentheses