Correction

This article refers to:

Explaining the gender pay gap among doctoral graduates: analyses of the German labour market

Article title: Explaining the gender pay gap among doctoral graduates: analyses of the German labour market

Goldan, Lea. 2020. Explaining the gender pay gap among doctoral graduates: analyses of the German labour market “European Journal of Higher Education. doi.org/10.1080/21568235.2020.1835516

DOI: https://doi.org/10.1080/21568235.2020.1835516

When conducting the analyses, the author had used the most recent scientific use file (version 3.0.0) available at that time. However, after the online publication of the article, the author was informed by the Research Data Centre of the DZHW that there had been an error in a variable in version 3.0.0. The error concerned the variable that was supposed to clearly identify the respondents of the survey: the person identification variable. The error occurred only when combining the two separate data sets that make up the data: a spell data set with calendar information about different types of activities and an individual data set with all other variables. Variables from one data set can be merged with the other data set using the person identification variable. However, due to the error in the person identification variable, this merging did not work correctly in version 3.0.0 of the scientific use file. To correct this error, the Research Data Centre has published a corrected scientific use file (version 4.0.0).

Unfortunately, this error also affected the analyses in the above-mentioned article. The author had generated a variable, namely professional experience after graduation, via the calendar information in the spell data set and had merged it with the individual data set using the person identification variable. Due to the error in the person identification variable, the merging was incorrect. As a result, the variable had too many missing values and its distribution was biased. Therefore, the author erroneously did neither find gender differences in professional experience after graduation nor professional experience contributing to the explanation of the gender pay gap. After repeating the multiple imputation procedure and all analyses using version 4.0.0 of the scientific use file, the author found that the overall results and interpretations were unchanged. However, the specific figures reported in the tables and text throughout the results chapter were slightly changed (Table 2, Table 3, Figure 3). In addition, the results regarding the variable in question had changed largely. Unlike before, there were now statistically significantly gender differences in professional experience after graduation that contributed to explaining the gender pay gap among doctoral graduates.

The corrected Table 2, Table 3 and Figure 3 follow.

Figure 3. Interaction effect of gender and academic employment on the logarithmic gross monthly earnings in M10 (predicted values and 95 % confidence interval). Data source: DZHW PhD Panel 2014, 5^th wave, multiply imputed data; author’s calculations.

Table 2. Description of variables

		Proportional and mean values (standard deviations)
		Total	Female	Male	Significance
Independent variable	Gender: female (ref. male)	.46
Dependent variable	Gross monthly earnings (in €)	6442.58	5211.16	7505.31	***
		(8630.93)	(7258.00)	(9536.97)
	Gross monthly earnings (log.)	8.54	8.34	8.71	***
		(.59)	(0.57)	(0.55)
Mediators	Doctoral subject
	- Humanities, art, sports	.14	.15	.13	n.s.
	- Legal, economic, social sciences	.25	.27	.23	*
	- Mathematics, natural sciences	.38	.41	.35	*
	- Agriculture, forestry, nutrition sciences, veterinary medicine	.06	.10	.03	***
	- Engineering	.17	.07	.26	***
	Formal type of doctoral training
	- Employment at a university	.56	.54	.58	n.s.
	- Employment at a non-university research institution	.10	.10	.10	n.s.
	- Employment outside academia	.12	.12	.13	n.s.
	- Doctoral grant	.18	.20	.16	n.s.
	- Private financing	.04	.05	.03	*
	Number of publications (stand.)	.03	–.07	.12	***
		(1.08)	(.92)	(1.19)
	Number of talks (stand.)	.00	–.07	.07	**
		(1.07)	(.96)	(1.16)
	Teaching experience: yes (ref. no)	.62	.58	.65	**
	International experience: yes (ref. no)	.18	.17	.19	n.s.
	Final grade of doctorate
	- Summa cum laude	.25	.22	.27	*
	- Magna cum laude	.61	.64	.58	*
	- Cum laude or less	.14	.14	.15	n.s.
	Duration of doctoral training (in years, stand.)	.03	.07	.00	n.s.
		(1.06)	(1.05)	(1.07)
	Professional experience after graduation (in months)	55.43	53.58	57.03	***
		(9.62)	(10.31)	(8.67)
	Industry
	- Manufacturing	.14	.08	.20	***
	- Health	.12	.19	.05	***
	- Social, cultural services	.26	.29	.24	*
	- Commercial, business-related services	.30	.29	.31	n.s.
	- IT, natural scientific services	.18	.15	.21	**
	Management position: yes (ref. no)	.40	.34	.45	***
	Working hours (per week)	36.40	34.02	38.45	***
		(7.12)	(8.78)	(4.35)
Moderator	Academic employment: yes (ref. no)	.37	.40	.35	*
Controls	Age (in years)	37.71	37.36	38.01	**
		(4.68)	(4.67)	(4.66)
	Parenthood: yes (ref. no)	.57	.54	.60	*
	Marriage: yes (ref. no)	.61	.54	.67	**
	Company size: big (ref. small/medium)	.65	.61	.68	**
	Region of employment: West Germany (ref. East)	.83	.81	.84	n.s.

Significance: * p < .05, ** p < .01, *** p < .001; data source: DZHW PhD Panel 2014, 1^st and 5^th waves, multiply imputed data, the results reported for m = 1, N = 1,768, author’s calculations

Table 3. OLS regression on the logarithmic gross monthly earnings

	M1	M2	M3	M4	M5	M6	M7	M8	M9	M10
Gender: female (ref. male)	–.363***	–.355***	–.311***	–.308***	–.302***	–.273***	–.246***	–.230***	–.112***	–.171***
	(.027)	(.026)	(.027)	(.027)	(.027)	(.027)	(.027)	(.026)	(.026)	(.032)
Doctoral subject (ref. humanities, art, sports)
- Legal, economic, social sciences			.293***	.281***	.293***	.266***	.214***	.199***	.147***	.144***
			(.043)	(.043)	(.044)	(.043)	(.044)	(.043)	(.041)	(.041)
- Mathematics, natural sciences			.263***	.263***	.285***	.271***	.207***	.210***	.161***	.161***
			(.041)	(.042)	(.043)	(.043)	(.047)	(.046)	(.043)	(.043)
- Agriculture, forestry, nutrition sciences, veterinary medicine			.112	.097	.116	.078	.058	.060	.023	.026
			(.063)	(.063)	(.064)	(.063)	(.069)	(.068)	(.064)	(.064)
- Engineering			.418***	.413***	.436***	.389***	.289***	.275***	.216***	.204***
			(.047)	(.049)	(.049)	(.049)	(.053)	(.052)	(.049)	(.049)
Formal type of doctoral training (ref. employment at a university)
- Employment at a non-university research institution				–.025	–.011	–.018	–.023	–.023	–.017	–.007
				(.043)	(.045)	(.045)	(.044)	(.043)	(.040)	(.040)
- Employment outside academia				.100*	.148**	.119*	.101*	.072	.109*	.092*
				(.044)	(.048)	(.047)	(.047)	(.046)	(.043)	(.043)
- Doctoral grant				–.034	–.028	–.021	–.020	–.024	–.013	–.009
				(.035)	(.036)	(.035)	(.035)	(.034)	(.032)	(.032)
- Private financing				–.044	–.005	–.011	.004	–.014	.017	–.001
				(.069)	(.071)	(.070)	(.069)	(.068)	(.063)	(.063)
Number of publications (stand.)					.016	.014	.013	.006	.005	.008
					(.014)	(.014)	(.014)	(.014)	(.013)	(.013)
Number of talks (stand.)					.002	–.004	.003	.006	–.001	.002
					(.015)	(.015)	(.014)	(.014)	(.013)	(.013)
Teaching experience: yes (ref. no)					.048	.033	.035	.029	.054*	.052*
					(.030)	(.029)	(.029)	(.028)	(.027)	(.026)
International experience: yes (ref. no)					.059	.068*	.070*	.068*	.030	.031
					(.035)	(.034)	(.034)	(.033)	(.031)	(.031)
Final grade of doctorate (ref. summa cum laude)
- Magna cum laude					–.000	.003	–.015	–.010	–.002	–.012
					(.032)	(.031)	(.031)	(.030)	(.028)	(.028)
- Cum laude or less					–.022	–.010	–.035	–.032	–.015	–.030
					(.046)	(.045)	(.044)	(.044)	(.041)	(.041)
Duration of doctoral training (in years, stand.)					–.002	.001	.001	.001	.002	–.001
					(.013)	(.012)	(.012)	(.012)	(.011)	(.011)
Professional experience after graduation (in months)						.011***	.010***	.010***	.007***	.008***
						(.001)	(.001)	(.001)	(.001)	(.001)
Industry (ref. social, cultural services)
- Manufacturing							.252***	.267***	.250***	.203***
							(.048)	(.048)	(.044)	(.047)
- Health							.035	.051	.082	.056
							(.052)	(.051)	(.049)	(.050)
- Commercial, business-related services							.245***	.234***	.205***	.156***
							(.036)	(.035)	(.033)	(.037)
- IT, natural scientific services							.130**	.143**	.138***	.106*
							(.045)	(.045)	(.042)	(.042)
Management position: yes (ref. no)								.174***	.105***	.103***
								(.026)	(.024)	(.024)
Working hours									.031***	.031***
									(.002)	(.002)
Academic employment: yes (ref. no)										–.164***
										(.037)
Female*academic employment										.155**
										(.048)
Age (in years)		–.005	–.002	–.004	–.004	–.006	–.003	–.003	–.001	–.001
		(.003)	(.003)	(.003)	(.003)	(.003)	(.003)	(.003)	(.003)	(.003)
Parenthood: yes (ref. no)		–.128***	–.137***	–.137***	–.137***	–.119***	–.118***	–.125***	–.019	–.018
		(.031)	(.030)	(.030)	(.030)	(.030)	(.029)	(.029)	(.027)	(.027)
Marriage: yes (ref. no)		.024	.020	.019	.015	.017	.012	.014	.020	.015
		(.031)	(.031)	(.031)	(.031)	(.030)	(.030)	(.029)	(.027)	(.027)
Company size: big (ref. small/medium)		.198***	.170***	.176***	.171***	.162***	.159***	.169***	.139***	.142***
		(.028)	(.028)	(.028)	(.028)	(.028)	(.027)	(.027)	(.025)	(.025)
Region of employment: West Germany (ref. East)		.115**	.105**	.098**	.099**	.088*	.083*	.077*	.094**	.083**
		(.037)	(.036)	(.036)	(.036)	(.035)	(.035)	(.034)	(.031)	(.031)
Constant	8.707***	8.708***	8.366***	8.471***	8.407***	7.880***	7.731***	7.678***	6.559***	6.642***
	(.018)	(.116)	(.127)	(.133)	(.141)	(.153)	(.153)	(.151)	(.160)	(.160)
Adjusted R^2	.095	.139	.178	.181	.182	.210	.237	.257	.377	.380
N	1,768	1,768	1,768	1,768	1,768	1,768	1,768	1,768	1,768	1,768

Significance: * p < .05, ** p < .01, *** p < .001; standard errors in parentheses; data source: DZHW PhD Panel 2014, 1^st and 5^th waves, multiply imputed data, author’s calculations