The influence of gender beliefs and early exposure to math, science and technology in female degree choices Laura Cristina Rojas Blanco PhD The University of York Politics, Economics and Philosophy July, 2013 2 Abstract This research consists of three sections testing the hypothesis that gender roles and gender-stereotyping of certain fields of study could be associated with women choosing traditionally female degree options characterized by lower wages. The analysis is framed within the identity economics framework. In the first chapter, data from the 1970 British Cohort Study supports the hypothesis that teenage girls are more likely to accept gender-equal beliefs when their mother shares these beliefs or she works; and that having gender equal beliefs and developing early mathematical and technological skills either encourage girls to study for high-paying degrees or discourage them from entering female-dominated degrees. The second chapter analyses the responses from an online questionnaire applied to female academics at the University of York. Such survey collected testimonies about their experiences regarding the construction of gender, encouragement and discouragement in mathematics, science and technology at school and the household environments; and their degree choice. Results provide some evidence in favour of the initial hypothesis, but they also show a disassociation between how women perceive the sex-typing of subject fields and their own confidence in their capabilities and tastes. It also suggests that bad experiences with certain subjects are more relevant in keeping women away from high-earnings degrees than the lack of positive experiences. Finally, the third chapter estimates earnings functions and provides a gender wage decomposition using data from the 1970 British Cohort Study at ages 29 and 34. Results do not support the hypothesis that having a high-earnings degree is associated with higher wages for women. Although there is an initial premium, it disappears by age 34. In contrast, working in a high-earnings occupation is positively associated with higher wages, while remaining in female-dominated occupations is negatively associated with wages for women. 3 List of contents Abstract 2 List of contents 3 List of tables 6 List of figures 8 List of graphics 9 Acknowledgements 10 Author’s declaration 11 1. Introduction 12 2. Literature review 21 2.1. The human capital model 22 2.2. Discrimination and the gender wage gap 28 2.2.1. Empirical literature 32 2.3. Occupational segregation and female labour participation 36 2.4. Subject choice within education and the wage gap 44 2.5. Skill-bias technological change and the wage gap 46 2.6. Identity economics and gender roles 49 2.7. Conclusion 64 3. Getting a ‘girlie’ education: gender beliefs and early mathematical and technological stimuli in female degree choices 65 3.1. Introduction 65 3.2. Model 70 3.3. Dataset: 1970 British Cohort Study 74 3.4. Construction of gender identity 76 3.4.1. Variable description 76 3.4.2. Model specification 85 3.4.3. Probit estimation results for believing that women can do the same job as men 86 3.4.4. Probit estimation results for believing in gender equality in sex and marriage 92 3.4.5. Ordinary least square estimation results for gender equality in sex and marriage score 96 3.5. Degree choice 99 3.5.1. Variable description 99 4 3.5.2. Model specification 104 3.5.3. Estimation results for degree choice 105 3.6. Conclusion 112 4. A mixed methods approach to female degree choices 114 4.1. Introduction 114 4.2. Dataset 116 4.3. Descriptive analysis 119 4.3.1. Degree choice 120 4.3.1.1. Reasons for choosing a degree 120 4.3.1.2. Role models and others’ influence 130 4.3.2. School environment 136 4.3.2.1. Teachers’ behaviour towards math, science and technology 140 4.3.2.2. Participation in extracurricular activities 146 4.3.2.3. Remarks on school environment 148 4.3.3. Household environment 150 4.3.3.1. Toys 151 4.3.3.2. Technological confidence 155 4.3.3.3. Parents’ behaviour towards math, science and technology 156 4.3.3.4. Parental aspirations 162 4.3.4. Beliefs and personal views 165 4.3.5. Satisfaction 172 4.4. A model of degree choice 177 4.4.1. Model 177 4.4.2. Findings 180 4.5. Conclusions 183 5. Exploring the correlation between degree and occupational choice with the earnings function and gender wage gap decomposition 187 5.1. Introduction 187 5.2. Methodology 190 5.2.1. Human capital model 191 5.2.2. Augmented human capital model 193 5.2.3. Variable inclusion model 194 5.2.4. Full model 195 5.2.5. Wage decomposition 195 5.3. Data discussion 196 5 5.4. Results 206 5.4.1. Probability of working for women 206 5.4.2. Human capital model 208 5.4.3. Augmented human capital model 212 5.4.4. Results for high-earnings degrees 217 5.4.5. Results for female-dominated degrees 219 5.4.6. Results for high-earnings occupations 221 5.4.7. Results for female-dominated occupations 223 5.4.8. Full model 225 5.4.9. Wage decomposition 229 5.5. Conclusions 235 6. Concluding remarks 237 7. Appendices 248 Appendix 1: Observations per variable used in estimating gender identity and degree choice, as percentage of cohort size 249 Appendix 2: Subject fields of study classified as traditionally female 252 Appendix 3: Degree subject fields associated with high earnings 253 Appendix 4: Online questionnaire 254 Appendix 5: Observations per variable used in estimating earnings, as percentage of cohort size 267 Appendix 6: Standard Occupational Classification codes (1990) classified as traditionally female 269 Appendix 7 Standard Occupational Classification codes (1990) associated with high earnings 270 8. References 271 6 List of tables TABLE 1: Science and Engineering graduate students in the USA, by field of study and sex. Year: 2008 13 TABLE 2: Correlations observed between 3-point Likert attitudinal variables regarding gender 77 TABLE 3: Correlations observed between 5-point Likert scale maternal attitudinal variables regarding gender 81 TABLE 4: Summary of descriptive statistics related to gender identity, by respondent's sex 84 TABLE 5: Probit estimates for believing women can do the same job as men 90 TABLE 6: Probit estimates for believing in gender equality regarding sex and marriage 94 TABLE 7: Ordinary least squares estimates for gender equality in sex and marriage index 97 TABLE 8: Summary of descriptive statistics related to degree choice, by respondent's sex 102 TABLE 9: Probit estimates for degree choice 110 TABLE 10: Educational level and degree types 119 TABLE 11: Main reason to choose degree program, by degree choice 121 TABLE 12: Correlation coefficients between degree choices and possible reasons to study a particular program 122 TABLE 13: Participation in extracurricular activities and its correlation with degree choice 147 TABLE 14: Correlation coefficients between academic ability and degree choice 149 TABLE 15: Toys frequently played with during childhood and its correlation with degree choices 152 TABLE 16: Parental aspirations 163 TABLE 17: Distribution of level of agreement with gender stereotype statements and its correlation with degree choice 166 TABLE 18: Distribution of parental beliefs 168 TABLE 19: Correlation coefficients for respondent's and parental beliefs 169 TABLE 20: Satisfaction and its correlation with degree choice 173 TABLE 21: Descriptive statistics for the balanced sample, by age group 179 TABLE 22: Average marginal effects of the binary response models on degree choice 183 TABLE 23: Descriptive statistics for graduates in the 1970 BCS, by wave 204 TABLE 24: Probit results for working graduate women 207 7 TABLE 25: Earnings functions according to the human capital model, with and without Heckman sample selection correction 209 TABLE 26: Earnings functions according to the augmented human capital model 215 TABLE 27: Earnings functions, including holding a high-earnings degree into the model 218 TABLE 28: Earnings functions, including holding a female-dominated degree into the model 220 TABLE 29: Earnings functions, including working in a high-earnings occupation into the model 222 TABLE 30: Earnings functions, including working in a female-dominated occupation into the model 224 TABLE 31: Earnings functions, full model 225 TABLE 32: Gender wage gap decomposition 234 8 List of figures FIGURE 1: Tree game payoffs 73 FIGURE 2: Concept map for degree choice 186 9 List of graphics GRAPHIC 1: Percentage of women employed for some gender segregated occupations in the United States. Year: 2009………………………………………………………………………………………………………14 GRAPHIC 2: Mean and median annual wage for some gender segregated occupations in the United States. Year: 2009………………………………………………………………………………………………………15 GRAPHIC 3: Graduate qualifications obtained on high education institutions in the United Kingdom, by gender and subject area. Year: 2009-2010……………………………………………………… 17 GRAPHIC 4: Mean salaries for graduate in the United Kingdom, by gender and subject area. Year: 2009-2010…………………………………………………………………………………………………………………… 18 GRAPHIC 5: Occupational destination of graduates employed in the UK, by gender. Year: 2010……………………………………………………………………………………………………….………………………………19 GRAPHIC 6: Sexuality index score distribution, by gender …………………………………… ………………79 GRAPHIC 7: Maternal gender equality index score distribution, by cohort member's gender…82 GRAPHIC 8: Gross log wage distribution, by gender…………………………………………………………… 200 10 Acknowledgements A special thanks to my supervisor, Professor Karen Mumford for her constant support and comments. I am also grateful to Professor Stevi Jackson, Professor Jonathan Bradshaw, Emma Tominey and Professor Sarah Brown for their comments; to Alison Watson and to my family and friends for their emotional support. I am grateful to the University of York for awarding me with an Overseas Research Scholarship, without which I would have never been able to study my PhD, and to Universidad de Costa Rica for funding me through their credit scheme to study abroad. [...]... the work presented in this dissertation is my own and belongs to the research carried out as a student at the University of York from October, 2010 to the present day 11 The influence of gender beliefs and early exposure to math, science and technology in female degree choices “One might ask: if an education geared to the growth of the human mind weakens femininity, will an education geared to femininity... below that of men after ten years of work experience Among their findings, the authors point out that part-time and intermittent employment, as well as greater constraints to change jobs, are the key determinants of the evolution of the female wage and the widening of the gender gap over time Notice that, although their findings do not contradict this thesis’ initial hypothesis, they do find that occupational... determinant in the evolution of the wage differential Nonetheless, other studies, which would be mentioned later, do point out to the contribution of the field of study in explaining the gender gap, a topic that is worth studying because even if the evolution of female wages does not respond to the career choice, it does determine the initial level, i.e., the base from which to start growing In general,... these beliefs, the exposure to mathematics, science and technology or other childhood experiences are associated with girls choosing high-earnings or male-dominated degrees And, finally, it tests whether these degrees actually imply higher earnings for women In all cases, the scope of the study is limited to the United Kingdom The reason for this is that the United Kingdom has long invested in rich... cost and the cost of training, k Further, by rearranging terms and defining G as the present value of the net profits from training labour, the above condition becomes (Becker, 1993, p.p 32-33): (2) , with ∑ and That is, the marginal costs of training, expressed by the term gains expected from it ( , must equal the ): if the net flow of expected marginal productivities of labour was higher than the. .. for the United States, Blau and Kahn (2006) provide evidence of a glass ceiling effect, despite the narrowing of the gender wage gap around the mean over the past decades Although the female mean wage relative to the male’s wage in the US had narrowed to 91% by 1998, the authors argue that the narrowing of the gender wage gap actually slowed down at the top of the distribution and even increased during... for one type of workers would push their wages up but, in time, this higher wage would create an incentive for the workers in the other sector to invest enough in their human capital in order to mobilize to the other, more dynamic one Eventually, this would increase the labour supply in the first sector and reduce it in the second one, so that the market wages would tend to converge again However, this... higher than the marginal cost of training, the firm would have an incentive to keep investing up to where (2) is satisfied On the other hand, if the present value of such net revenues were lower than the costs of training, the firm will cut back on the investment in human capital Becker points out that G-C are the net returns from training, which implies that MP0 need not be equal to W0 In fact, MP0 only... Further, Gronau (1988) argues that participation in the labour force and training decisions are endogenous to the human capital model, so that as the probability of dropping out of the labour market increases, the probability of investing in training decreases and vice versa So the gender wage gap persists because demographic changes (mainly motherhood decisions) affect the probability of women dropping... employees in the workplace are also found to be significant in explaining the gender wage gap This reflects the same pattern observed for the United States and commented above, according to which occupational segregation is key in understanding the gender wage gap Walby (1988, p 1) also points out to occupational segregation as the main cause for the gender wage gap in the West In contrast, Glover and Kirton . including holding a female-dominated degree into the model 220 TABLE 29: Earnings functions, including working in a high-earnings occupation into the model 222 TABLE 30: Earnings functions, including. Earnings functions according to the augmented human capital model 215 TABLE 27: Earnings functions, including holding a high-earnings degree into the model 218 TABLE 28: Earnings functions, including. The influence of gender beliefs and early exposure to math, science and technology in female degree choices Laura Cristina Rojas Blanco PhD The