eva schlenker university of hohenheim mannheim, 21st march · eva schlenker university of hohenheim...
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The University of Hohenheim The Labour Supply of Women in STEM
Eva Schlenker University of Hohenheim Mannheim, 21st March
Motivation
• the labour force in science, engineering, technology, and mathematics (STEM)
is a key issue in industrialised countries
• continued technological progress requires more highly skilled workers
• the demographic change reduces the quantity of the labour force
• occupations in STEM have always been highly segregated by gender
• European policies aim at reducing gender segregation in the labour market
• many regional and national campaigns aim at increasing women‘s
share in STEM (e.g.,“Girl‘s day – Future Prospects for Girls“ in Germany)
Agenda
• Literature review
• Data and identification strategy
• Empirical results
• Conclusion
Literature review I
• few economic studies on occupation specific labour supply
• but: extended strands of literature on female labour supply and gender segregation
Gender segregation
• Phelps/Arrow (1972): statistical discrimination enhances gender segregation
• Polachek (1981): women anticipate times out of the labour force and choose
occupations with low rates of atrophy
• Akerlof and Kranton (2000): gender identity increases the chance to choose a
typical male or female job
• Blau (2012): gender segregation has been declining at a diminished pace
Literature review II
Fertility and educational field
• Lappegård and Rønsen (2005): correlation between fields of study and fertility
behaviour in Norway
• Hoem et al. (2006): field of study is a better indicator for fertility than the
educational level in Sweden
• Oppermann (2012): women in mixed occupations face lower probability for
giving birth in Germany
Literature review III
Women in STEM
• Leslie and Oaxaca (1998): influence of parental level of education
• Minks (1996, 2001): worse job opportunities for female graduates in STEM
• Legewie and DiPrete (2012): influence of schooling environment
• Schlenker (2009): female engineers display a specific employment behaviour
• Carrell et al. (2012): girls are more likely to choose STEM and perform better if
they are taught by a female professor
lack of academic understanding of differences in the employment
behaviour of occupational groups
Hypotheses
Women in STEM behave differently in terms of labour supply and their
reaction to labour market policies differs from other women.
Possible explanations:
1. higher labour participation due to high wage levels
2. higher labour participation because of high intrinsic motivation
3. less opportunities to work parttime and/or for re-entry after times out of the
labour force
Identification strategy I
• problems in the econometric analysis of occupational groups:
• selection effects
• unobserved heterogeneity
• biased and inconsistent estimates can occur
• Blundell et al. (1998) introduce a grouping estimator to overcome these
problems
• core idea: definition of groups with homogenous developments of net
wages and other net income
Identification strategy II
• Implementation of the grouping estimator by a control function approach:
1. Estimation of reduced forms using labour participation, log wages,
and non-wife income as dependent variables
2. Estimation of the final equation of labour supply as:
including group and time effects, a vector of socio-demographic
variables, hourly net wages, non-wife income and the residuals of the
reduced forms.
Data I
• EU-SILC cross-sectional waves 2007, 2008, and 2009
• pooled dataset including
• women born between 1960 and 1990
• neither retired nor in education, military or social services
• approximately 205.000 individuals included
• classification as “working in STEM“ if ISCO-88 code equals one of the
following: 21, 31, 71-73, 82, 93
Data II
• division of the data set into 72 groups per wave by 10-year birth cohort,
educational level, geographical area, and working in STEM
• data on the national level of spending on childcare (% of GDP in CC) and on
the national level of spending on family allowances and child benefits (% of
GDP on FA) is merged from the online database of Eurostat
• also included are the national female employment share and the share of
women working in parttime jobs (available via the online database of
Eurostat)
Percentage of women in STEM in the data set per country
Source: EU-SILC 2007, 2008, 2009; own calculations.
Descriptive results Variable Mean Std. Dev. min max N
Women in STEM
labour participation 0.747 0.435 0 1 22692
hours worked per week 38.351 6.890 1 84 16877
year of birth 1971.03 7.504 1960 1990 22692
youngest child 0-3 0.143 0.350 0 1 22692
youngest child 4-6 0.101 0.301 0 1 22692
youngest child 7-10 0.122 0.327 0 1 22692
Women in other occupations
labour participation 0.801 0.399 0 1 181918
hours worked per week 35.66 9.499 1 99 145002
year of birth 1971.35 7.624 1960 1990 181918
youngest child 0-3 0.157 0.364 0 1 181918
youngest child 4-6 0.110 0.312 0 1 181918
youngest child 7-10 0.125 0.331 0 1 181918
Source: EU-SILC 2007, 2008, 2009; own calculations.
Estimation results: reduced forms
Source: EU-SILC 2007, 2008, 2009, own calculations. + p<0.1, * p<0.05, ** p<0.01, *** p<0.001.
Labour participation Log hourly wage Non-wife income Variable Coefficient Std. Dev Coefficient Std. Dev Coefficient Std. Dev youngest child 0-3 -0.851*** 0.009 -0.255*** 0.025 4.165*** 0.244
youngest child 4-6 -0.384*** 0.010 -0.087*** 0.012 2.299*** 0.273
youngest child 7-10 -0.233*** 0.009 -0.054*** 0.008 1.659*** 0.255
hazard rate 0.388*** 0.058 Anteil Hochqualif.
Notes: All three models include a complete set of group and time effects as well as a full set of interaction terms. Country effects are also controlled for. Wages and non-wife income are measured in purchase power standards.
Estimation results: final estimation of labour suuply
Source: EU-SILC 2007, 2008, 2009, own calculations. + p<0.1, * p<0.05, ** p<0.01, *** p<0.001.
Weekly working hours Weekly working hours Weekly working hours Variable Coefficient Std. Dev Coefficient Std. Dev Coefficient Std. Dev
working in STEM 7.082*** 0.965 8.040*** 1.021 -12.44 25465.3
youngest child 0-3 -3.565*** 0.240 -3.516*** 0.246 -3.339*** 0.285
youngest child 4-6 -3.349*** 0.123 -3.327*** 0.125 -3.146*** 0.143
youngest child 7-10 -2.775*** 0.090 -2.751*** 0.092 -2.536*** 0.105
STEM x yc 0-3 1.016*** 0.243 1.005*** 0.243 0.881*** 0.271
STEM x yc 4-6 1.151*** 0.239 1.142*** 0.239 0.976*** 0.268
STEM x yc 7-10 1.174*** 0.214 1.174*** 0.214 0.965*** 0.239
high education 1.295*** 0.046 1.334*** 0.047 1.496*** 0.056
% of GDP in FA -2016.1*** 289.4 -1785.2*** 351.8
% of GDP in CC 405.4** 156.7 309.3+ 180.2
STEM x % of GDP in FA -38.53+ 20.5 -46.89+ 25.15
STEM x % of GDP in CC -138.5*** 33.54 -144.2*** 39.49 Anteil Hochqualif.
N 161879 161879 113949
Adj. R² 0.226 0.228 0.258
Notes: All three models include a complete set of group and time effects as well as country and birth cohort effects. It is also controlled for log wage, non-wife income, estimated residuals of three reduced forms. Modell (3) only includes cohabiting women.
Estimation results: final estimation of labour suuply
Source: EU-SILC 2007, 2008, 2009, own calculations. + p<0.1, * p<0.05, ** p<0.01, *** p<0.001.
Weekly working hours Weekly working hours Weekly working hours Variable Coefficient Std. Dev Coefficient Std. Dev Coefficient Std. Dev
working in STEM 7.082*** 0.965 8.040*** 1.021 -12.44 25465.3
youngest child 0-3 -3.565*** 0.240 -3.516*** 0.246 -3.339*** 0.285
youngest child 4-6 -3.349*** 0.123 -3.327*** 0.125 -3.146*** 0.143
youngest child 7-10 -2.775*** 0.090 -2.751*** 0.092 -2.536*** 0.105
STEM x yc 0-3 1.016*** 0.243 1.005*** 0.243 0.881*** 0.271
STEM x yc 4-6 1.151*** 0.239 1.142*** 0.239 0.976*** 0.268
STEM x yc 7-10 1.174*** 0.214 1.174*** 0.214 0.965*** 0.239
high education 1.295*** 0.046 1.334*** 0.047 1.496*** 0.056
% of GDP in FA -2016.1*** 289.4 -1785.2*** 351.8
% of GDP in CC 405.4** 156.7 309.3+ 180.2
STEM x % of GDP in FA -38.53+ 20.5 -46.89+ 25.15
STEM x % of GDP in CC -138.5*** 33.54 -144.2*** 39.49 Anteil Hochqualif.
N 161879 161879 113949
Adj. R² 0.226 0.228 0.258
Notes: All three models include a complete set of group and time effects as well as country and birth cohort effects. It is also controlled for log wage, non-wife income, estimated residuals of three reduced forms. Modell (3) only includes cohabiting women.
Estimation results: final estimation of labour suuply
Source: EU-SILC 2007, 2008, 2009, own calculations. + p<0.1, * p<0.05, ** p<0.01, *** p<0.001.
Weekly working hours Weekly working hours Weekly working hours Variable Coefficient Std. Dev Coefficient Std. Dev Coefficient Std. Dev
working in STEM 7.082*** 0.965 8.040*** 1.021 -12.44 25465.3
youngest child 0-3 -3.565*** 0.240 -3.516*** 0.246 -3.339*** 0.285
youngest child 4-6 -3.349*** 0.123 -3.327*** 0.125 -3.146*** 0.143
youngest child 7-10 -2.775*** 0.090 -2.751*** 0.092 -2.536*** 0.105
STEM x yc 0-3 1.016*** 0.243 1.005*** 0.243 0.881*** 0.271
STEM x yc 4-6 1.151*** 0.239 1.142*** 0.239 0.976*** 0.268
STEM x yc 7-10 1.174*** 0.214 1.174*** 0.214 0.965*** 0.239
high education 1.295*** 0.046 1.334*** 0.047 1.496*** 0.056
% of GDP in FA -2016.1*** 289.4 -1785.2*** 351.8
% of GDP in CC 405.4** 156.7 309.3+ 180.2
STEM x % of GDP in FA -38.53+ 20.5 -46.89+ 25.15
STEM x % of GDP in CC -138.5*** 33.54 -144.2*** 39.49 Anteil Hochqualif.
N 161879 161879 113949
Adj. R² 0.226 0.228 0.258
Notes: All three models include a complete set of group and time effects as well as country and birth cohort effects. It is also controlled for log wage, non-wife income, estimated residuals of three reduced forms. Modell (3) only includes cohabiting women.
Estimation results: final estimation of labour suuply
Source: EU-SILC 2007, 2008, 2009, own calculations. + p<0.1, * p<0.05, ** p<0.01, *** p<0.001.
Weekly working hours Weekly working hours Weekly working hours Variable Coefficient Std. Dev Coefficient Std. Dev Coefficient Std. Dev
working in STEM 7.082*** 0.965 8.040*** 1.021 -12.44 25465.3
youngest child 0-3 -3.565*** 0.240 -3.516*** 0.246 -3.339*** 0.285
youngest child 4-6 -3.349*** 0.123 -3.327*** 0.125 -3.146*** 0.143
youngest child 7-10 -2.775*** 0.090 -2.751*** 0.092 -2.536*** 0.105
STEM x yc 0-3 1.016*** 0.243 1.005*** 0.243 0.881*** 0.271
STEM x yc 4-6 1.151*** 0.239 1.142*** 0.239 0.976*** 0.268
STEM x yc 7-10 1.174*** 0.214 1.174*** 0.214 0.965*** 0.239
high education 1.295*** 0.046 1.334*** 0.047 1.496*** 0.056
% of GDP in FA -2016.1*** 289.4 -1785.2*** 351.8
% of GDP in CC 405.4** 156.7 309.3+ 180.2
STEM x % of GDP in FA -38.53+ 20.5 -46.89+ 25.15
STEM x % of GDP in CC -138.5*** 33.54 -144.2*** 39.49 Anteil Hochqualif.
N 161879 161879 113949
Adj. R² 0.226 0.228 0.258
Notes: All three models include a complete set of group and time effects as well as country and birth cohort effects. It is also controlled for log wage, non-wife income, estimated residuals of three reduced forms. Modell (3) only includes cohabiting women.
Conclusion
• empirical results indicate significant differences in the employment behaviour of
women in STEM and of women in other occupations
• socio-demographic variables:
• women, especially mothers, work more hours if working in STEM
• but: women in STEM are more often out of the labour force
• influence of institutional settings:
• higher spendings on childcare increase female labour supply
• higher spendings on family allowances decrease female labour supply
• additional effects for women in STEM are small in size
• further research needs to include firm-level data to control for working
conditions, e.g., the provision of parttime jobs
Literature
•Akerlof, G. A. and Kranton, R. E. (2000), Economics and Identity, Quarterly Journal of Economics 115(3), pp. 715–753. •Arrow, K .J. (1972), Models of Job Discrimination, in A. H. Pascal (ed.), Racial Discrimination in Economic Life, Lexington, MA: D.C. Heath, pp. 83–102. •Blau, F. D., Brummund, P., and Liu, A. Y. H. (2012), Trends in Occupational Segregation by Gender 1970-2009: Adjusting for the Impact of Changes in the Occupational Coding System, IZA Discussion Paper 6490, Institute for the Study of Labor (IZA). •Blundell, R., Duncan, A., and Meghir, C. (1998), Estimating Labor Supply Responses Using Tax Reforms, Econometrica 66(4), pp. 827–861. •Carrell, S. E., Page, M. E., and West, J. E. (2010), Sex and Science: How Professor Gender Perpetuates the Gender Gap, Quarterly Journal of Economics 125(3), pp. 1101–1144. •Hoem, J. M., Neyer, G., and Andersson, G. (2006), Education and Childlessness – The Relationship between Educational Field, Educational Level, and Childlessness among Swedish Women born in 1955-59, Demographic Research 14(15), pp. 331–380. •Lappegard, T. and Ronsen, M. (2005), The Multifaceted Impact of Education on Entry into Motherhood, European Journal of Population – Revue Europeenne De Demographie 21(1), pp. 45–75. •Leslie, L. L., McClure, G. T., and Oaxaca, R. L. (1998), Women and Minorities in Science and Engineering, a Life Sequence Analysis, The Journal of Higher Education 69(3), pp. 239–276. •Minks, K.-H. (1996), Frauen aus technischen und naturwissenschaftlichen Studiengängen – Ein Vergleich der Berufsübergänge von Absolventinnen und Absolventen, vol. 116, Hannover: Hochschulplanung. •Minks, K.-H. (2001), Ingenieurinnen und Naturwissenschaftlerinnen – neue Chancen zwischen Industrie- und Dienstleistungsgesellschaft: Ergebnisse einer Längsschnittuntersuchung zur beruflichen Integration von Frauen aus technischen und naturwissenschaftlichen Studiengängen, vol. 153, Hannover: Hochschulplanung. •Oppermann, A. (2012), A new Color in the Picture: The Impact of Educational Fields on Fertility in Western Germany, SOEP Papers 496, German Institute for Economic Research (DIW). •Phelps, E. S. (1972), The Statistical Theory of Racism and Sexism., American Economic Review 62, pp. 659–661. •Polachek, S. W. (1981), Occupational Self-Selection: A Human Capital Approach to Sex Differences in Occupational Structure, The Review of Economics and Statistics 63(1), pp. 60–69. •Schlenker, E. (2009), The Labour Supply of Female Engineers in Germany, Austrian Journal of Statistics 38(4), pp. 255–264.