When opportunity knocks: Gender wage gaps and labor market churning
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Differences in earnings between men and women differ across time and countries. Yet, explanations for these changes, after taking into account worker characteristics, are lacking. In this research we study the relation between differences in earnings and movements in the labor market in transition countries. We find that periods of high churning are related to larger gender wage gaps.
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When opportunity knocks: Gender wage gaps and labor market churning
- 1. When the opportunity knocks When the opportunity knocks Large structural shocks and the gender wage gap Joanna Tyrowicz Lucas van der Velde Warsaw School of Economics FAME|GRAPE European Association of Comparative Economics Studies, September 2018
- 2. When the opportunity knocks Introduction Motivation Gender wage gaps vary greatly across countries and over time (Stanley and Jarrell 1998, Weichselbaumer and Winter-Ebmer 2007, ˜Nopo et al. 2012) But why does it vary...? Family-friendly policies and institutions (Blau and Kahn 2003, Weichselbaumer and Winter-Ebmer 2007) Work-time flexibility (Mandel and Semyonov 2005, Cha and Weeden 2014, Goldin 2014) Skill-biased technological change (Juhn et al. 1993, Card and DiNardo 2002, Lemieux 2006, Black and Spitz-Oener 2010) Labor market churning → Our curent paper
- 3. When the opportunity knocks Introduction This study → We analyze relation between GWG and large structural shocks Our contribution Obtain comparable GWG estimates from transition countries Unique measure of flows We find a relation between churning and GWG → heterogeneity analysis
- 4. When the opportunity knocks Introduction On the effects of structural shocks Reallocation → Skill biased technical changes WW II evidence → lack of men = ↑ fem participation (Acemoglu et al. 2004, Fern´andez et al. 2004, Goldin and Olivetti 2013) The effect of transition → ↑ Gender Gaps (Brainerd 2000, Blau and Kahn 2003, Munich et al. 2005, Tyrowicz et al. 2018) Differences in risk aversion → Stablity-wage trade off
- 5. When the opportunity knocks Introduction Motivation Source Female SE R2 Easy to find job Germany_(E) Russia Japan Bangladesh Slovenia Hungary Italy France Bulgaria Portugal Germany_(W) Switzerland Spain Cyprus Czech_Republic Israel Poland Great_Britain Canada philippines Netherlands Denmark New_Zealand Norway United_States 0 .2 .4 .6 Easy to find new job Germany_(E) Bulgaria France Hungary Russia Great_Britain Job is secure
- 6. When the opportunity knocks Data and Methods Data Gender wage gaps Collection of individual level databases Several sources: ISSP, EU-SES, LFS, LMS, LSMS Detailed list Labor market flows Life in Transition Survey (LiTS) - 2006 Retrospective data on 1000 ind in all transition countries Quality checks
- 7. When the opportunity knocks Data and Methods Gender wage gap – Measurement We apply ˜Nopo (2008) non-parametric decomposition to hourly wages. Raw gap = ∆X + ∆A + ∆M + ∆W where ∆X : explained component ∆A: unexplained / adjusted component ∆M : component due to Men different than Women in the sample ∆W : component due to Women different than Men in the sample Control variables: age group, education (3 levels), marital status and urban/rural Why? Distinguish between chorts born before and after 1965 How does it look like?
- 8. When the opportunity knocks Data and Methods Labour market flows General flows Hirings = FlowN→E + FlowEi →Ej Et−1 and Separations = FlowE→N + FlowEi →Ej Et−1 , We also compute gross (sum), net (difference) and excess (gross-net) Sectoral flows Inflowi = Hiringst,i n i Hiringst,i and Outflowsj = FlowEj →N + FlowEj →E Et−1,j , Where i refers to private sector or service private and j to public SOE
- 9. When the opportunity knocks Data and Methods Labour market shocks – Measurement Follow Hausmann et al. (2005) and focus on episodes of rapid change Episode = 1 if vart > 80th percentile and vart > 1.5 ∗ vart−1 0 otherwise, How do episodes they look like? 1 Between 4 and 25 episodes (∼ 600 year×country) Examples 2 Weak correlation in hirings/separations episodes
- 10. When the opportunity knocks Data and Methods Our model Specification ∆Ai, j, t = β0 + β1episodei,t,n + θt + θi,j + εi,j,t Where i, j, t index coutry, source and times ∆A is the adjusted GWG episodei,t,n dummy for an episode in the last n years θt year FE θi,j country×source FE
- 11. When the opportunity knocks Results General flows Hiring Separation Gross Net Excess Cohorts born before 1965 Episode1 -0.01 0.04*** -0.04* 0.04* -0.01 (0.02) (0.01) (0.02) (0.02) (0.02) Episode2 0.01 0.02** -0.03 0.06*** -0.00 (0.02) (0.01) (0.02) (0.01) (0.02) Episode3 0.01 0.02 -0.00 0.06*** -0.01 (0.02) (0.01) (0.02) (0.01) (0.02) Cohorts born after 1965 Episode1 0.01 0.00 0.08*** 0.14*** 0.00 (0.02) (0.02) (0.02) (0.04) (0.05) Episode2 0.01 -0.00 0.02 0.01 0.00 (0.02) (0.02) (0.02) (0.04) (0.03) Episode3 0.00 0.01 0.03* 0.02 0.00 (0.02) (0.02) (0.02) (0.03) (0.03) Notes: Ln indicates that a country experienced an episode in the last n years. Regressions also include year and country×source FE. SD clustered at country-year level. Observations weighted by SD adjusted gap.
- 12. When the opportunity knocks Results Reallocation flows Separations Hirings SOE Manuf. Private Services Cohorts born before 1965 Episode1 0.05*** 0.03*** 0.03 0.01 (0.02) (0.01) (0.03) (0.02) Episode2 0.04*** 0.04*** -0.03 -0.00 (0.02) (0.01) (0.03) (0.01) Episode3 0.04*** 0.04*** -0.05*** -0.02 (0.02) (0.01) (0.02) (0.02) Cohorts born after 1965 Episode1 -0.02 -0.00 -0.03 -0.06 (0.02) (0.02) (0.03) (0.05) Episode2 -0.01 -0.01 -0.00 -0.03 (0.02) (0.02) (0.02) (0.02) Episode3 -0.02 -0.01 -0.01 -0.00 (0.01) (0.02) (0.01) (0.03) Notes: Ln indicates that a country experienced an episode in the last n years. Regressions also include year and country×source FE. SD clustered at country-year level. Observations weighted by SD adjusted gap.
- 13. When the opportunity knocks The end Concluding remarks Asymmetric effect of crisis based on gender and cohort Differences across cohorts: 1 Bargaining position? 2 Primary vs secondary earners? 3 Skill-mismatch? Policy implications → ALMP consider specific groups , e.g. gender/minority quotas
- 14. When the opportunity knocks The end Last frame Questions Thank you for your attention Lucas van der Velde Contact: lvandervelde@grape.org.pl More about our research on http://grape.org.pl Twitter: @GRAPE ORG
- 15. When the opportunity knocks References Acemoglu, D., Autor, D. H. and Lyle, D.: 2004, Women, war, and wages: The effect of female labor supply on the wage structure at midcentury, Journal of Political Economy 112(3), 497–551. Black, S. E. and Spitz-Oener, A.: 2010, Explaining women’s success: Technological change and the skill content of women’s work, Review of Economics and Statistics 92(1), 187–194. Blau, F. D. and Kahn, L. M.: 2003, Understanding international differences in the gender pay gap, Journal of Labor Economics 21(1). Brainerd, E.: 2000, Women in transition: Changes in gender wage differentials in Eastern Europe and the former Soviet Union, Industrial and labor relations review pp. 138–162. Card, D. and DiNardo, J. E.: 2002, Skill-biased technological change and rising wage inequality: Some problems and puzzles, Journal of Labor Economics 20(4), 733–783. Cha, Y. and Weeden, K. A.: 2014, Overwork and the slow convergence in the gender gap in wages, American Sociological Review 79(3), 457–484. Fern´andez, R., Fogli, A. and Olivetti, C.: 2004, Mothers and sons: Preference formation and female labor force dynamics, The Quarterly Journal of Economics 119(4), 1249–1299. Goldin, C.: 2014, A grand gender convergence: Its last chapter, American Economic Review 104(4), 1091–1119. Goldin, C. and Olivetti, C.: 2013, Shocking labor supply: A reassessment of the role of world war ii on women’s labor supply, The American Economic Review 103(3), 257–262.
- 16. When the opportunity knocks The end Hausmann, R., Pritchett, L. and Rodrik, D.: 2005, Growth accelerations, Journal of Economic Growth 10(4), 303–329. Juhn, C., Murphy, K. M. and Pierce, B.: 1993, Wage inequality and the rise in returns to skill, Journal of Political Economy 101(3), 410. Lemieux, T.: 2006, Increasing residual wage inequality: Composition effects, noisy data, or rising demand for skill?, American Economic Review 96(3), 461–498. Mandel, H. and Semyonov, M.: 2005, Family policies, wage structures, and gender gaps: Sources of earnings inequality in 20 countries, American Sociological Review 70(6), 949–967. Munich, D., Svejnar, J. and Terrell, K.: 2005, Is women’s human capital valued more by markets than by planners?, Journal of Comparative Economics 33(2), 278–299. ˜Nopo, H.: 2008, Matching as a tool to decompose wage gaps, The Review of Economics and Statistics 90(2), 290–299. ˜Nopo, H., Daza, N. and Ramos, J.: 2012, Gender earning gaps around the world: a study of 64 countries, International Journal of Manpower 33(5), 464–513. Stanley, T. D. and Jarrell, S. B.: 1998, Gender wage discrimination bias? A meta-regression analysis, Journal of Human Resources pp. 947–973. Tyrowicz, J., van der Velde, L. and Goraus, K.: 2018, How (not) to make women work?, Social Science Research 75, 154–167. Weichselbaumer, D. and Winter-Ebmer, R.: 2007, The effects of competition and equal treatment laws on gender wage differentials, Economic Policy 22(50), 235–287.
- 17. When the opportunity knocks The end LiTS JC p-val JD p-val Pairwise correlation 0.449 0.001 0.328 0.018 OLS - no controls 0.524 0.001 0.641 0.018 OLS - country dummies 0.345 0.003 0.580 0.074 OLS - year dummies 0.443 0.025 0.765 0.012 OLS - country and year dummies 0.226 0.153 1.057 0.005 Notes: Reproduced from Tyrowicz and van der Velde (2018). Table adapted from Table D.1 in ?. The dependent variable is the median of job creation (destruction) in the literature for each country year and the independent variable hirings (separations) from LiTS data. Back
- 18. When the opportunity knocks The end Detailed list of sources Country ISSP LFS LMS LSMS SES BGR 1992/1993, 1997/2000, 2002/2005 1995, 1997, 2001 2002, 2006 CZE 1992, 1995/1999 2002, 2006 EST 2002, 2006 HRV 2006 HUN 1990, 1992/1999, 2002/2006 2002, 2006 LTU 2002, 2006 LVA 1995/1996, 1998/2006 2002, 2006 POL 1991/1999, 2001/2004, 2006 1995/2006 2002, 2006 ROM 2002, 2006 RUS 1991/1997, 1999, 2001, 2003, 2005/2006 1994/1996, 1998, 2000/2006 SRB 1995/2002 2002/2003 SVK 1999, 2002/2004 2002, 2006 SVN 1993/2006 UKR 2003/2004 Notes: ISSP: International Social Survey Program; LFS: Labour Force Survey; LMS: Longitudinal Monitoing Survey; LSMS: Living Standards Measurement Survey, SES: Structure of Earnings Survey. Back
- 19. When the opportunity knocks The end GWG in Poland: adding controls .75.8.85.9.951 Average%matched 1996 1998 2000 2002 2004 2006 Year .05.1.15.2.25 Adjustedwagegap 1996 1998 2000 2002 2004 2006 Year Basic control + Firm characteristics + Industry + Occupation Notes: Figure shows the percentage matched with additional controls (top) and the resulting adjusted GWG (bottom) Source Polish LFS. Back
- 20. When the opportunity knocks The end GWG in two cohorts Notes: Figure shows GWG in transition economies.Left provides Raw GWG and right the Adjusted GWG. Back
- 21. When the opportunity knocks The end Episodes of fast reallocation: cohort <1965 0 1 BGR 1990 1995 2000 2005 0 1 CZE 1990 1995 2000 2005 0 1 HUN 1990 1995 2000 2005 0 1 LVA 1990 1995 2000 2005 0 1 POL 1990 1995 2000 2005 0 1 RUS 1990 1995 2000 2005 0 1 SRB 1990 1995 2000 2005 0 1 SVN 1990 1995 2000 2005 Episode type: Hirings Separations Both Back
- 22. When the opportunity knocks The end Synchronicity of flows Overall flows Transition flows Globalization flows Episodes Correlation -0.05 -0.06 -0.09* Episodes Partial correlation -0.04 -0.14*** -0.07 Levels Correlation -0.32*** 0.03 -0.12*** Levels Partial correlation 0.11** -0.03 -0.13*** Notes: Table displays correlation and partial correlation coefficients of measures of hirings and separations. Overall refers to correlation between hirings and separations; transition, to the correlation between private sector in hirings and separations from public sector; and globalization, to the correlation between services in hirings and separations from manufacturing sector. The number of observations is 308. Back