Internet use and gender wage gap: evidence from China

Journal for Labour Market Research

Table 3 Frequency of Internet use for different purposes

	(1) Work and study		(2) Communication		(3) Living
	IV	FE	IV	FE	IV	FE
Internet use	2.020*** (4.96)	− 0.003 (−0.14)	2.540*** (5.59)	0.056* (1.94)	1.385*** (6.58)	0.010 (0.45)
Female	2.709*** (3.77)		2.987*** (4.21)		1.922*** (4.44)
Internet use × Female	− 1.317*** (− 4.66)	− 0.008 (− 0.29)	− 1.540*** (− 5.11)	− 0.016 (− 0.39)	− 0.797*** (− 5.93)	− 0.015 (− 0.54)
First stage estimation
IV1	0.204*** (4.66)		0.151*** (4.91)		0.411*** (9.78)
IV2	− 0.351*** (− 9.55)		− 0.249*** (− 9.63)		− 0.612*** (− 17.32)
Control variables	Yes	Yes	Yes	Yes	Yes	Yes
Observations	18,605	18,605	18,605	18,605	18,605	18,605
Groups		12,920		12,920		12,920
R-sq. Between		0.192		0.198		0.192
Within		0.015		0.030		0.015
Overall		0.007		0.033		0.006
F test (Prob > F)		0.000		0.000		0.000
BP test (Prob > chibar2)		73.70 (p = 0.000)		77.77 (p = 0.000)		77.22 (p = 0.100)
Hausman test		2406.81 (p = 0.000)		2355.36 (p = 0.000)		2380.03 (p = 0.000)
Endogeneity test (DWH)	p = 0.000		p = 0.000		p = 0.000
Hansen J statistic	p = 0.917		p = 0.082		p = 0.482
Cragg-Donald Wald F statistic	18.682		26.134		47.343

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 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