Trade unions and the gender wage gap: evidence from China

Figures & data

Figure 1. Kernel density of wage by union membership and gender. (a) Union members, (b) Non-union members.

Notes: M: Men; F: Women.

Table 1. Gender disparities in individual characteristics among union and non-union members.

	Union members				Non-union members
	Men	Women	G1=M-F	t-test	Men	Women	G2=M-F	t-test
Education	12.81	13.91	−1.10	***	10.48	10.85	−0.38	***
Experience	26.09	21.46	4.63	***	23.14	20.78	2.36	***
Han ethnicity	0.97	0.97	0.00		0.96	0.95	0.01	**
Health	0.28	0.24	0.04	***	0.48	0.41	0.07	***
Urban	0.95	0.97	−0.02		0.46	0.77	−0.31	***
Married	0.88	0.85	0.03	***	0.42	0.76	−0.34	***
Party	0.31	0.21	0.10	***	0.38	0.09	0.29	***
Occupation
Manager	0.10	0.07	0.03	***	0.09	0.05	0.04	***
Technician	0.18	0.37	−0.19	***	0.12	0.20	−0.08	***
Clerk	0.20	0.20	0.00		0.13	0.13	0.00
Operator	0.38	0.15	0.23	***	0.45	0.23	0.22	***
Other	0.13	0.21	−0.08	***	0.20	0.40	−0.20	***
Industrial sector
Manufacture	0.38	0.30	0.08	***	0.37	0.31	0.06	***
Traffic and information	0.08	0.04	0.04	***	0.11	0.04	0.07	***
Retail	0.05	0.12	−0.07	***	0.10	0.23	−0.12	***
Service	0.25	0.39	−0.14	***	0.20	0.29	−0.09	***
Other	0.24	0.15	0.09	***	0.22	0.13	0.09	***
Public sector	0.72	0.69	0.03		0.34	0.32	0.02	***
Region
East	0.52	0.51	0.01		0.54	0.57	−0.03	***
Central	0.33	0.33	0.00		0.31	0.30	0.01	*
West	0.15	0.15	0.00		0.15	0.14	0.01	*
Obs.	1,429	889			7,248	5,053	14,619

Notes: ***p < 0.01; *p < 0.1.

Table 2. Gender disparities in union wage premium.

	(1) Nation			(2) Public Sector			(3) Private Sector
	Coef.		z	Coef.		z	Coef.		z
Union	0.057	**	2.56	0.037		1.41	0.092	**	2.12
Woman	−0.273	***	−16.25	−0.136	***	−4.93	−0.332	***	−16.19
Union×Woman	0.077	**	2.26	0.027		0.66	0.058		0.90
Control variables	Yes			Yes			Yes
No. of sample	14,619			5,801			8,818
No. of group	8,269			2,943			5,724
R-sq.
Within	0.151			0.184			0.107
Between	0.213			0.227			0.205
Overall	0.229			0.235			0.215
BP test (Prob > chibar2)	1064.04 (p = 0.000)			574.2 (p = 0.000)			393.52 (p = 0.000)

Notes: Union×Woman, interaction term of union and female worker dummy variables; BP test, Breusch and Pagan Lagrangian multiplier test for random effects. Control variables compose of demographic factors such as education, years of work experience and squared term, health status, ethnicity, urban hukou, married, CPC membership, work-related factors such as occupation, industrial sector, region, and year dummy variables; the public sector dummy variable was added for all samples in Column 1. All standard errors were adjusted as cluster-robust standard error clustering on the individual level.

***p < 0.01; **p < 0.05.

Table 3. Robustness test of the gender gap in union wage premiums.

	(1) Nation			(2) Public Sector			(3) Private Sector
	Coef.		z	Coef.		z	Coef.		z
(a) OLS
Union	0.065	***	3.03	0.065	**	2.50	0.085	**	2.08
Woman	−0.271	***	−18.48	−0.133	***	−5.60	−0.334	***	−18.10
Union×Woman	0.127	***	3.91	0.043		1.06	0.107	*	1.79
(b) Heckman two stage
Union	0.061	***	2.69	0.048	*	1.76	0.096	**	2.23
Woman	−0.246	***	−10.98	−0.199	***	−5.50	−0.229	***	−7.60
Union×Woman	0.119	***	3.43	0.068		1.57	0.062		0.99
Correction term	−0.275	*	−1.79	0.778	**	2.26	−1.102	***	−4.44
(c) LVt-1 method
Union	0.073	***	2.60	0.123	***	3.70	0.033		0.62
Woman	−0.26	***	−12.81	−0.126	***	−3.98	−0.336	***	−12.80
Union×Woman	0.081	*	1.95	−0.008		−0.10	0.106		1.37
(d) FE model
Union	0.047		1.48	−0.027		−0.71	0.150	**	2.46
Woman	-			-			-
Union×Woman	−0.036		−0.76	0.007		0.12	−0.114		−1.25
Hausman test (Chi2)	145.77			91.17			94.42
Prob>chi2	0.000			0.000			0.000
(e) Replace with samples aged 16–50 (RE)
Union	0.033		1.24	−0.008		−0.27	0.114	**	2.19
Woman	−0.289	***	−16.24	−0.156	***	−5.19	−0.344	***	−16.03
Union×Woman	0.108	***	2.92	0.078	*	1.78	0.023		0.33
(f) Using monthly wage (RE)
Union	0.054	***	2.98	0.053	**	2.39	0.061	*	1.87
Woman	−0.311	***	−23.67	−0.226	***	−10.11	−0.347	***	−21.87
Union×Woman	0.066	***	2.61	0.041		1.35	0.051		1.09
(g) Add parental education variables (RE)
Union	0.043	*	1.83	0.025		0.90	0.075	*	1.69
Woman	−0.278	***	−15.45	−0.141	***	−4.77	−0.333	***	−15.26
Union X Woman	0.074	**	2.07	0.032		0.75	0.038		0.57

Notes: Union×Woman, interaction term of union and female worker dummy variable.Control variables including demographic factors such as education, years of work experience and squared term, health status, ethnicity, urban hukou, married, CPC membership, work-related factors such as occupation, industrial sector, region, and year dummy variables were used in these models; the public sector dummy variable was added for all samples in Column 1. All standard errors were adjusted as cluster-robust standard error clustering on the individual level.

***p < 0.01; **p < 0.05; *p < 0.1.

Table 4. Gender disparities in the probability of obtaining union membership.

	(1)			(2)			(3)
Total	Coef.		z	Coef.		z	Coef.	z
Panel A: Nation
Woman	−0.417	***	−7.01	−0.157	***	−2.71	−0.066	−1.07
Demographic factors	No			Yes			Yes
Work-related factors	No			No			Yes
Regional fixed effect	Yes			Yes			Yes
Time year fixed effect	Yes			Yes			Yes
No. of obs.	29,356			29,356			29,356
No. of group	12,092			12,092			12,092
Log likelihood	−10180.653			−8854.412			−7150.578
LR test of rho = 0	7064.63			4554.3			2889.63
Prob ≥ chibar2	0.000			0.000			0.000
Panel B: Public sector
Public sector
Woman	−0.225	**	−2.55	−0.184	**	−2.09	−0.064	−0.68
Demographic factors	No			Yes			Yes
Work-related factors	No			No			Yes
Regional fixed effect	Yes			Yes			Yes
Time year fixed effect	Yes			Yes			Yes
No. of obs.	8,512			8,512			8,512
No. of group	3,570			3,570			3,570
Log likelihood	−4464.838			−4017.148			−3673.311
LR test of rho = 0	1918.58			1526.58			1324.83
Prob ≥ chibar2	0.000			0.000			0.000
Panel C: Private sector
Private sector
Woman	−0.296	***	−4.21	−0.137	*	−1.89	−0.038	−0.43
Demographic factors	No			Yes			Yes
Work-related factors	No			No			Yes
Regional fixed effect	Yes			Yes			Yes
Time year fixed effect	Yes			Yes			Yes
No. of obs.	20,844			20,844			20,844
No. of group	9,488			9,488			9,488
Log likelihood	−4899.2333			−4379.407			−3372.185
LR test of rho = 0	2395.99			1892.97			1422.35
Prob ≥ chibar2	0.000			0.000			0.000

Notes: The RE probit regression model was used. Demographic factors include years of schooling, years of work experience and squared term, health status, ethnicity, urban hukou, married, CPC membership; work-related factors includes occupation and industrial sector; the public sector dummy variable was added for all samples in Panel A. All standard errors were adjusted as cluster-robust standard error clustering on the individual level.

***p < 0.01; ** p < 0.05; *p < 0.1.

Table 5. Decomposition results of gender wage gap based on the O-R method.

	(1) Nation		(2) Public Sector		(3) Private Sector
	(a) Endowment	(b) Return	(c) Endowment	(d) Return	(e) Endowment	(f) Return
Wage gap (value)	−0.018	0.222	−0.050	0.108	−0.0002	0.2858
Contribution rate (%)
Total	−8.6%	108.6%	−86.7%	186.7%	−0.1%	100.1%
Union	0.7%	−1.2%	1.3%	−18.5%	0.2%	0.1%
Other factors	−9.3%	109.8%	−88.0%	205.2%	−0.3%	100.0%
Education	−10.3%	−29.1%	−105.4%	−21.5%	−4.1%	−28.9%
Exp.	0.5%	25.7%	53.1%	−61.5%	−1.7%	36.2%
Health	1.0%	4.7%	1.2%	11.7%	1.2%	4.7%
Ethnicity	0.2%	−48.6%	0.9%	−509.9%	0.2%	4.7%
Urban	−1.7%	−31.8%	−4.0%	78.0%	−0.9%	−31.1%
Married	−0.2%	21.2%	−4.1%	88.5%	0.0%	17.8%
Party	3.6%	−4.5%	19.6%	−34.0%	0.9%	−1.0%
Occupation	5.3%	−63.8%	−6.4%	−269.8%	8.1%	−37.5%
Industry	7.0%	6.5%	−10.9%	15.5%	10.0%	2.3%
Public sector	0.3%	−23.4%
Region	−2.8%	14.9%	2.6%	13.4%	−2.9%	12.4%
Year	−12.2%	1.6%	−34.6%	41.3%	−11.0%	−4.4%
Constants	0.0%	236.4%	0.0%	853.5%	0.0%	124.7%

Notes: Oaxaca-Ransom (Oaxaca & Ransom, 1994) decomposition method was used. The results of wage functions from the RE model were used in the decomposition analysis.

Table 6. Decomposition results of gender wage gap based on the D-R method.

	(1) Nation		(2) Public Sector		(3) Private Sector
	Value	%	Value	%	Value	%
Total gender wage gap	0.204	100%	0.058	100%	0.286	100%
A: Endowment effect	−0.108	−53.1%	−0.108	−186.8%	−0.243	−85.1%
A1: Union	−0.017	−8.1%	−0.030	−52.1%	−0.005	−1.8%
A2: Non-union	−0.092	−45.0%	−0.078	−134.7%	−0.238	−83.3%
B: Return effect	0.303	148.8%	0.270	464.9%	0.526	183.9%
B1: Union	0.026	12.5%	0.033	56.5%	0.041	14.3%
B2: Non-union	0.278	136.3%	0.237	408.4%	0.485	169.6%
C: Unionism effect	0.009	4.4%	0.103	−178.1%	0.003	1.2%
C1: Endowment	0.008	3.8%	−0.005	−8.7%	0.003	1.2%
C2: Return	0.001	0.6%	−0.098	−169.4%	0.000	0.0%

Table A1. Definition and descriptive statistics of variables (total samples).

	Definitions	Mean	SD	Min	Max
Union	1=union member, 0=non-union member	0.159	0.365	0	1
Logarithm of hourly wage	Calculated based on wage and work hours	2.357	0.774	0.693	6.781
Education	Years of schooling	11.086	4.072	0	22
Experience	Years of experience=age-6-years of schooling	22.340	11.853	0	54
Gender	1=female workers, 0=male workers	0.406	0.491	0	1
Ethnicity	1=Han majority, 0=ethnic minorities	0.959	0.198	0	1
Health	1=health status is very good or good, 0=otherwise	0.427	0.495	0	1
Urban	1=urban hukou, 0=rural hukou	0.769	0.421	0	1
Married	1=having a spouse, 0=otherwise	0.778	0.416	0	1
CPC membership	1= CPC member, 0=non-CPC member	0.166	0.372	0	1
Occupation
Manager	1=manager, 0=otherwise	0.077	0.267	0	1
Technician	1=technician, 0=otherwise	0.171	0.376	0	1
Clerk	1=clerk, 0=otherwise	0.143	0.350	0	1
Operator	1=operator, 0=otherwise	0.346	0.476	0	1
Other	1=other occupation, 0=otherwise	0.263	0.440	0	1
Industry sector		1.000
Manufacture	1=manufactural industry sector, 0=otherwise	0.343	0.475	0	1
Traffic and information	1=traffic and information industry sector, 0=otherwise	0.080	0.271	0	1
Retail trade	1=retail industry sector, 0=otherwise	0.139	0.346	0	1
Service	1=service industry sector, 0=otherwise	0.250	0.433	0	1
Other	1=other industry sector, 0=otherwise	0.188	0.390	0	1
Public sector	1=working in the public sector, 0=working in the private sector	0.397	0.489	0	1
Region
East	1=eastern regions, 0=otherwise	0.550	0.498	0	1
Central	1=central regions, 0=otherwise	0.305	0.460	0	1
West	1=western regions, 0=otherwise	0.146	0.353	0	1
Year
2010	1=2010 survey year, 0=otherwise	0.161	0.367	0	1
2012	1=2012 survey year, 0=otherwise	0.169	0.375	0	1
2014	1=2014 survey year, 0=otherwise	0.178	0.382	0	1
2016	1=2016 survey year, 0=otherwise	0.176	0.381	0	1
2018	1=2018 survey year, 0=otherwise	0.179	0.383	0	1
2020	1=2020 survey year, 0=otherwise	0.137	0.344	0	1
Observations	14,619

Oaxaca, R. L., & Ransom, M. R. (1994). On discrimination and the decomposition of wage differentials. Journal of Econometrics, 61(1), 5–21. https://doi.org/10.1016/0304-4076(94)90074-4

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Data availability statement

The dataset used in this study was obtained from the China Family Panel Studies (CFPS) and is publicly available (http://opendata.pku.edu.cn/en). The dataset and materials constructed during this study are available from the corresponding author upon request.