Skip to content
Licensed Unlicensed Requires Authentication Published online by De Gruyter Oldenbourg February 10, 2023

The Role of Sex Segregation in the Gender Wage Gap Among University Graduates in Germany

  • Juliane Ransmayr and Doris Weichselbaumer EMAIL logo

Abstract

In this paper we examine the gender wage gap among university graduates in Germany from 1997 to 2013 based on the DZHW (the German Centre for Higher Education Research and Science Studies) Absolventenpanel. We focus in particular on the effect of female presence in a subject or occupation on wage inequality. Earlier research has shown not only that female-dominated university subjects or occupations pay less, but also that men face a higher wage penalty than women when they graduated in a female-dominated subject and experience a lower penalty for working in a female-dominated occupation. For the five waves considered, we confirm the very strong negative association between female presence in a subject or occupation and wages. However, no consistent pattern emerges with regard to whether men’s or women’s wages suffer larger penalties. There is also no time trend observable with regard to the wage penalty that is associated with female-dominated fields. We further show that significant gender wage gaps exist within fields of studies, especially in male-dominated fields like engineering and natural science.

JEL Classification: J16; J3; J7

Corresponding author: Doris Weichselbaumer, Johannes Kepler University Linz, Linz, Austria, E-mail:
Article note: “This article is part of the special issue “Gender Economics” published in the Journal of Economics and Statistics. Access to further articles of this special issue can be obtained at www.degruyter.com/journals/jbnst.”

Acknowledgements

We thank Johannes Bacher, Silvia Ulrich, Miriam Beblo, Luise Görges and two anonymous referees for their helpful comments and suggestions, and Julien Deimling for his excellent research assistance.

Appendix

Table A1:

Field of studies, subjects and female shares within subjects for the year 2013.

Field of study Subject Female share 2013
Education Education 0.78 Female
Special education 0.82 Female
Humanities Language and culture 0.74 Female
Protestant theology 0.60 Integrated
Catholic theology 0.55 Integrated
Philosophy 0.44 Integrated
History 0.46 Integrated
Library science 0.75 Female
Linguistics and literature 0.75 Female
Classical philology 0.59 Integrated
German studies 0.77 Female
English studies 0.72 Female
Romance studies 0.81 Female
Slavic studies 0.76 Female
Non-European languages 0.63 Integrated
Cultural studies 0.75 Female
Health Psychology 0.75 Female
Sport science 0.39 Integrated
Health sciences 0.73 Female
Medicine 0.61 Integrated
Dentistry 0.63 Integrated
Veterinary medicine 0.84 Female
Agriculture Landscape planning 0.56 Integrated
Agrarian food science 0.48 Integrated
Forestry 0.33 Integrated
Food and household science 0.83 Female
Engineering Industrial engineering 0.26 Male
Engineering 0.20 Male
Mining 0.18 Male
Mechanical engineering 0.18 Male
Electrical engineering 0.11 Male
Traffic engineering 0.11 Male
Architecture 0.58 Integrated
Regional planning 0.50 Integrated
Construction 0.28 Male
Land surveying 0.31 Integrated
Industrial engineering 0.21 Male
Arts Arts 0.81 Female
Visual arts 0.54 Integrated
Design 0.61 Integrated
Performing arts 0.63 Integrated
Music 0.53 Integrated
Natural Sciences Natural sciences interdisciplinary 0.55 Integrated
Mathematics 0.47 Integrated
Computer science 0.19 Male
Physics, astronomy 0.25 Male
Chemistry 0.43 Integrated
Pharmacy 0.70 Integrated
Biology 0.62 Integrated
Geoscience 0.42 Integrated
Geography 0.48 Integrated
Law Law 0.54 Integrated
Social Sciences Law, business and social sciences, general 0.63 Integrated
Political sciences 0.42 Integrated
Social sciences 0.59 Integrated
Social work 0.77 Female
Economics & BA Public administration 0.51 Integrated
Economics & business administration 0.49 Integrated
  1. The data come from the German Federal Statistical Office.

Table A2:

Share of men and women in female-dominated, male-dominated and integrated subjects and occupations for the years 1997–2013.

Subjects Occupations
Female-dominated Integrated Male-dominated Female-dominated Integrated Male-dominated
1997 Male 0.06 0.43 0.51 0.07 0.37 0.56
Female 0.30 0.60 0.09 0.32 0.47 0.21
All 0.16 0.50 0.33 0.17 0.41 0.42
2001 Male 0.09 0.43 0.47 0.09 0.40 0.51
Female 0.33 0.48 0.20 0.27 0.42 0.31
All 0.23 0.46 0.31 0.20 0.41 0.39
2005 Male 0.08 0.45 0.48 0.07 0.39 0.54
Female 0.36 0.53 0.10 0.30 0.48 0.21
All 0.24 0.50 0.26 0.20 0.44 0.36
2009 Male 0.10 0.47 0.43 0.12 0.49 0.38
Female 0.44 0.50 0.06 0.39 0.52 0.09
All 0.31 0.49 0.20 0.29 0.51 0.20
2013 Male 0.05 0.43 0.52 0.14 0.45 0.41
Female 0.28 0.61 0.11 0.43 0.47 0.10
All 0.18 0.53 0.28 0.31 0.46 0.23
  1. Female shares are based on data from the German Federal Statistical Office (subjects) and from the Federal Employment Agency (occupations). The shares of men and women within female-dominated, male-dominated and integrated subjects and occupations are calculated based on the DZHW graduate panel.

Table A3:

OLS estimates for gross hourly wages, 1997–2013, based on reduced sample.

(1) (2) (3) (4) (5) (6)
1997a Female −0.264*** −0.124*** −0.103*** −0.0928*** −0.0790*** −0.0621***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Female subject −0.141*** −0.0714* −0.0727**
(0.001) (0.087) (0.050)
Integrated subject −0.196*** −0.163*** −0.107***
(0.000) (0.000) (0.000)
Female occupation −0.249*** −0.242*** −0.120***
(0.000) (0.000) (0.000)
Integrated occupation −0.110*** −0.0966*** −0.0388**
(0.000) (0.000) (0.025)
n 3749 3749 3749 3749 3749 3749

2001 Female −0.203*** −0.151*** −0.121*** −0.131*** −0.111*** −0.0491***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.004)
Female subject −0.219*** −0.118** −0.104***
(0.000) (0.018) (0.007)
Integrated subject −0.283*** −0.257*** −0.161***
(0.000) (0.000) (0.000)
Female occupation −0.278*** −0.284*** −0.106***
(0.000) (0.000) (0.001)
Integrated occupation −0.0909*** −0.0660** −0.0559**
(0.001) (0.019) (0.026)
n 1856 1856 1856 1856 1856 1856

Controls for:
Field of study Yes Yes Yes Yes Yes
Full controls Yes

2005 Female −0.287*** −0.174*** −0.114*** −0.137*** −0.0941*** −0.0660***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Female subject −0.435*** −0.357*** −0.217***
(0.000) (0.000) (0.000)
Integrated subject −0.333*** −0.299*** −0.197***
(0.000) (0.000) (0.000)
Female occupation −0.289*** −0.227*** −0.0760***
(0.000) (0.000) (0.000)
Integrated occupation −0.111*** −0.0683*** −0.0506***
(0.000) (0.000) (0.000)
n 6053 6053 6053 6053 6053 6053

2009 Female −0.240*** −0.144*** −0.108*** −0.104*** −0.0882*** −0.0593***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Female subject −0.263*** −0.130*** −0.109***
(0.000) (0.000) (0.001)
Integrated subject −0.268*** −0.235*** −0.167***
(0.000) (0.000) (0.000)
Female occupation −0.336*** −0.308*** −0.116***
(0.000) (0.000) (0.000)
Integrated occupation −0.0483** −0.000388 0.00221
(0.012) (0.985) (0.910)
n 4635 4635 4635 4635 4635 4635

  Controls for:
Field of study Yes Yes Yes Yes Yes
Full controls Yes

2013 Female −0.216*** −0.116*** −0.0797*** −0.0791*** −0.0592*** −0.0489***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.001)
Female subject −0.392*** −0.227*** −0.196***
(0.000) (0.000) (0.000)
Integrated subject −0.279*** −0.223*** −0.174***
(0.000) (0.000) (0.000)
Female occupation −0.293*** −0.254*** −0.208***
(0.000) (0.000) (0.000)
Integrated occupation −0.0581*** −0.0335* −0.0453**
(0.002) (0.068) (0.013)
  n 3058 3058 3058 3058 3058 3058

Controls for:
Field of study Yes Yes Yes Yes Yes
Full controls Yes
  1. p-values in parenthesis (***p < 0.01, **p < 0.05, *p < 0.1). Full Controls include in addition to fields of study (education, health, agriculture, engineering, arts, natural science, law, social science, economics & business administration; ref: humanities): education specific characteristics (highest degree, current PhD student, age at graduation, final grade university), job specific characteristics (internship, management position, part-time job), firm specific characteristics (public sector, big firm, location company), work history (working prior to university, working during university, unemployment duration, work experience, time as homemaker) and family specific characteristics (married, children, parent with Abitur). All data come from the DZHW graduate panel except for the female shares within subjects (German Federal Statistical Office), female share within occupations (Federal Employment Agency), as well as and the data on the consumer price index (German Federal Statistical Office).

    aFull Controls include final university grade for all years but 1997 (not available).

Table A4:

OLS estimates for gross hourly wages, 1997–2013 pooled, based on reduced sample.

(1) (2) (3) (4) (5) (6) (7) (7) cont.
Main effects IA effectsa
Female −0.248*** −0.146*** −0.104*** −0.110*** −0.0829*** −0.0568*** −0.267***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Representation women in subject
Female-dominated subject −0.310*** −0.203*** −0.159*** −0.131*** −0.0544
(0.000) (0.000) (0.000) (0.000) (0.101)
Integrated subject −0.284*** −0.245*** −0.173*** −0.165*** −0.0212
(0.000) (0.000) (0.000) (0.000) (0.316)
Representation women in occupation
Female-dominated occupation −0.297*** −0.267*** −0.129*** −0.161*** 0.0293
(0.000) (0.000) (0.000) (0.000) (0.212)
Integrated occupation −0.0950*** −0.0636*** −0.0258*** 0.00369 −0.0499***
(0.000) (0.000) (0.001) (0.722) (0.001)
Fields of study
Education 0.111*** 0.116*** 0.173*** 0.155*** 0.0959*** 0.139*** −0.0523
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.172)
Health 0.453*** 0.450*** 0.403*** 0.416*** 0.227*** 0.236*** −0.0196
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.516)
Agriculture 0.242*** 0.223*** 0.150*** 0.174*** 0.0279 −0.000942 0.0425
(0.000) (0.000) (0.000) (0.000) (0.134) (0.975) (0.265)
Engineering 0.510*** 0.269*** 0.381*** 0.231*** 0.103*** 0.112*** −0.0513
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.113)
Arts 0.0874*** 0.0775*** 0.0483* 0.0673** −0.0410* −0.0408 −0.00603
(0.002) (0.006) (0.080) (0.017) (0.092) (0.403) (0.915)
Natural Science 0.365*** 0.256*** 0.302*** 0.253*** 0.120*** 0.107*** 0.0201
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.495)
Law −0.186*** −0.193*** −0.224*** −0.193*** −0.0904*** −0.0597* −0.0450
(0.000) (0.000) (0.000) (0.000) (0.000) (0.050) (0.245)
Social Science 0.273*** 0.273*** 0.322*** 0.324*** 0.166*** 0.127*** 0.0487
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.111)
Economics & Business Administration 0.503*** 0.497*** 0.436*** 0.475*** 0.250*** 0.255*** −0.0144
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.621)
Full controls Yes Yes Yes
Constant 2.847*** 2.432*** 2.698*** 2.570*** 2.739*** 2.589*** 2.722***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Number of observations 19,799 19,799 19,799 19,799 19,799 19,799 19,799
  1. p-values are presented in parenthesis (***p < 0.01, **p < 0.05, *p < 0.1). All columns include year dummies. Full Controls include in addition to fields of study (education, health, agriculture, engineering, arts, natural science, law, social science, economics & business administration; ref: humanities): education specific characteristics (highest degree, current PhD student, age at graduation, final grade university), job specific characteristics (internship, management position, part-time job), firm specific characteristics (public sector, big firm, location company), work history (working prior to university, working during university, unemployment duration, work experience, time as homemaker) and family specific characteristics (married, children, parent with Abitur). The two columns (7) present the results from the fully interacted model, with the first column reporting the main effects and the second column presenting the interaction effects. Note that both column (6) and column (7) show the same results as in Table 4, because they are produced based on the same reduced sample and are only replicated here for completeness. All data come from the DZHW graduate panel except for the female shares within subjects (German Federal Statistical Office), female share within occupations (Federal Employment Agency), as well as and the data on the consumer price index (German Federal Statistical Office).

    aIA Effects stands for Interaction Effects and captures the effects of the interaction terms [covariable*female].

References

Albrecht, J., Bronson, M.A., Thoursie, P.S., and Vroman, S. (2018). The career dynamics of high-skilled women and men: evidence from Sweden. Eur. Econ. Rev. 105: 83–102, https://doi.org/10.1016/j.euroecorev.2018.03.012.Search in Google Scholar

Allison, P.D. (2001). Missing data. Sage Publications, California.Search in Google Scholar

Bacher, J., Beham-Rabanser, M., and Forstner, M. (2022). Can work value orientations explain the gender wage gap in Austria? Int. J. Sociol. 52: 1–21, https://doi.org/10.1080/00207659.2022.2042114.Search in Google Scholar

Behr, A. and Theune, K. (2018). The gender pay gap at labour market entrance: evidence from Germany. Int. Lab. Rev. 157: 83–100, https://doi.org/10.1111/ilr.12037.Search in Google Scholar

Bergmann, B.R. (1971). The effect on white incomes of discrimination in employment. J. Polit. Econ. 79: 294–313, https://doi.org/10.1086/259744.Search in Google Scholar

Bergmann, B.R. (1974). Occupational segregation, wages and profits when employers discriminate by race or sex. E. Econ. J. 1: 103–110.Search in Google Scholar

Bertrand, M. (2018). Coase lecture – the glass ceiling. Economica 85: 205–231, https://doi.org/10.1111/ecca.12264.Search in Google Scholar

Bertrand, M., Goldin, C., and Katz, L. (2010). Dynamics of the gender gap for young professionals in the financial and corporate sectors. Am. Econ. J. Appl. Econ. 2: 228–255, https://doi.org/10.1257/app.2.3.228.Search in Google Scholar

Blau, F.D. and Kahn, L.M. (2017). The gender wage gap: extent, trends, and explanations. J. Econ. Lit. 55: 789–865, https://doi.org/10.1257/jel.20160995.Search in Google Scholar

Blinder, A.S. (1973). Wage discrimination: reduced form and structural estimates. J. Hum. Resour. 8: 436–455, https://doi.org/10.2307/144855.Search in Google Scholar

Blundell, J. (2021). Wage responses to gender pay gap reporting requirements. CEPR Working Paper 1750.10.2139/ssrn.3584259Search in Google Scholar

Braakmann, N. (2010). Fields of training, plant characteristics and the gender wage gap in entry wages among skilled workers – evidence from German administrative data. Jahrb. Natl. Stat. 230: 27–41, https://doi.org/10.1515/jbnst-2010-0103.Search in Google Scholar

Braakmann, N. (2013). What determines wage inequality among young German university graduates? Jahrb. Natl. Stat. 233: 130–158, https://doi.org/10.1515/jbnst-2013-0202.Search in Google Scholar

Brandt, G., Briedis, K., Fabian, G., Kerst, C., Minks, K., Rehn, T., Schaeper, H., and Schramm, M. (2021). DZHW Graduate Panel 2001. Data Collection: 2002–2007. Version: 1.0.0. Data Package Access Way: SUF: Remote-Desktop. Hanover: FDZ-DZHW. Data Curation: Daniel, A, Available at: <https://doi.org/10.21249/DZHW:gra2001:1.0.0>.Search in Google Scholar

Brandt, G., Briedis, K., Fabian, G., Klüver, S., Rehn, T., and Trommer, M. (2018). DZHW Graduate Panel 2009. Data Collection: 2010–2015. Version: 1.0.1. Data Package Access Way: SUF: Remote-Desktop. Hanover: FDZ-DZHW. Data Curation: Baillet, F., Franken A. and Weber, A, Available at: <https://doi.org/10.21249/DZHW:gra2009:1.0.1>.Search in Google Scholar

Bredtmann, J. and Otten, S. (2014). Getting what (employers think) you’re worth. Evidence on the gender gap in entry wages among university graduates. Int. J. Manpow. 35: 291–305, https://doi.org/10.1108/ijm-01-2012-0013.Search in Google Scholar

Briedis, K., Euler, T., Grotheer, M., Isleib, S., Minks, K., Netz, N., Schaeper, H., Trennt, F., and Trommer, M. (2021a). DZHW Graduate Panel 2005. Data Collection: 2006–2016. Version: 2.0.1. Data Package Access Way: SUF: Remote-Desktop. Hanover: FDZ-DZHW. Data Curation: Baillet, F., Franken, A. and Weber, A, Available at: <https://doi.org/10.21249/DZHW:gra2005:2.0.1>.Search in Google Scholar

Briedis, K., Fabian, G., Kerst, C., Minks, K., and Schaeper, H. (2021b). DZHW Graduate Panel 1997. Data Collection: 1998–2003. Version: 1.0.0. Data Package Access Way: SUF: Remote-Desktop. Hanover: FDZ-DZHW. Data Curation: Daniel, A, Available at: <https://doi.org/10.21249/DZHW:gra1997:1.0.0>.Search in Google Scholar

Briedis, K., Fabian, G., Landers, G., Redeke, S., Rehn, T., Schulz, J., and Trennt, F. (2020). DZHW Graduate Panel 2013. Data Collection: 2014/2015. Version: 1.0.0. Data Package Access Way: SUF: Remote-Desktop. Hanover: FDZ-DZHW. Data Curation: Hoffstätter, U. and Vietgen, S, Available at: <https://doi.org/10.21249/DZHW:gra2013:1.0.0>.Search in Google Scholar

Busch, F. (2018). Occupational devaluation due to feminization? Causal mechanics, effect heterogeneity, and evidence from the United States, 1960 to 2010. Soc. Forces 96: 1351–1376, https://doi.org/10.1093/sf/sox077.Search in Google Scholar

Charles, M. and Bradley, K. (2009). Indulging our gendered selves? Sex segregation by field of study in 44 countries. Am. J. Sociol. 114: 924–976, https://doi.org/10.1086/595942.Search in Google Scholar

Chevalier, A. (2007). Education, occupation and career expectations: determinants of the gender pay gap for UK graduates. Oxf. Bull. Econ. Stat. 69: 819–842.10.1111/j.1468-0084.2007.00483.xSearch in Google Scholar

Clarke, H.M. (2020). Gender stereotypes and gender-typed work. In: Zimmermann, K.F. (Ed.), Handbook of Labor, Human Resources and Population. Springer, Champ, pp. 1–23.10.1007/978-3-319-57365-6_21-1Search in Google Scholar

Cohen, P.N. and Huffman, M.L. (2003). Individuals, jobs, and labor markets: the devaluation of women’s work. Am. Socio. Rev. 68: 443–463, https://doi.org/10.2307/1519732.Search in Google Scholar

Correll, S.J. and Ridgeway, C.L. (2003). Expectation states theory. In: Delamater, J. (Ed.), Handbook of Social Psychology. Kluwer Academic/Plenum Publishers, New York, pp. 29–51.10.1007/0-387-36921-X_2Search in Google Scholar

Duesenberry, J.S. (1949). Income, saving and the theory of consumer behavior. Harvard University Press, Cambridge, MA.Search in Google Scholar

Duncan, O.D. (1967). Discrimination against Negroes. Ann. Am. Acad. Polit. Soc. Sci. 371: 85–103, https://doi.org/10.1177/000271626737100106.Search in Google Scholar

Duncan, O.D. and Duncan, B. (1955). A methodological analysis of segregation indexes. Am. Socio. Rev. 20: 210–217, https://doi.org/10.2307/2088328.Search in Google Scholar

England, P. (1982). The failure of human capital theory to explain occupational sex segregation. J. Hum. Resour. 17: 358–370, https://doi.org/10.2307/145585.Search in Google Scholar

England, P. (1992). Comparable worth. Theories and evidence. Aldine de Gruyter, New York.Search in Google Scholar

England, P., Allison, P., and Wu, Y. (2007). Does bad pay cause occupations to feminize, does feminization reduce pay, and how can we tell with longitudinal data? Soc. Sci. Res. 36: 1237–1256, https://doi.org/10.1016/j.ssresearch.2006.08.003.Search in Google Scholar

England, P., Herbert, M.S., Kilbourne, B.S., Reid, L.L., and Megdal, L.M. (1994). The gendered valuation of occupations and skills: earnings in 1980 census occupations. Soc. Forces 73: 65–100, https://doi.org/10.2307/2579918.Search in Google Scholar

Eurostat. (2021). Gender pay gap: how much less do women earn than men?, <https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Gender_pay_gap_statistics> (Accessed 11 March 2022).Search in Google Scholar

Folke, O. and Rickne, J. (2022). Sexual harassment and gender inequality in the labor market. Q. J. Econ. 137: 2163–2212.10.1093/qje/qjac018Search in Google Scholar

Francesconi, M. and Parey, M. (2018). Early gender gaps among university graduates. Eur. Econ. Rev. 109: 63–82, https://doi.org/10.1016/j.euroecorev.2018.02.004.Search in Google Scholar

Gallego Granados, P. and Wrohlich, K. (2018). Gender Pay Gap besonders groß bei niedrigen und hohen Löhnen. DIW Wochenbericht 85: 173–179.Search in Google Scholar

Goldin, C. (2014). A grand gender convergence: its last chapter. Am. Econ. Rev. 104: 1091–1119, https://doi.org/10.1257/aer.104.4.1091.Search in Google Scholar

Görlich, D. and De Grip, A. (2008). Human capital depreciation during hometime. Oxf. Econ. Pap. 61: i98–i121.10.1093/oep/gpn044Search in Google Scholar

Graham, M.E., Hotchkiss, J.L., and Gerhart, B. (2000). Discrimination by parts: a fixed-effects analysis of starting pay differences across gender. E. Econ. J. 26: 9–27.Search in Google Scholar

Harris, J. (2022). Do wages fall when women enter an occupation? Lab. Econ. 74: 102102, https://doi.org/10.1016/j.labeco.2021.102102.Search in Google Scholar

Hausmann, A.C., Kleinert, C., and Leuze, K. (2015). Entwertung von Frauenberufen oder Entwertung von Frauen im Beruf? Kölner Z. Soziol. Sozialpsychol. 67: 217–242, https://doi.org/10.1007/s11577-015-0304-y.Search in Google Scholar

Heilman, M.E. and Caleo, S. (2018). Combatting gender discrimination: a lack of fit framework. Group Process. Intergr. Relat. 21: 725–744, https://doi.org/10.1177/1368430218761587.Search in Google Scholar

Hultin, M. (2003). Some take the glass escalator, some hit the glass ceiling? Career consequences of occupational sex segregation. Work Occup. 30: 30–61, https://doi.org/10.1177/0730888402239326.Search in Google Scholar

Jann, B. (2008). The Blinder–Oaxaca decomposition for linear regression models. Stata J. 8: 453–479, https://doi.org/10.1177/1536867x0800800401.Search in Google Scholar

Kitagawa, E.M. (1955). Components of a difference between two rates. J. Am. Stat. Assoc. 50: 1168–1194, https://doi.org/10.1080/01621459.1955.10501299.Search in Google Scholar

Kricheli-Katz, T. (2013). Choice-based discrimination: labor-force-type discrimination against gay men, the obese, and mothers. J. Empir. Leg. Stud. 10: 670–695, https://doi.org/10.1111/jels.12023.Search in Google Scholar

Leuze, K. and Strauß, S. (2009). Lohnungleichheiten zwischen Akademikerinnen und Akademikern: Der Einfluss von fachlicher Spezialisierung, frauendominierten Fächern und beruflicher Segregation. Z. Soziol. 38: 262–281, https://doi.org/10.1515/zfsoz-2009-0401.Search in Google Scholar

Leuze, K. and Strauß, S. (2014). Female-typical subjects and their effect on wage inequalities among higher education graduates in Germany. Eur. Soc. 16: 275–298, https://doi.org/10.1080/14616696.2012.748929.Search in Google Scholar

Leuze, K. and Strauß, S. (2016). Why do occupations dominated by women pay less? How ‘female-typical’ work tasks and working-time arrangements affect the gender wage gap among higher education graduates. Work. Employ. Soc. 30: 802–820, https://doi.org/10.1177/0950017015624402.Search in Google Scholar

Levanon, A., England, P., and Allison, P. (2009). Occupational feminization and pay: assessing causal dynamics using 1950–2000 U.S. census data. Soc. Forces 88: 865–892, https://doi.org/10.1353/sof.0.0264.Search in Google Scholar

Li, H.H.C. (2013). The effects of human capital depreciation on occupational gender segregation, <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.370.4963&rep=rep1&type=pdf> (Accessed 14 March 2022).Search in Google Scholar

Machin, S. and Puhani, P.A. (2003). Subject of degree and the gender wage differential: evidence from the UK and Germany. Econ. Lett. 79: 393–400, https://doi.org/10.1016/s0165-1765(03)00027-2.Search in Google Scholar

McDonald, J.A. and Thornton, R.J. (2007). Do new male and female college graduates receive unequal pay? J. Hum. Resour. 42: 32–48, https://doi.org/10.3368/jhr.xlii.1.32.Search in Google Scholar

Mincer, J. and Polachek, S. (1974). Family investments in human capital: earnings of women. J. Polit. Econ. 82: S76–S108, https://doi.org/10.1086/260293.Search in Google Scholar

Murphy, E. and Oesch, D. (2016). The feminization of occupations and change in wages: a panel analysis of Britain, Germany, and Switzerland. Soc. Forces 94: 1221–1255, https://doi.org/10.1093/sf/sov099.Search in Google Scholar

Oaxaca, R. (1973). Male-female wage differentials in urban labor markets. Int. Econ. Rev. 14: 693–709, https://doi.org/10.2307/2525981.Search in Google Scholar

Ochsenfeld, F. (2014). Why do women’s fields of study pay less? A test of devaluation, human capital, and gender role theory. Eur. Socio Rev. 30: 536–548, https://doi.org/10.1093/esr/jcu060.Search in Google Scholar

Polachek, S. (1981). Occupational self-selection: a human capital approach to sex differences in occupational structure. Rev. Econ. Stat. 63: 60–69, https://doi.org/10.2307/1924218.Search in Google Scholar

Reimer, D. and Schröder, J. (2006). Tracing the gender wage gap: income differences between male and female university graduates in Germany. Z. Arbeitsmarktforsch. – J. Lab. Mark. Res. 39: 235–253.Search in Google Scholar

Reskin, B.F. and Roos, P. (1990). Job queues, gender queues: explaining women’s inroads into male occupations. Temple University Press, Philadelphia.Search in Google Scholar

Ridgeway, C.L. (2011). Framed by gender: how gender inequality persists in the modern world. Oxford University Press, New York.10.1093/acprof:oso/9780199755776.001.0001Search in Google Scholar

Rosen, S. (1986). The theory of equalizing differences. Handb. Labor Econ. 1: 641–692, https://doi.org/10.1016/s1573-4463(86)01015-5.Search in Google Scholar

Sánchez-Mangas, R. and Sánchez-Marcos, V. (2021). Wage growth across fields of study among young college graduates: is there a gender gap? CESifo Econ. Stud. 67: 251–274.10.1093/cesifo/ifaa021Search in Google Scholar

Snyder, K.A. and Green, A.I. (2008). Revisiting the glass escalator: the case of gender segregation in a female dominated occupation. Soc. Probl. 55: 271–299.10.1525/sp.2008.55.2.271Search in Google Scholar

Sparreboom, T. (2014). Gender equality, part-time work and segregation in Europe. Int. Lab. Rev. 153: 245–268, https://doi.org/10.1111/j.1564-913x.2014.00203.x.Search in Google Scholar

Statistisches Bundesamt (2021). Verbraucherpreisindex: Deutschland, Jahre. Available at https://www-genesis.destatis.de/genesis/online?operation=result&code=61111-0001&deep=true#abreadcrumb (Accessed 12 May 2021).Search in Google Scholar

Tam, T. (1997). Sex segregation and occupational gender inequality in the United States: devaluation or specialized training? Am. J. Sociol. 102: 1652–1692, https://doi.org/10.1086/231129.Search in Google Scholar

Weichselbaumer, D. (2004). Is it sex or personality? The impact of sex stereotypes on discrimination in applicant selection. E. Econ. J. 30: 159–186.10.2139/ssrn.251249Search in Google Scholar

Weichselbaumer, D. and Winter-Ebmer, R. (2005). A meta-analysis of the international gender wage gap. J. Econ. Surv. 19: 479–511, https://doi.org/10.1111/j.0950-0804.2005.00256.x.Search in Google Scholar

Wieschke, J. (2018). Frequency of employer changes and their financial return: gender differences amongst German university graduates. J. Lab. Mark. Res. 52: 1–13, https://doi.org/10.1186/s12651-017-0235-3.Search in Google Scholar

Williams, C.L. (1992). The glass escalator: hidden advantages for men in the “female” professions. Soc. Probl. 39: 253–267, https://doi.org/10.1525/sp.1992.39.3.03x0034h.Search in Google Scholar

Williams, C.L. (2013). The glass escalator, revisited: gender inequality in neoliberal times, SWS feminist lecturer. Gend. Soc. 27: 609–629, https://doi.org/10.1177/0891243213490232.Search in Google Scholar

Wiswall, M. and Zafar, B. (2018). Preference for the workplace, investment in human capital, and gender. Q. J. Econ. 133: 457–507, https://doi.org/10.1093/qje/qjx035.Search in Google Scholar

Yavorsky, J. (2019). Uneven patterns of inequality: an audit analysis of hiring-related practices by gendered and classed contexts. Soc. Forces 98: 461–492, https://doi.org/10.1093/sf/soy123.Search in Google Scholar

Zafar, B. (2013). College major choice and the gender gap. J. Hum. Resour. 48: 545–595, https://doi.org/10.1353/jhr.2013.0022.Search in Google Scholar

Received: 2022-03-15
Accepted: 2022-12-07
Published Online: 2023-02-10

© 2023 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 24.2.2024 from https://www.degruyter.com/document/doi/10.1515/jbnst-2022-0018/html?utm_source=landingpage&utm_medium=link&utm_campaign=cross_womens-history_acad_ww&utm_term=rl&utm_content=read_product_cluster
Scroll to top button