Presentation on theme: "1 Wage differences between women and men in Sweden – the impact of skill mismatch by Mats Johansson and Katarina Katz Katarina Katz, Department of Economics."— Presentation transcript:
1 Wage differences between women and men in Sweden – the impact of skill mismatch by Mats Johansson and Katarina Katz Katarina Katz, Department of Economics and statistics, Karlstad University, Sweden
2 ABSTRACT We investigate skill mismatch and its impact on gender differences in wage gap and in returns to education in Sweden 1993 to 2002. Women are more likely to have more formal education than what is normally required for their occupation (overeducation), while men are more likely to have less (undereducation). Over- and undereducation contribute far more to the gender wage gap than years of schooling and work experience. In decompositions, adjusting for skill mismatch decreases the gender wage gap by between one tenth and one sixth. This is roughly a third to a half as much as is accounted for by segregation by industry. Thus, taking skill mismatch into account is essential for the analysis of gender wage differentiation, even though it does not alter the result that the estimated returns to education are smaller for women than for men in Sweden. Published by the Institute for Labour Market Policy Evaluation, Uppsala, Sweden as IFAU Working Paper 2007:13 www.ifau.se
3 Aim of study:Join insights and approaches from two fields of economics : Economics of gender Gender and wages ─Decomposition of the gender wage gap Economics of education Over-, Under- and Required Education ─ORU and wage effects of skill mismatch
4 Standard result from the ORU- literature: Women are more often overeducated than men. Men are more often undereducated than women.
5 Standard results from the ORU- literature: An overeducated person earns more than a person doing the same job who has only the required education. An overeducated person earns less than a person with the same education whose job requires this education An undereducated person earns less than a person doing the same job who has the required education. An undereducated person earns more than a person who has the same education but whose job doesn’t require more.
6 Why mismatch? Differences in ability (unobserved heterogeneity) More other human capital compensates for undereducation/overeducation compensates for less other human capital. Search theory (cost of finding a good match) Spatial restrictions (local labour markets). Assignment theory (supply and demand on the labour market do not match). BUT WHY THE GENDER DIFFERENCE?
7 Frank (1978): Married women are tied movers/stayers Weak or no support for Frank’s hypothesis in Büchel (2000), McGoldrick & Robst (1996), Battu et al. (2000) and Büchel & Battu (2003). Married women are not more overeducated than single. Rubery et al (1989): Women, particularly, part-timers are often overqualified and underpaid (education & content of work). They also tend to undervalue the skill level of their jobs. Many ORU-studies disregard gender and hardly any place it at the centre of attention.
8 Our research questions: 1.Are women more often overeducated for their jobs and men more often undereducated in Sweden too? 2.Does skill mismatch explain the (observed) gender difference in returns to education? 3.Does skill mismatch explain any part of the persistent gender wage differential in Sweden? 4.Have changes in skill mismatch had an impact on the development of the gender wage differential in the 1990s?
9 Data HINK/HEK –repeated yearly cross-section 1993-2002 (collected by Statistics Sweden). – 6 000-10 000 employed individuals aged 20- 64 each year (self-employed excluded). – register data on level of education – interview data on occupation
10 Structure of study: Estimated probability of being over/under- educated, separately by gender (multinomial logit). Estimated wage equations to measure returns to education with/without controls for over- and undereducation, separately by gender (OLS). Oaxaca-Blinder decomposition of the gender wage differential with a ORU-model. Juhn-Murphy-Pierce decomposition of the change in gender wage differential 1993-2002.
11 Explanations of gender wage differentials human capital job segregation wage discrimination value discrimination
12 To operationalise empirically: Oaxaca Blinder decomposition Wage equation: ln W i =X i β j j = f, m ”Explained” (endowment) term Unexplained term
13 Wage equation Dependent variable: ln (hourly wage) Independent variables: Experience + experience squared Years of education Years of undereducation (if positive) Years of overreducation (if positive) Industry (12) Country of birth (3 categories) Region (3 categories) Marital status Children under 18 in household
14 ORU-measure We use Socio Economic Index (SEI) which defines the level of education required for occupations. From the level we impute years of education. We use a non-standard specification of the ORU wage equation: ln W = α + β 1j AE + β 2j OE + β 3j UE + β j X + ε j = f, m where AE = years of actual education RE = years of required education
15 Robustness of data and measure? Swedish studies using the Level of Living Survey show (le Grand, Szulkin, Thålin & Korpi) –Levels of male/female OE and UE similar to ours. –High correlation between SEI-based measure and respondents self-assessment of required education. Study using Living Conditions Survey (Oscarsson and Grannas) –Alsp find similar levels when using SEI –Find substantially less OE when using a more classification (SSYK)
16 Over-, Under and Adequate Education Sweden 1993-2002
17 * Probabilities were also estimated by a multinomial logit model which confirmed the descriptive results.
19 Factors associated with the higher probability of overeducation: Level of education: – Women: Long ( ≥ 3 yrs) secondary or long ( ≥ 3 yrs) higher education. –Men: Short secondary education. Field of education: –Both women and men: Transport & communication, services. Industry: –Women: Transport & communication, trade –Men: Transport & communication, public administration, culture & recreation. Sector: –Women: Private sector –Men: Local government Work time: –Both women and men: Part-time work
20 Factors associated with the lower probability of overeducation: Level of education: – Women: Short higher education. –Men: Short and long higher education. Field of education: –Women: Health care –Men: Health care, science, technology & manufacturing. Industry: –Women: Financial and insurance, health care, recreation and culture –Men: Hotels & restaurants, construction Sector: –Women and men: Central government.
21 Family and female overeducation: Men are more likely to be undereducated and less likely to be overeducated if they are married. Women are slightly more likely to be overeducated if they are single. Women with children aged 0-6 are somewhat less likely to be overeducated than women without children. The difference between women without children and with children age 7-16 is not statistically significant. Thus, the Swedish data do not support the hypothesis that female overeducation is due to restricted labour markets for women with family committments.
22 Both models include experience, education, industry, large city dummy, marital status, children, country of birth.
23 Standard Mincer model Returns to actual education, no control for over- or undereducation (per year) –4.6% for women –5.8% for men ORU-model: Returns to education 2002 (per year): For those having the appropriate level: –5.6 % for women –7.7 % for men For a year of overeducation: –2.3 % for women (5.6% - 3.4%) –3.2 % for men (7.7% - 4.5%) Reward per year of undereducation: –2.9 % for women –3.5 % for men
24 Gender wage differentials in Sweden 1993–2002 (unadjusted and adjusted), female average wages in percent of the male wages. Adjusted differential Unadjusted differential Adjusted, female Adjusted, male 1993 85.088.689.9 1994 85.687.291.4 1995 85.188.590.9 1996 86.290.592.5 1997 86.089.191.5 1998 84.588.790.9 1999 85.189.490.8 2000 86.090.191.1 2001 85.489.390.0 2002 86.890.091.7
25 Decomposition of the gender gap in log wages using the parameters estimated for women Total wage gap Total endow - ments Work exper- ience Educa-tionSkill mis- match IndustryOther 1993 0.1630.0420.008-0.0050.0140.026-0.001 1994 0.1550.0180.006-0.0040.0150.001-0.001 1995 0.1610.0390.005-0.0080.0140.029-0.002 1996 0.1480.0490.005-0.0070.0090.0410.000 1997 0.1500.0350.000-0.0060.0130.030-0.002 1998 0.1690.0490.000-0.0050.0140.042-0.001 1999 0.1620.0500.001-0.0080.0140.0420.000 2000 0.1510.0460.003-0.0160.0140.048-0.002 2001 0.1580.0450.000-0.0190.0200.046-0.001 2002 0.1420.0370.005-0.0140.0150.0320.000
26 Decomposition of the gender gap in log wages using the parameters estimated for men Total wage gap Total endow - ments Work exper- ience Educa-tionSkill mis- match IndustryOther 1993 0.1630.0420.008-0.0050.0140.026-0.001 1994 0.1550.0180.006-0.0040.0150.001-0.001 1995 0.1610.0390.005-0.0080.0140.029-0.002 1996 0.1480.0490.005-0.0070.0090.0410.000 1997 0.1500.0350.000-0.0060.0130.030-0.002 1998 0.1690.0490.000-0.0050.0140.042-0.001 1999 0.1620.0500.001-0.0080.0140.0420.000 2000 0.1510.0460.003-0.0160.0140.048-0.002 2001 0.1580.0450.000-0.0190.0200.046-0.001 2002 0.1420.0370.005-0.0140.0150.0320.000
27 Results of Oaxaca decomposition A quarter to a third of gap ”explained” if parameters for women are used, 2/5 if those for men are. Contribution of actual education < 0, of experience very small 1.5 - 3 percentage point differential is attributable to mismatch 3-6 percentage point differentiable attributable to industry
28 Results of JMP decomposition Total change 1993-2002 very small – 2.1 percentage points. But according to JMP it is the net of larger changes in opposite directions –Gender specific factors tend to decrease gap This is mainly the mainly ”unobserved skills” term which includes discrimination –Changes in wage structure tend to increase it This is mainly the ”unobserved wage structure” i.e. wage dispersion
29 Summary only about half of employed women and men have an occupation that matches their level of education. women are more often overeducated than men women are less often undereducated than men more undereducation and less overeducation among those with long experience formal educational requirements and the education of the employee correspond more closely in the public than in the private sector independently of skill mismatch women received smaller rewards to education than men. skill mismatch does contribute to the gender wage gap. with the same over- and under-education the gender wage gap would decline by 1.4 (female eq.) or 2.3 (male eq.) percentage points. skill mismatch accounts for a considerably larger part of the endowment term than traditional human capital variables
30 Suggestion for further work 1 Panel studies – with a gender perspective To control for unobserved heterogeneity when doing a gender comparison. To observe OE and UE changes over careers and see if there is a gender difference. Korpi & Thålin (2007) find that the overeducated do not ”catch up” in terms of wages – but is this equally the case for women and men? To observe effects of changes in family status and of parental leave on both incidence of and wage effects of overeducation and see if these vary according to gender.
31 Suggestion for further work 2 More detailed study of the occupations in which the expert- and or self-assessed required education levels often differ from the actual. –Scrutiny of assigned required education – do the classifications have a gender bias? –Qualitative study – in-depth interviews with respondents and employers about the occupations that quantitative study identify as having high levels of overeducation. –Integrate experience and results from comparable worth studies – for instance the ”equal opportunity wage revisions” that are mandatory for Swedish employers.
32 Suggestion for further work 3: Integrate over- and undereducation in the mainstream of studies of gender wage differentials and of gender differences in careers (job mobility and wage increases).
33 Suggestion for further work 4 Similar studies focussing on immigrants – integrating both gender and ethnic dimension.