Why do girls' STEM aspirations differ between countries?
Why do girls' STEM aspirations differ between countries? How cultural norms and institutional constraints shape young women's occupational aspirations Marcel Helbig, Wissenschaftszentrum Berlin für Sozialforschung Kathrin Leuze, Leibniz Universität Hannover Womens‘ share in tertiary education 1970/80 Below 30 % 52 to 60 % 30 to 40 % 60 to 70 % 40 to 48 % 70 % and more 48 to 52 % Quelle: Unesco: Global Education Digest 2009 2 Seite 2 Womens‘ share in tertiary education 2005 Below 30 % 52 to 60 % 30 to 40 % 60 to 70 % 40 to 48 % 70 % and more 48 to 52 % Quelle: Unesco: Global Education Digest 2009 3 Seite 3 .3 .2 .1 0 Share of girls and boys with STEM preferences .4 STEM aspirations of 15-year-old girls and boys (PISA 2006) NL SE FR FI LU LV CH BE DK AT IS US SK NO DE UK IE EE PL IT CZ HU LT ES SI CA PT GR BG JP Girls' preferences Source: PISA 2006, authors‘ calculations Boys' preferences Seite 4 Research question How can we explain cross-national variations in girls‘ preferences for STEM occupations? STEM occupations: - Science: e.g. physicists, chemists, biologists - Technology: e.g. computing professionals, computer programmers - Engineering: e.g. electrical engineers, mechanical engineers, chemical engineers - Mathematics: mathematicians, statisticians Seite 5 5 Rational choice explanations for STEM aspirations (Jonsson 1999, Eccles 1994) • Probabilities of success / expectancy-value: • Higher competencies in and valuation of mathematics and science Higher probabilities of success for entering STEM occupations • H1: Countries with high female-to-male competence advantage in reading and mathematics • Better chances for girls in general to succeed in the education system • Better confidence of girls for successfully entering STEM occupations more STEM aspirations of girls Seite 6 Rational choice explanations for STEM aspirations (Jonsson 1999) • Benefits: • Different valuation of income and status (boys) vs. working with others and communicating (girls) gender-differentiated valuation of returns to STEM occupations • H2: Countries with a large service sector • good employment chances (benefits) for women in „female-typcical“ occupations • Better options for girls to achieve their work-related benefit expectations less STEM aspirations of girls Seite 7 Socialization explanations for STEM aspirations (Hannover 2008, Ruble et al. 2006, Eccles 1987) • Normative gender-role expectations: • gender-typical parental expectations in general • parents with higher socio-economic status more progressive gender role expectations, also regarding occupational aspirations • H3: Countries with progressive gender ideology: • less gender-typical role expectations of the wider social environment (teachers, peers, media etc.) regarding occupational preferences more STEM aspirations of girls Seite 8 Socialization explanations for STEM aspirations (Hannover 2008, Ruble et al. 2006, Eccles 1987) • Same-sex role models: • Girls orient themselves towards mothers and boys towards fathers as role models to learn from • Mothers working in STEM occupations ‘same-sex role models‘ to learn from • H4: Countries with less occupational sex segregation: • more same-sex role models in the wider social environment (teachers, acquaintances, media, etc.) working in gender-atypical occupations more STEM aspirations of girls Seite 9 Data and methods Programme for International Student Assessment 2006 Focus on 30 EU and OECD countries: AT, BE, BG, CA, CZ, DK, EE, FI, FR, DE, GR, HU, IC, IE, IT, JP, LT, LV, LU, NL, NO, PL, PT, SK, SI, ES, SE, CH, UK, US Dependent variable: STEM occupational aspiration of 15year-old pupils at the age of 30 Sample restriction: Aspiration of academic occupation (ISCO major groups 1 to 3) 61,394 girls, 49,835 boys Method: Multilevel random intercept logistic regression models with countries as second level (AMEs) all metric variables are mean centered and standardised Seite 10 10 Individuallevel control variables General: age, grade, migration background Rational choice: probability of success / expectancy: objective: mathematics and science competencies subjective: science self-concept value of science: personal value of science benefits: instrumental science motivation Socialization: gender norms: socioeconomic status of household (ESCS index of economic, social and cultural status) same-sex role model: STEM occupation of mother and father Seite 11 11 Countrylevel independent variables Rational choice: • Female-to-male overall competence advantage: Differences between mean reading and mathematics scores for girls and boys for the year 2006 (PISA 2006) • Size of the service sector: Service sector employment as share of total employment 2006 Socialisation: • Progressive gender ideology: Country mean of gender role attitudes towards women‘s and mothers‘ employment (ISSP 2002, WVS 2000) • Occupational sex segregation: Index of Dissimilarity (ILO 2000) Countrylevel control variable: Female to male tertiary graduation rate 2006 (Eurostat, OECD) Seite 12 12 Results of the multilevel random intercept logistic regression models – countrylevel variables Per cent women in occupational category H1: High female-to-male competence advantage H2: Large size of the service sector H3: Progressive gender ideology H4: More occupational sex segregation Female-to-male graduation rate Variance country level intercept only model % % explained individual vars. % explained full model Girls Boys Expected Observed Expected Observed + 0.155* ? -0.043 - -0.350*** ? -0.336*** + -0.014 - -0.027 - -0.382*** + -0.103 - 0.098 -0.034 11.35 5.39 -10.41 46.73 -13.96 43.23 Significant * p<0.05, ** p<0.01, *** p<0.001, std. coeff., average marginal effects Seite 13 Girls‘ competence advantage: interaction with sex -3.5 -3 Linear Prediction -2.5 -2 -1.5 -1 Predictive Margins with 95% CIs 2 NL 3 4 5 6 7 8 9 10 11 12 13 Girls' overall competence advantage Boys Girls 14 15 16 BG Seite 14 Size of service sector: interaction with sex -4 -3 Linear Prediction -2 -1 0 Predictive Margins with 95% CIs 50 PL 55 60 65 70 Size of service sector Boys Girls 75 80 LU Seite 15 Progressive gender norms: interaction with sex -3.5 -3 Linear Prediction -2.5 -2 -1.5 -1 Predictive Margins with 95% CIs .4 JP .45 .5 .55 .6 Progressive gender norms Boys Girls .65 .7 DK Seite 16 Index of dissimilarity: interaction with sex -5 Linear Prediction -4 -3 -2 -1 Predictive Margins with 95% CIs 40 US 45 50 55 60 Index of dissimilarity Boys Girls 65 70 CZ Seite 17 Summary of main findings Cross-national variations in STEM aspirations matter, but not as much as individuallevel differences more longitudinal analysis of STEM aspirations on the individual level On the country level, girls‘ STEM aspirations are higher the better girls perform in school relative to boys the smaller the size of the service sector the less progressive the prevailing gender norms the lower the general occupational sex segregation Question: What to do? Are gender-typical occupational aspirations problematic? Seite 18 THANK YOU FOR YOUR ATTENTION! Seite 19 Results of the multilevel random intercept logistic regression models – individuallevel variables STEM occupational aspiration Girls Boys Expected Observed Expected Observed Math competencies + 0.045 + 0.137*** Science competencies + 0.193*** + 0.107** Science self-concept + 0.145*** + 0.094*** Personal value of science + 0.157*** + 0.084*** Instrumental science motivation + 0.650*** + 0.265*** High parental SES + -0.111*** - -0.155*** Mother STEM occupation + 0.810*** 0 0.331** Father STEM occupation 0 0.549*** + 0.573*** % Variance explained 21.62 15.00 Significant * p<0.05, ** p<0.01, *** p<0.001, std. coeff., average marginal effects Seite 20 Index of Dissimilarity of 30 OECD and EU countries 80 70 60 50 40 30 20 10 0 KO US JP GR IT CA PO NL ES LU PT SI UK FR BE BU CH DE AT IR DK LT HU NO LV IC SE FI EE SK CZ Source: ILO, authors‘ estimations Seite 21 21 Further robustness checks Models without Japan: same results Girls‘ (dis-)advantage in math: similar results, but not as strong Male to female science self-concept (country mean) stronger gender-essentialist beliefs of male- and female-typical science self-concepts as part of the gendered selves General value of science (country mean) increased value of working in male-typical occupations also for girls no effect Gender pay gap higher benefits in male-typical occupations no effect Share women in management increased probability of success of girls for working in a male-typial occupational field no effect Seite 22 Girls‘ (dis-)advantage in math: interaction with sex -3.5 -3 Linear Prediction -2.5 -2 -1.5 -1 Predictive Margins with 95% CIs -4 -3 -2 -1 Female (dis-)advantage math girl=0 0 1 girl=1 Seite 23
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