InGrid2 Research Visit Project on Female Labor and Gender Discrimination in STEM fields - Progress and Preliminary Outcomes Prof. Dr. Seo-Young Cho Faculty of Economics Philipps-University of Marburg, Germany HIVA KU-Leuven, 30 th March, 2018
Main Focus and Research Questions • This project addresses female underrepresentation in STEM fields that is still significant in most countries despite noticeable improvement in female education and employment over the last decades. • This project aims to identify systematic gender inequality in STEM fields: - Whether gender pay gaps in STEM fields are larger than in non- STEM fields. - Whether gender gaps in social capital – trust, relationship, network, etc. – are greater in STEM fields than non-STEM fields. 2
During My Research Visit • Data examination and organization - European Working Conditions Survey (EWCS) • Discussions on the concepts and data with HIVA researchers • Participation in Workshop Education Economics at Economics Faculty, KU Leuven • Preliminary regression analysis 3
Hypotheses for Empirical Investigation • Gender effect H1. The gender effect on wage/perceived discrimination/ trust in workplace/ network participation is negative for female workers. • STEM-specific gender effect H2. The negative gender effect is greater for female workers in STEM fields compared to others in non-STEM industries. • Gender-matching effect H3. Gender-matching environments reduce the negative gender effect. 4
Model Outcome i = β 1 gender i + β 2 STEM i + β 3 gender i *STEM i + β 4 gender/boss i + β 5 gender-matching i + X i ψ + Z i ω + + country i Ɲ + u i • Sample: full-time employed (unemployed, part-time, self-employed are excluded in the sample) to minimize self-selection into employment types • i = {1,,,,, 34,370 }, individual workers • Outcome vector = {wage; trust in colleagues/management; perceived discrimination – gender, age, and ethnic; fairness in workload and evaluation; training opportunities; cooperation with colleagues/bosses; experience of being abused at work} 5
Model (cont.) • Gender: the gender of employee i (1 = female, 0 = otherwise) • Gender/boss: the gender of the direct boss of employee i • Gender-matching: gender of employee i * gender of the boss • STEM: industrial classification indicating STEM intensity • Vector X: individual characteristics (education level, household size, marital status, age, years of tenure, tasks, etc.) – controlling factors on the supply side • Vector Z: firm characteristics (firm size, location, main production, years, etc.) – controlling factors on the demand side • Country: country fixed effect (35 countries) 6
STEM Identification (Measurements) • Approach 1 (US Brooking Institute classification) - Constructing STEM dummies extended: architecture and engineering + computer and math + life and health limited: : architecture and engineering + computer and math • Approach 2 (UK Commission for Employment and Skills classification) - STEM intensity based on proportion of people with STEM qualifications (SOC composite score) 7
How much are you treated fairly at workplace? oprobit ME Rob. Std. Err. z P>z gender -.0844422 .0159609 -5.29 0.000 boss_gender .0514125 .0249771 2.06 0.040 gender#boss_gender .0550371 .0301255 1.83 0.068 STEM .1520256 .0258448 5.88 0.00 gender#STEM -.1627088 .03581 -4.54 0.000 Controls (X) Yes Controls (Z) Yes Country FE Yes 8
How much does your boss give you praise and recognition when you do a good job? oprobit ME Rob. Std. Err. z P>z gender -.0634189 .0154391 -4.11 0.000 boss_gender .1293307 .0245852 5.26 0.000 gender#boss_gender .033078 .0296301 1.12 0.264 STEM .1671826 .0252718 6,62 0.000 gender#STEM -.1850912 .0360024 -5.14 0.000 Controls (X) Yes Controls (Z) Yes Country FE Yes 9
Have you taken training (paid by employers) over the past 12 months? Probit ME Rob. Std. Err. Z P>z Gender -.0081336 .0183569 -0.44 0.658 boss_gender .234772 .0282059 8.32 0.000 gender #boss_gender .0847273 .0341452 2.48 0.013 STEM .4770867 .030206 15.79 0.000 gender#STEM .0230659 .0423598 0.54 0.586 Controls (X) Yes Controls (Z) Yes Country FE Yes 10
Log Wage OLS Coef. Std. Err. t P>t gender -.2698182 .0214033 -12.61 0.000 boss_gender -.0397034 .0345846 -1.15 0.251 Gender #boss_gender -.0238794 .0417666 -0.57 0.568 STEM .4614581 .0371782 12.41 0.000 gender#STEM .1145812 .0526771 2.188 0.030 Controls (X) Yes Controls (Z) Yes Country FE Yes 11
Some preliminary findings • STEM provides positive working environments for men - Male workers perceive higher levels of fairness and recognition, earn more and receive more training opportunities in STEM fields, compared to men in non-STEM fields. • The effect of STEM is mixed for female workers - Negative effect on non-monetary conditions: female workers perceive lower levels of fairness and recognition in STEM fields, compared to other female workers in non-STEM fields. - Positive effect on earnings and job training & the positive effect of STEM on earning is greater for females than males! 12
Some preliminary findings (cont.) • Gender inequality in STEM fields is more related to non-monetary forms of perceived discrimination and working relationship than monetary ones. • Positive gender-matching effect - Female workers perceive higher levels of fairness and receive more training opportunities if they work with female bosses. Gender inequality in STEM fields can be reduced by promoting women into lead-positions. 13
Next Steps • Extension of data - Different waves of EWCS - EU Labor force Data (incorporating detailed education/degrees variables and employees’ intention to leave) • Causality issues: self-selection into STEM fields due to unobserved gender-based characteristics - PSM - Structural Simultaneous Equations Model (wage-training-trust- networks) 14
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