| Literature DB >> 29315326 |
Catherine Verniers1,2, Jorge Vala2.
Abstract
The issue of gender equality in employment has given rise to numerous policies in advanced industrial countries, all aimed at tackling gender discrimination regarding recruitment, salary and promotion. Yet gender inequalities in the workplace persist. The purpose of this research is to document the psychosocial process involved in the persistence of gender discrimination against working women. Drawing on the literature on the justification of discrimination, we hypothesized that the myths according to which women's work threatens children and family life mediates the relationship between sexism and opposition to a mother's career. We tested this hypothesis using the Family and Changing Gender Roles module of the International Social Survey Programme. The dataset contained data collected in 1994 and 2012 from 51632 respondents from 18 countries. Structural equation modellings confirmed the hypothesised mediation. Overall, the findings shed light on how motherhood myths justify the gender structure in countries promoting gender equality.Entities:
Mesh:
Year: 2018 PMID: 29315326 PMCID: PMC5760038 DOI: 10.1371/journal.pone.0190657
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Means, standard deviations and correlation matrix of the indicators.
| Correlation matrix | ||||||
|---|---|---|---|---|---|---|
| Indicators | (1) | (2) | (3) | (4) | ||
| Sexism | ||||||
| (1) | 2.75 | 1.30 | ||||
| Motherhood myths | ||||||
| (2) | 3.10 | 1.22 | .428 | |||
| (3) | 3.05 | 1.25 | .443 | .633 | ||
| Opposition | ||||||
| (4) | 2.31 | 0.68 | .337 | .377 | .345 | |
| (5) | 1.81 | 0.66 | .384 | .332 | .365 | .542 |
All coefficients are significant at p < .001.
Summary of hierarchical regression analysis for variables predicting opposition to women’s career.
| Predictor | ||||
|---|---|---|---|---|
| Block 1 | .098 | |||
| Gender | -.033 | .006 | -.056 | |
| Age | .058 | .006 | .095 | |
| Partnership | .002 | .006 | .003 | |
| Education | -.135 | .007 | -.193 | |
| Social status | -.057 | .007 | -.085 | |
| Religiosity | .076 | .006 | .122 | |
| Political orientation | .04 | .006 | .068 | |
| Block 2 | .280 | |||
| Gender | -.003 | .005 | -.006 | |
| Age | .016 | .005 | .025 | |
| Partnership | -.001 | .006 | -.001 | |
| Education | -.072 | .007 | -.103 | |
| Social status | -.020 | .006 | -.030 | |
| Religiosity | .022 | .006 | .035 | |
| Political orientation | .020 | .005 | .035 | |
| Sexism | .151 | .006 | .244 | |
| Motherhood myth—Child | .100 | .007 | .170 | |
| Motherhood myth—Family | .090 | .007 | .151 |
Gender is coded -1 for men and 1 for women. Partnership is coded -1 for no partner and 1 for partner. All coefficients and Rs2 are significant at the level p < .001, except the coefficients with superscripts:
a p > .10,
b p < .005.
Goodness-of-fit indices for the hypothesized mediational model and alternative models by country.
| Country | χ 2 | CFI | RMSEA | SRMR | AIC | Δ χ 2 |
|---|---|---|---|---|---|---|
| Austria | ||||||
| Hypothesized model (df = 4) | 91.2 | .959 | .10 [.09, .12] | .05 | 23013 | |
| Alternative model 1 (df = 4) | 350.3 | .838 | .21 [.19, .23] | .13 | 23273 | |
| Alternative model 2 (df = 5) | 658.16 | .694 | .26 [.24, .28] | .20 | 23578 | 566.9 |
| Australia | ||||||
| Hypothesized model (df = 4) | 110.58 | .978 | .09 [.08, .11] | .05 | 30161 | |
| Alternative model 1 (df = 4) | 634.5 | .868 | .24 [.22, .25] | .14 | 30685 | |
| Alternative model 2 (df = 5) | 1024.1 | .787 | .27 [.25, .28] | .22 | 31072 | 913.4 |
| Bulgaria | ||||||
| Hypothesized model (df = 4) | 18.82 | .989 | .04 [.02, .06] | .01 | 24157 | |
| Alternative model 1 (df = 4) | 199.7 | .851 | .16 [.14, .18] | .10 | 24337 | |
| Alternative model 2 (df = 5) | 290.97 | .782 | .18 [.16, .19] | .13 | 24427 | 272.1 |
| Canada | ||||||
| Hypothesized model (df = 4) | 56.82 | .985 | .08 [.06, .10] | .04 | 21367 | |
| Alternative model 1 (df = 4) | 312.84 | .911 | .20 [.18, .22] | .11 | 21623 | |
| Alternative model 2 (df = 5) | 736.87 | .789 | .28 [.26, .30] | .23 | 22045 | 680.0 |
| Czech Republic | ||||||
| Hypothesized model (df = 4) | 17.14 | .995 | .03 [.02, .05] | .01 | 32741 | |
| Alternative model 1 (df = 4) | 124.8 | .958 | .10 [.09, .12] | .06 | 32849 | |
| Alternative model 2 (df = 5) | 370.17 | .874 | .17 [.15, .18] | .13 | 33092 | 353.04 |
| Germany | ||||||
| Hypothesized model (df = 4) | 238.7 | .971 | .11 [.10, .13] | .06 | 51502 | |
| Alternative model 1 (df = 4) | 1123.6 | .861 | .25 [.24, .26] | .15 | 52387 | |
| Alternative model 2 (df = 5) | 1771.5 | .781 | .28 [.27, .29] | .23 | 53033 | 1532.8 |
| Great Britain | ||||||
| Hypothesized model (df = 4) | 77.57 | .971 | .10 [.08, .13] | .04 | 16910 | |
| Alternative model 1 (df = 4) | 304.26 | .881 | .22 [.20, .24] | .12 | 17137 | |
| Alternative model 2 (df = 5) | 616.97 | .757 | .28 [.26, .30] | .22 | 17447 | 539.4 |
| Ireland | ||||||
| Hypothesized model (df = 4) | 61.39 | .982 | .09 [.07, .11] | .05 | 20300 | |
| Alternative model 1 (df = 4) | 315.71 | .900 | .21 [.19, .23] | .13 | 20554 | |
| Alternative model 2 (df = 5) | 712.91 | .772 | .28 [.27, .30] | .23 | 20949 | 651.52 |
| Israel | ||||||
| Hypothesized model (df = 4) | 21.06 | .993 | .04 [.02, .06] | .01 | 26053 | |
| Alternative model 1 (df = 4) | 237.67 | .901 | .16 [.14, .18] | .10 | 26269 | |
| Alternative model 2 (df = 5) | 505.04 | .788 | .21 [.19, .22] | .16 | 26592 | 483.98 |
| Japan | ||||||
| Hypothesized model (df = 4) | 29.12 | .983 | .05 [.03, .07] | .02 | 26340 | |
| Alternative model 1 (df = 4) | 115.31 | .925 | .12 [.10, .14] | .07 | 26426 | |
| Alternative model 2 (df = 5) | 214.43 | .859 | .14 [.13, .16] | .10 | 26523 | 185.31 |
| Norway | ||||||
| Hypothesized model (df = 4) | 77.97 | .989 | .07 [.06, .09] | .03 | 32441 | |
| Alternative model 1 (df = 4) | 718.14 | .892 | .24 [.23, .26] | .13 | 33081 | |
| Alternative model 2 (df = 5) | 1558.4 | .764 | .32 [.31, .33] | .27 | 33920 | 1480.5 |
| Philippines | ||||||
| Hypothesized model (df = 4) | 19.01 | .984 | .04 [.02, .06] | .01 | 29706 | |
| Alternative model 1 (df = 4) | 40.49 | .961 | .06 [.04, .08] | .03 | 29728 | |
| Alternative model 2 (df = 5) | 180.81 | .814 | .12 [.10, .14] | .08 | 29866 | 161.8 |
| Poland | ||||||
| Hypothesized model (df = 4) | 117.24 | .964 | .11 [.09, .13] | .06 | 28495 | |
| Alternative model 1 (df = 4) | 409.59 | .870 | .21 [.19, .23] | .12 | 28788 | |
| Alternative model 2 (df = 5) | 993.28 | .683 | .29 [.28, .31] | .22 | 29369 | 876.04 |
| Russia | ||||||
| Hypothesized model (df = 4) | 12.71 | .997 | .02 [.01, .04] | .01 | 35328 | |
| Alternative model 1 (df = 4) | 199.42 | .928 | .12 [.11, .14] | .07 | 35514 | |
| Alternative model 2 (df = 5) | 387.38 | .859 | .16 [.14, .17] | .12 | 35701 | 374.67 |
| Slovenia | ||||||
| Hypothesized model (df = 4) | 7.25 | .999 | .02 [.00, .04] | .01 | 22547 | |
| Alternative model 1 (df = 4) | 281.77 | .914 | .19 [.17, .21] | .12 | 22821 | |
| Alternative model 2 (df = 5) | 595.08 | .817 | .25 [.23, .26] | .21 | 23133 | 587.83 |
| Spain | ||||||
| Hypothesized model (df = 4) | 51.3 | .991 | .05 [.04, .06] | .01 | 48463 | |
| Alternative model 1 (df = 4) | 382.6 | .930 | .14 [.13, .16] | .09 | 48794 | |
| Alternative model 2 (df = 5) | 1388.5 | .746 | .25 [.24, .26] | .19 | 49798 | 1337.2 |
| Sweden | ||||||
| Hypothesized model (df = 4) | 81.63 | .979 | .10 [.08, .12] | .04 | 20684 | |
| Alternative model 1 (df = 4) | 543.2 | .856 | .26 [.24, .28] | .14 | 21145 | |
| Alternative model 2 (df = 5) | 994.45 | .735 | .32 [.30, .33] | .25 | 21595 | 912.82 |
| USA | ||||||
| Hypothesized model (df = 4) | 2.76 | 1.00 | .00 [.00, .02] | .01 | 25311 | |
| Alternative model 1 (df = 4) | 408.53 | .872 | .21 [.20, .23] | .13 | 25717 | |
| Alternative model 2 (df = 5) | 683.44 | .785 | .25 [.23, .26] | .20 | 25990 | 680.68 |
Δ χ2 compares the second alternative model with the hypothesized mediational model. All Δ χ2 tests are significant at p < .001. The hypothesized mediational model and the first alternative model are not nested and therefore a Δ χ2 test cannot be computed.
Standardized maximum likelihood coefficients estimated for the hypothesized model by country.
| Country | Sexism effect on myths | Myths effect on opposition | Total effect | Indirect effect | Direct effect |
|---|---|---|---|---|---|
| Austria | .49 | .61 | .46 | .24 | .22 |
| Australia | .51 | .74 | .36 | .26 | .10 |
| Bulgaria | .42 | .42 | .33 | .18 | .15 |
| Canada | .54 | .72 | .44 | .29 | .15 |
| Germany | .55 | .76 | .35 | .30 | .05 |
| Great Britain | .53 | .64 | .44 | .28 | .16 |
| Ireland | .53 | .66 | .46 | .28 | .18 |
| Israel | .42 | .49 | .41 | .18 | .22 |
| Japan | .28 | .36 | .25 | .07 | .18 |
| Norway | .65 | .81 | .49 | .42 | .06 |
| Poland | .50 | .50 | .56 | .25 | .31 |
| Russia | .35 | .46 | .30 | .12 | .17 |
| Slovenia | .51 | .60 | .44 | .26 | .17 |
| Spain | .45 | .37 | .57 | .20 | .36 |
| Sweden | .66 | .78 | .44 | .43 | .01, |
| USA | .55 | .59 | .46 | .31 | .15 |
Significance of the indirect effects was estimated using bootstrap analyses with 1000 bootstrapping resamples.
* p < .02,
*** p < .001.
Fig 1Standardized maximum likelihood coefficients for the structural equation model testing the relationship between sexism and opposition to women’s career, mediated by the endorsement of motherhood myths.
The coefficient in parentheses represents parameter estimate for the total effect of prejudice on opposition to women’s career. *** p < .001.
Standardized maximum likelihood coefficients estimated for the total and indirect effects as a function of the survey wave.
| Country | Total effect | Direct effect | Indirect effect | Δ Indirect effect |
|---|---|---|---|---|
| Austria | ||||
| 2012 (986) | .66 | .44 | .21 | |
| 1994 (877) | .45 | .15 | .29 | -.07, |
| Australia | ||||
| 2012 (1225) | .50 | .14 | .36 | |
| 1994 (1518) | .56 | .16 | .40 | -.04, |
| Bulgaria | ||||
| 2012 (838) | .32 | .15 | .17 | |
| 1994 (914) | .24 | .13 | .11 | .06, |
| Canada | ||||
| 2012 (727) | .58 | .18 | .39 | |
| 1994 (1098) | .59 | .21 | .37 | .02, |
| Germany | ||||
| 2012 (1390) | .50 | .13 | .38 | |
| 1994 (2882) | .54 | .15 | .39 | -.01, |
| Great Britain | ||||
| 2012 (735) | .55 | .28 | .27 | |
| 1994 (806) | .53 | .13 | .40 | -.13, |
| Ireland | ||||
| 2012 (899) | .56 | .19 | .37 | |
| 1994 (794) | .55 | .25 | .29 | .08, |
| Israel | ||||
| 2012 (1043) | .52 | .32 | .20 | |
| 1994 (1159) | .41 | .17 | .24 | -.04, |
| Japan | ||||
| 2012 (826) | .35 | .22 | .13 | |
| 1994 (1098) | .24 | .16 | .08 | .05, |
| Norway | ||||
| 2012 (1190) | .58 | .15 | .43 | |
| 1994 (1784) | .64 | .15 | .49 | -.06, |
| Poland | ||||
| 2012 (970) | .69 | .19 | .34 | |
| 1994 (1278) | .45 | .15 | .30 | .03, |
| Russia | ||||
| 2012 (1303) | .30 | .15 | .15 | |
| 1994 (1694) | .35 | .22 | .13 | .02, |
| Slovenia | ||||
| 2012 (937) | .48 | .24 | .24 | |
| 1994 (931) | .41 | .17 | .24 | .00, |
| Spain | ||||
| 2012 (2189) | .58 | .35 | .23 | |
| 1994 (2067) | .53 | .36 | .17 | .06, |
| Sweden | ||||
| 2012 (862) | .56 | .09, | .46 | |
| 1994 (1062) | .53 | .10 | .43 | .03, |
| USA | ||||
| 2012 (915) | .52 | .21 | .30 | |
| 1994 (1202) | .49 | .13 | .36 | -.05, |
Significance of the indirect effects are estimated using bootstrap analyses with 1000 bootstrapping resamples.
* p < .05,
** p < .01,
*** p < .001.
Standardized maximum likelihood coefficients estimated for the total and indirect effects as a function of the respondents’ gender.
| Country | Total effect | Direct effect | Indirect effect | Δ Indirect effect |
|---|---|---|---|---|
| Austria | ||||
| Female (1049) | .59 | .32 | .27 | |
| Male (814) | .52 | .24 | .27 | .00, |
| Australia | ||||
| Female (1370) | .51 | .15 | .38 | |
| Male (1342) | .54 | .16 | .36 | .02, |
| Bulgaria | ||||
| Female (1066) | .31 | .13 | .14 | |
| Male (685) | .29 | .16 | .15 | -.01, |
| Canada | ||||
| Female (960) | .61 | .23 | .37 | |
| Male (854) | .55 | .16 | .38 | -.01, |
| Germany | ||||
| Female (2130) | .54 | .12 | .41 | |
| Male (2142) | .52 | .12 | .39 | .02, |
| Great Britain | ||||
| Female (837) | .55 | .19 | .35 | |
| Male (704) | .53 | .26 | .27 | .08, |
| Ireland | ||||
| Female (995) | .49 | .16 | .33 | |
| Male (693) | .61 | .31 | .30 | .03, |
| Israel | ||||
| Female (1214) | .37 | .14 | .22 | |
| Male (985) | .53 | .34 | .18 | .04, |
| Japan | ||||
| Female (1068) | .27 | .15 | .11 | |
| Male (856) | .30 | .20 | .10 | .01, |
| Norway | ||||
| Female (1569) | .63 | .13 | .50 | |
| Male (1405) | .62 | .13 | .49 | .00, |
| Poland | ||||
| Female (1225) | .60 | .37 | .23 | |
| Male (1023) | .54 | .39 | .14 | .08, |
| Russia | ||||
| Female (1967) | .36 | .20 | .16 | |
| Male (1030) | .32 | .15 | .17 | .01, |
| Slovenia | ||||
| Female (1017) | .52 | .20 | .31 | |
| Male (850) | .48 | .16 | .32 | -.01, |
| Spain | ||||
| Female (2242) | .53 | .38 | .14 | |
| Male (2012) | .51 | .36 | .14 | .00, |
| Sweden | ||||
| Female (1020) | .56 | .06, | .50 | |
| Male (883) | .56 | .16 | .40 | .10, |
| USA | ||||
| Female (1192) | .48 | .13 | .31 | |
| Male (925) | .48 | .17 | .35 | -.03, |
Significance of the indirect effects are estimated using bootstrap analyses with 1000 bootstrapping resamples.
** p < .005,
*** p < .001.