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- Actual experience is constructed from panel data for 1985â2014, while potential experience = age â years of education + 6.
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- B1 Figure B.3. Predicted AKM actual-experience FEs, by gender 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Predicted AKM actualâexperience FE 0 4 8 12 16 20 24 28 32 36 40 Actual experience in formal sector (years) Men Women Note: Figure shows predicted AKM actual-experience FEs separately for men and women based on estimating earnings equation (1). Source: RAIS, 2007â2014. Figure B.4. Predicted AKM tenure FEs, by gender 0.0 0.1 0.2 0.3 0.4 0.5 Predicted AKM tenure FE 0 4 8 12 16 20 24 28 32 36 40 Tenure in current job (years) Men Women Note: Figure shows predicted AKM tenure FEs separately for men and women based on estimating earnings equation (1).
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- B2 Figure B.5. Predicted AKM education-year FEs, by gender A. Men â0.15 â0.12 â0.09 â0.06 â0.03 0.00 Predicted AKM educationâyear FE 2007 2008 2009 2010 2011 2012 2013 2014 Year 0 years <5 years 5 years <9 years 9 years <12 years 12 years 13â15 years >=16 years B. Women â0.15 â0.12 â0.09 â0.06 â0.03 0.00 Predicted AKM educationâyear FE 2007 2008 2009 2010 2011 2012 2013 2014 Year 0 years <5 years 5 years <9 years 9 years <12 years 12 years 13â15 years >=16 years Note: Figure shows predicted AKM education-year FEs separately for men and women based on estimating earnings equation (1). Note that the declining pattern for both genders and all education categories is due to measuring earnings in multiples of the prevailing minimum wage, which increased over this periodâsee Engbom and Moser (2022) for details.
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- Density â1.0 â0.8 â0.6 â0.4 â0.2 0.0 0.2 0.4 0.6 0.8 1.0 Withinâemployer gap in AKM employer FEs (men â women) Note: Figure shows pay distributions underlying the Oaxaca-Blinder decompositionsâspecifically, the between-gap using female FEs (Panel A) and using male FEs (Panel C). as well as the within-gap using male weights (Panel B) and using female weights (Panel D) Decomposition 1 (Panels A and B) and decomposition 2 (Panels C and D) correspond to equations (B.2) and (B.3) of the main text, respectively. Dashed vertical line shows mean of the distribution. Source: RAIS, 2007â2014.
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- Figure B.2. Predicted AKM occupation FEs, by gender â0.3 â0.2 â0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Mean AKM occupation FE 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Percentiles of AKM occupation FE for men Men Women Note: Figure shows predicted AKM occupation FEs separately for men and women based on estimating earnings equation (1). Fixed effects of both genders are sorted by mean FEs of male FE quantiles. Source: RAIS, 2007â2014.
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- Grey vertical patterned lines represent mean values for workers of a given gender. Source: Model estimates based on RAIS, 2007â2014.
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- Grey vertical patterned lines represent mean values for workers of a given gender. Source: Model estimates based on RAIS, 2007â2014.
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- Poaching Ranks. The poaching rank (Bagger and Lentz, 2019) of every firm j is defined as Poaching rankj = number of E-to-E hiresj number of all hiresj (D.33) = number of E-to-E hiresj number of E-to-E hiresj + number of U-to-E hiresj , (D.34) which in our model can be rewritten as Poaching rankj = λEGj + λG(1 â u) λEGj + λG + λUu (D.35) The poaching rank in equation (D.35) is a monotonic transformation of the utility rank of a firm because it is strictly increasing in the cumulative employment distribution Gj, which is precisely the employment-weighted rank of firm j in the pool of all firms.49 This proves that, in our model, the Poaching rank is monotonically increasing in firm rank.
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- Solid line represents the 45-degree line, for which actual experience equals potential experience. Source: RAIS, 2007â2014.
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- Source: RAIS, 2007â2014. A5 Table A.5. Comparison of summary statistics (connected vs. selection )
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- Source: RAIS, 2007â2014. To illustrate these two decompositions, Figure B.7 shows the distributions of gender-specific employer FEs underlying the individual terms in equations (B.2) and (B.3). B4 Figure B.7. Components of Oaxaca-Blinder decompositions A. Decomposition 1: Between-gap,female FEs 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Density â1.0 â0.8 â0.6 â0.4 â0.2 0.0 0.2 0.4 0.6 0.8 1.0 Genderâspecific AKM employer FE Men Women B. Decomposition 1: Within-gap, male weights 0 1 2 3 4 5 Density â1.0 â0.8 â0.6 â0.4 â0.2 0.0 0.2 0.4 0.6 0.8 1.0 Withinâemployer gap in AKM employer FEs (men â women) C. Decomposition 2: Between-gap, male FEs 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Density â1.0 â0.8 â0.6 â0.4 â0.2 0.0 0.2 0.4 0.6 0.8 1.0 Genderâspecific AKM employer FE Men Women D. Decomposition 2: Within-gap, female weights 0 1 2 3 4 5
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- Xiao, Pengpeng, âEquilibrium Sorting and the Gender Wage Gap,â Working Paper, 2023. Online AppendixâNot for Publication A Data Description Appendix A.1 Comparison of Actual versus Potential Experience Figure A.1. Percentiles of actual experience conditional on potential experience A. Men 0 5 10 15 20 25 30 35 40 45 50 Actual experience (years) 0 5 10 15 20 25 30 35 40 45 50 Potential experience (years) P5 P10 P25 P50 P75 P90 P95 B. Women 0 5 10 15 20 25 30 35 40 45 50 Actual experience (years) 0 5 10 15 20 25 30 35 40 45 50 Potential experience (years) P5 P10 P25 P50 P75 P90 P95 Note: Figure shows percentiles of actual against potential experience separately for men (Panel A) and women (Panel B).
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