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The gender wage gap: evidence from South Korea Cover

The gender wage gap: evidence from South Korea

IZA Journal of Labor Economics
Volume 11 (2022): Issue 1 (January 2022)
By: Kyeongah Lee  
Open Access
|Oct 2022

Figures & Tables

Figure 1

Gender Wage Gap in OECD Countries, 2018.Source: OECD Database.Notes: This figure shows the 2018 or latest gender wage gaps in OECD countries. The gender wage gap is defined as the difference between median earnings of full-time male and female workers. OECD, Organisation for Economic Co-operation and Development.

Figure 2

Labor Force Participation Rate (%).Source: OECD.Stat.Notes: Sample includes male and female workers aged 25–54 from 1998 to 2020. OECD, Organisation for Economic Co-operation and Development.

Figure 3

Female/Male Log Monthly Wage Ratios, Full-Time Workers.Source: KLIPS.Notes: Sample includes full-time male and female workers aged 20–59 years. They are non-farm, nonmilitary, and salaried workers. Each bar indicates exp(A), where A is the female mean log monthly wage, 10th, 50th, or 90th percentile log monthly wage minus the corresponding male log monthly wage for the year. KLIPS, Korean Labor and Income Panel Survey.

Figure 4

Gender Log Monthly Wage Gaps by Age Cohort, 1998 and 2020.Source: KLIPS.Notes: Each bar indicates exp(A), where A is the female mean log monthly wage minus the corresponding male log monthly wage for each age group and year. KLIPS, Korean Labor and Income Panel Survey.

Figure 5

Female Labor Force Participation Rate by Age Cohort (%).Source: OECD.Stat.Notes: Sample data indicate female labor force participation rates by age cohort 20–59 for South Korea, Japan, Italy, and OECD in 1998, 2005, 2015, and 2020. OECD, Organisation for Economic Co-operation and Development.

Figure 6

Male and Female Labor Force Participation Rates by Age Cohort (%).Source: KOSIS.Notes: Sample data indicate male and female labor force participation rates by age cohort 20–59 for South Korea in 1998 and 2020. KOSIS, Korean Statistical Information Service.

Figure 7

Quantile Decomposition of the Gender Wage Gap.Source: KLIPS.Notes: Sample includes full-time male and female workers aged 20–59 in 1998 and 2020. They are nonfarm, nonmilitary, and salaried workers. For (A) and (B), the red dashed lines plot the explained wage gap, and the green dotted lines show the unexplained wage gap. KLIPS, Korean Labor and Income Panel Survey.

Figure 8

Impacts of Marriage.Source: KLIPS.Notes: Sample includes males and females who got married at any time after 2000. The marriage effect for each panel shows the percentage difference of the labor market outcome for females than for males and is estimated at event time 2 from Eq. (10). The results for monthly wages, hourly wage, and hours of work are estimated conditional on employment. The result for the labor supply is estimated unconditional on employment status. Thin dashed lines indicate 95% confidence intervals (CIs) based on robust standard errors. KLIPS, Korean Labor and Income Panel Survey.

Figure 9

Impacts of Children.Source: KLIPS.Notes: Sample includes males and females who have their first childbirth at any time after 2000. The child penalty for each panel shows the percentage difference of the labor market outcome for females compared to males and is estimated at event time 10 from Eq. (10). The results for monthly wages, hourly wages, and hours of work are estimated conditional on employment. The result for the labor supply is estimated unconditional on employment status. Thin dashed lines indicate 95% CIs based on robust standard errors. CIs, confidence intervals; KLIPS, Korean Labor and Income Panel Survey.

Figure A1

Impacts of Children in the Long Run.Source: KLIPS.Notes: This figure was created in the same way as Figure 9, but expands the event time to include 15 years after first childbirth. The child penalty for each panel shows the percentage difference of the labor market outcome for females compared to males and is estimated at event time 15 from Eq. (10). The results for the monthly wages, hourly wages, and hours of work are estimated conditional on employment. The result for the labor supply is estimated unconditional on employment status. Thin dashed lines indicate 95% CIs based on robust standard errors. CIs, confidence intervals; KLIPS, Korean Labor and Income Panel Survey.

Figure A2

Impact of Children Below the 50th Percentile of the Wage Distribution.Source: KLIPS.Notes: This figure was created in the same way as Figure 9, but focuses only on those males and females whose monthly wages are below the 50th percentile of the wage distribution 1 year before marriage (t = −1). The child penalty for each panel shows the percentage difference of the labor market outcome for females compared to males and is estimated at event time 10 from Eq. (10). The results for the monthly wages, hourly wages, and hours of work are estimated conditional on employment. The result for the labor supply is estimated unconditional on employment status. Thin dashed lines indicate 95% CIs based on robust standard errors. CIs, confidence intervals; KLIPS, Korean Labor and Income Panel Survey.

Figure A3

Impact of Children Above the 50th Percentile of the Wage Distribution.Source: KLIPS.Notes: This figure was created in the same way as Figure 9, but focuses only on those males and females whose monthly wages are above the 50th percentile of the wage distribution 1 year before marriage (t = −1). The child penalty for each panel shows the percentage difference of the labor market outcome for females compared to males and is estimated at event time 10 from Eq. (10). The results for the monthly wages, hourly wages, and hours of work are estimated conditional on employment. The result for the labor supply is estimated unconditional on employment status. Thin dashed lines indicate 95% CIs based on robust standard errors. CIs, confidence intervals; KLIPS, Korean Labor and Income Panel Survey.

Figure A4

Impact of Children in Professional Occupations.Source: KLIPS.Notes: This figure was created in the same way as Figure 9, but focuses only on those males and females who work in professional occupations. Professional jobs include science professionals, computer-related professionals, engineering science professionals, health and medical professionals, teaching professionals, administration, business, and finance professionals, legal, social welfare, and religion professionals, and culture, arts, and broadcasting professionals in the 5th KSCO. The child penalty for each panel shows the percentage difference of the labor market outcome for females compared to males and is estimated at event time 10 from Eq. (10). The results for the monthly wages, hourly wages, and hours of work are estimated conditional on employment. The result for the labor supply is estimated unconditional on employment status. Thin dashed lines indicate 95% CIs based on robust standard errors. CIs, confidence intervals; KLIPS, Korean Labor and Income Panel Survey.

Decomposition of the gender wage gaps

Variables19982020
Log pointsPercent of gender gap explainedLog pointsPercent of gender gap explained
Age0.121629.080.01715.06
Education0.01623.880.00431.28
Tenure0.071717.150.03269.66
Hours of work−0.0148−3.540.01354.01
Unionization−0.0001−0.020.00010.02
Business size0.01874.470.03028.96
Industry−0.0220−5.270.01905.61
Occupation0.01092.60−0.0054−1.60
Total explained0.202148.340.111433.00
Total unexplained0.216051.660.226267.00
Total wage gap0.4181100.000.3377100.00

Average effects of marriage on labor market outcomes

Log Monthly Wage(1)Log Hourly Wage(2)Log Hours of Work(3)Labor Supply(4)
Female−0.151*** (0.006)−0.158*** (0.007)−0.046*** (0.004)0.062*** (0.005)
Married0.141*** (0.011)0.173*** (0.012)0.013* (0.007)0.436*** (0.015)
Female × Married−0.089*** (0.017)−0.117*** (0.018)−0.006 (0.010)−0.463*** (0.019)
Constant2.409*** (0.225)1.597*** (0.235)5.283*** (0.122)0.431*** (0.015)
Observations15,06813,74213,74441,670
R20.4300.5210.1960.024

Share of full-time male and female workers by age cohort (%)

Age cohort19982020
MaleFemaleMaleFemale
20–2949.2850.7242.5857.42
30–3974.3425.6662.9137.09
40–4965.9034.1065.9034.10
50–5970.1829.8259.6740.33

Share of marital status for full-time workers by age cohort (%)

Age cohort19982020
MaleFemaleMaleFemale
20–2925.0713.5910.6510.29
30–3984.5984.3860.8762.12
40–4996.2382.2981.6480.65
50–5995.0073.5383.9675.78

Share of full-time male and female workers at wage distribution percentile (%)

Percentile19982020
MaleFemaleMaleFemale
Below 10th percentile26.7273.2831.6568.35
Below 50th percentile46.8653.1439.6360.37
Above 90th percentile87.2212.7889.3210.68

Log monthly wage and share of males and females by size of establishment

No. of workers19982020
MaleFemaleMaleFemale
Log monthly wageShare of full-time male workers (%)Log monthly wageShare of full-time female workers (%)Log monthly wageShare of full-time male workers (%)Log monthly wageShare of full-time female workers (%)
1–47.17 (0.40)49.626.77 (0.42)50.837.59 (0.41)45.287.48 (0.31)54.72
5–97.29 (0.34)56.826.90 (0.37)43.187.75 (0.43)49.527.57 (0.26)50.48
10–297.38 (0.42)62.196.85 (0.33)37.817.95 (0.31)57.197.58 (0.27)42.81
30–497.35 (0.45)70.396.89 (0.33)29.617.97 (0.40)66.667.68 (0.33)33.34
50–697.39 (0.45)70.096.95 (0.52)29.918.03 (0.35)53.257.70 (0.30)46.75
70–997.40 (0.42)72.866.86 (0.27)27.147.91 (0.39)55.687.80 (0.36)44.32
100–2997.39 (0.51)74.077.03 (0.46)25.938.11 (0.33)47.657.78 (0.30)52.35
300–4997.54 (0.43)66.276.98 (0.30)33.738.15 (0.28)58.167.78 (0.18)41.84
500–9997.50 (0.38)67.557.19 (0.37)32.458.32 (0.43)65.167.72 (0.29)34.84
over 1,0007.65 (0.41)69.827.12 (0.56)30.188.33 (0.43)65.037.90 (0.50)34.97

Share of full-time male and female workers in professional jobs (%)

19982020
MaleFemaleMaleFemale
Professional jobs55.8344.1750.6749.33
Male professional jobs62.4137.5958.8141.19

Descriptive statistics

Korean Labor and Income Panel Study
19982020
MaleFemaleMaleFemale
Age36.52 (8.73)33.56 (10.03)42.06 (10.65)40.39 (11.40)
Education
Bachelor's degree0.24 (0.43)0.16 (0.37)0.40 (0.49)0.34 (0.48)
Advanced degree0.04 (0.21)0.01 (0.11)0.06 (0.23)0.05 (0.21)
Hours worked per month225.70 (55.39)214.15 (56.56)184.23 (33.74)177.58 (25.61)
Tenure6.41 (6.64)4.14 (5.01)9.09 (8.66)7.16 (6.86)
Union0.63 (0.48)0.59 (0.49)0.13 (0.34)0.11 (0.31)
Professional job0.12 (0.33)0.18 (0.38)0.21 (0.40)0.24 (0.43)
Married0.75 (0.44)0.52 (0.50)0.56 (0.50)0.50 (0.50)
Children0.72 (0.45)0.55 (0.50)0.54 (0.50)0.55 (0.50)
Log real monthly wages7.43 (0.46)6.97 (0.48)7.99 (0.45)7.68 (0.37)
No. of observations1,5598742,0361,354

Average effects of children on labor market outcomes

Log monthly wage(1)Log hourly wage(2)Log hours of work(3)Labor supply(4)
Female−0.180*** (0.006)−0.191*** (0.006)−0.046*** (0.003)0.029*** (0.004)
Children0.160*** (0.005)0.172*** (0.006)0.016*** (0.003)0.374*** (0.004)
Female × Children−0.238*** (0.007)−0.238*** (0.008)−0.041*** (0.004)−0.490*** (0.006)
Constant2.154*** (0.116)1.544*** (0.122)5.448*** (0.062)0.420*** (0.007)
Observations39,82135,80235,807112,205
R20.5980.6470.1740.131

Share of marital status for full-time workers at wage distribution percentile (%)

Percentile19982020
MaleFemaleMaleFemale
Below 10th percentile54.5564.6436.8167.05
Below 50th percentile57.8448.8544.7060.65
Above 90th percentile95.2688.2494.5784.42
DOI: https://doi.org/10.2478/izajole-2022-0005 | Journal eISSN: 2193-8997
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Language: English
Accepted on: Sep 6, 2022
|
Published on: Oct 23, 2022
Published by: Sciendo
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
unexplained gender wage gap,
glass ceiling,
impacts of marriage and children
Related subjects:
Business and economics,
Political economics,
Microeconomics,
Macroeconomics,
Economic policy,
Mathematics and statistics for economists,
Econometrics

© 2022 Kyeongah Lee, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 License.

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