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The signalling value of education across genders

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Abstract

This study examines gender discrimination and the possibility that education is more important for signalling ability among women than men. As social networks tend to run along gender lines and managers in the labour market are predominantly male, it may be more difficult for women to signal their ability without college credentials. The Lang and Manove (Am Econ Rev 101(4):1467–1496, 2011) model of racial discrimination and educational sorting is applied to examine the gender gap in schooling attainment. The model is empirically estimated for whites, blacks and Hispanics separately, with the results among whites consistent with education being more valuable to women due to signalling. For 90% of the whites in the sample women choose a higher level of education, given their ability, than men. Women on average obtain 0.5–0.7 extra years of schooling compared to men with the same ability score.

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Notes

  1. A noteworthy exception is a study by Hersch and Kip Viscusi (1996) that finds that women are more likely to get promoted, since they on average start at lower level jobs.

  2. Lang and Manove (2011) thoroughly discuss the possibility that the educational gap between blacks and whites may stem from the difference in the quality of their schooling. Blacks are overrepresented in low-quality schools and underrepresented in high-quality schools. To acquire a certain skill level, blacks need to obtain more schooling than whites, i.e. they substitute quality with quantity. In contrast, however, the share of females does not appear to vary with school quality (see, for example, Autor et al. 2016; Allensworth et al. 2016), which is why this is not a plausible explanation for the educational gap between males and females.

  3. This follows from our previous discussion. To briefly reiterate, Montgomery (1991) shows that groups that are inadequately represented in higher-level positions may be at an information disadvantage. The psychology and management literature also reveals how male managers may be worse at evaluating female compared to male productivity (Hamner et al. 1974; Surmann 1997; Bauer and Baltes 2002). Furthermore social networks are likely to run along gender lines as well as racial lines, with referrals and personal contacts playing an important role in channelling information in the labour market (see Altonji and Blank 1999).

  4. Moreover, as discussed above, more egalitarian gender–role attitudes among individuals with higher levels of education may result in employers with similar views in the educated labour market (Cherlin and Walters 1981; Thornton et al. 1983; Thornton and Freedman 1979), while networks may play a bigger role in the market for low-skill jobs, implying that a lack of network is more damaging at lower and intermediate educational levels (see Hellerstein et al. 2008).

  5. Several studies point out that the results from estimating a selection model are very sensitive to the modelling assumptions, see, for example, Bar et al (2015) and Huber and Melly (2015).

  6. Neal (2004) assumes women receive substantial spousal support if their husbands’ wages place them above the 90th (or alternatively 75th percentile in the personal income distribution for 20–35-year-old males of the same race.

  7. Using the actual wage observations from the 1996 and 1998 waves yields very similar results.

  8. They argue that there is statistical discrimination because the ability distribution differs between whites and blacks—and if ability is unobserved, then expected ability is higher for whites. This contrasts the LM model, where the difference in educational attainment stems from the assumption that the ability of some workers (here female workers) is less observable, which is why employers put more weight on education for those workers.

  9. Searching for the maximum gender difference in education implies differentiating Eq. (4) first with respect to (w.r.t.) Female and then w.r.t. AFQT, which gives \(0.112 + 2\times (-0.236)\times AFQT\). Finding the maximum by setting this equal to zero gives AFQT = 0.24. Thus, from Eq. (4) differentiated w.r.t. to Female, the maximum gender gap in education becomes \(0.519+0.112\times 0.24-0.236\times 0.24^{2}=0.56\) years of schooling.

  10. There are 6584 observations of whites, 5920 of whom have AFQT scores in the interval −1.26–1.74. This results in \(5925/6584 = 90.0\%\).

  11. This is obtained from differentiating equation (5) first w.r.t. Female and then w.r.t. Educ, which gives, for example, for column 1: \(-0.149 + 2\times (0.496)\times {\hbox {Edcuation}}/100\) using coefficient estimates from Column 2 in Table  5a. Setting this equal to zero gives Education \(=\) 15.

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Correspondence to Herdis Steingrimsdottir.

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Nielsson, U., Steingrimsdottir, H. The signalling value of education across genders. Empir Econ 54, 1827–1854 (2018). https://doi.org/10.1007/s00181-017-1264-z

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