Internet use and gender wage gap: evidence from China

Journal for Labour Market Research

Table 4 The gender differences in return to Internet use by educational group

	(1) Low		(2) Middle		(3) High
	IV	FE	IV	FE	IV	FE
Internet use	− 11.427 (−0.94)	0.496 (0.93)	10.277*** (4.60)	0.475** (2.22)	19.852** (2.37)	− 0.083 (− 0.14)
Female	− 2.598 (− 1.41)		3.403*** (3.50)	16.978** (2.25)
Internet use × Female	9.648 (− 0.97)	0.139 (0.21)	− 6.856*** (− 4.40)	− 0.370 (− 1.21)	− 18.215 (− 2.28)	− 0.033 (− 0.04)
First stage estimation
IV1	− 0.167*** (− 3.71)		0.165*** (5.40)
IV1	− 0.167*** (− 3.71)		0.165*** (5.40)		0.466*** (4.58)
IV2	− 0.060 (− 1.23)		− 0.277*** (− 10.83)
IV2	− 0.060 (− 1.23)		− 0.277*** (− 10.83)		− 0.472*** (− 5.70)
Control variables	Yes	Yes	Yes	Yes	Yes	Yes
Observations	4652	4652	10,284	10,284	3445	3445
Groups		3289		7070		1905
R-sq. Between		0.017		0.200		0.200
Within		0.008		0.004		0.004
Overall		0.000		0.001		0.001
F test (Prob > F)		0.000		0.000		0.000
BP test (Prob > chibar2)		0.000 (p = 0.987)		17.67 (p = 0.000)		19.58 (p = 0.000)
Hausman test		248.75 (p = 0.000)		874.72 (p = 0.000)		874.72 (p = 0.000)
Endogeneity test (DWH)	p = 0.131		p = 0.000		p = 0.000
Hansen J statistic	p = 0.001		p = 0.652		p = 0.127
Cragg-Donald Wald F statistic	1.199		21.914		19.93

Source: Authors’ Calculations Based on the data from CFPS of 2014, 2016 and 2018
Low: elementary school and lower; middle: junior and senior high school; high: college and higher
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
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