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Gender wage gap across the distribution: What is the role of within- and between-firm effects? Cover

Gender wage gap across the distribution: What is the role of within- and between-firm effects?

IZA Journal of Development and Migration
Volume 12 (2021): Issue 1 (January 2021)
By: Ezgi Kaya  
Open Access
|Oct 2021

Figures & Tables

Figure 1

Kernel density estimates of the wage distributions, by year and sex.Data source: Author's calculations based on the SES 2006 and 2014.Notes: The density functions are estimated using the Epanechnikov Kernel estimator. SES, Structure of Earnings Survey.
Kernel density estimates of the wage distributions, by year and sex.Data source: Author's calculations based on the SES 2006 and 2014.Notes: The density functions are estimated using the Epanechnikov Kernel estimator. SES, Structure of Earnings Survey.

Figure 2

Observed GWG, 2006 and 2014.Data source: Author's calculations based on the SES 2006 and 2014.Notes: The observed GWG is the difference between the log hourly wages of men and women. The graph is created using lowess smoothing, with a bandwidth of 0.25. GWG, gender wage gap; SES, Structure of Earnings Survey.
Observed GWG, 2006 and 2014.Data source: Author's calculations based on the SES 2006 and 2014.Notes: The observed GWG is the difference between the log hourly wages of men and women. The graph is created using lowess smoothing, with a bandwidth of 0.25. GWG, gender wage gap; SES, Structure of Earnings Survey.

Figure 3

Adjusted GWG at the mean and selected percentiles of the wage distribution.Data source: Author's calculations based on the SES 2006 and 2014.Notes: (i) Sample is restricted to individuals of working age (between 15 years old and 64 years old), excluding paid stagers and apprentices, who are working in establishments that employ both males and females. The data are not weighted. (ii) Each coefficient estimate and associated 95% CI come from a separate regression. Specifications (2a)–(2d) are as set out in the text. Point estimates are provided in Table 3. CI, confidence interval; GWG, gender wage gap; SES, Structure of Earnings Survey.
Adjusted GWG at the mean and selected percentiles of the wage distribution.Data source: Author's calculations based on the SES 2006 and 2014.Notes: (i) Sample is restricted to individuals of working age (between 15 years old and 64 years old), excluding paid stagers and apprentices, who are working in establishments that employ both males and females. The data are not weighted. (ii) Each coefficient estimate and associated 95% CI come from a separate regression. Specifications (2a)–(2d) are as set out in the text. Point estimates are provided in Table 3. CI, confidence interval; GWG, gender wage gap; SES, Structure of Earnings Survey.

Figure 4

Decomposition of the GWG at selected percentiles of the wage distribution, 2006.Data source: Author's calculations based on the SES 2006.Notes: (i) Specifications (2a)–(2d) are as set out in the text. (ii) RIF-BO method is used to decompose the GWG using male coefficients as the baseline. (iii) “Explained gap” is that part of the raw GWG that is due to gender differences in productivity-related characteristics included in the specification. “Unexplained gap” is that part of the raw GWG that remains unexplained after controlling for the productivity-related characteristics. BO, Blinder-Oaxaca; GWG, gender wage gap; RIF, Recentered Influence Function; RIF-BO, BO decomposition that relies on the RIF regression; SES, Structure of Earnings Survey.
Decomposition of the GWG at selected percentiles of the wage distribution, 2006.Data source: Author's calculations based on the SES 2006.Notes: (i) Specifications (2a)–(2d) are as set out in the text. (ii) RIF-BO method is used to decompose the GWG using male coefficients as the baseline. (iii) “Explained gap” is that part of the raw GWG that is due to gender differences in productivity-related characteristics included in the specification. “Unexplained gap” is that part of the raw GWG that remains unexplained after controlling for the productivity-related characteristics. BO, Blinder-Oaxaca; GWG, gender wage gap; RIF, Recentered Influence Function; RIF-BO, BO decomposition that relies on the RIF regression; SES, Structure of Earnings Survey.

Figure 5

Decomposition of the GWG at selected percentiles of the wage distribution, 2014.Data source: Author's calculations based on the SES 2014.Notes: (i) Specifications (2a)–(2d) are as set out in the text. (ii) RIF-BO method is used to decompose the GWG using male coefficients as the baseline. (iii) “Explained gap” is that part of the raw GWG that is due to gender differences in productivity-related characteristics included in the specification. “Unexplained gap” is that part of the raw GWG that remains unexplained after controlling for the productivity-related characteristics. BO, Blinder-Oaxaca; GWG, gender wage gap; RIF, Recentered Influence Function; RIF-BO, BO decomposition that relies on the RIF regression; SES, Structure of Earnings Survey.
Decomposition of the GWG at selected percentiles of the wage distribution, 2014.Data source: Author's calculations based on the SES 2014.Notes: (i) Specifications (2a)–(2d) are as set out in the text. (ii) RIF-BO method is used to decompose the GWG using male coefficients as the baseline. (iii) “Explained gap” is that part of the raw GWG that is due to gender differences in productivity-related characteristics included in the specification. “Unexplained gap” is that part of the raw GWG that remains unexplained after controlling for the productivity-related characteristics. BO, Blinder-Oaxaca; GWG, gender wage gap; RIF, Recentered Influence Function; RIF-BO, BO decomposition that relies on the RIF regression; SES, Structure of Earnings Survey.

Figure 6

Unexplained gap at selected percentiles of the wage distribution, 2006 and 2014.Data source: Author's calculations based on the SES 2006 and 2014.Notes: (i) Specifications (2a)–(2d) are as set out in the text. (ii) Each point estimate and associated 95% CI are estimated using the RIF-BO method to decompose the GWG using male coefficients as the baseline. (iii) “Unexplained gap” is that part of the observed GWG that remains unexplained after controlling for the productivity-related characteristics included in each specification. BO, Blinder-Oaxaca; CI, confidence interval; GWG, gender wage gap; RIF, Recentered Influence Function; RIF-BO, BO decomposition that relies on the RIF regression; SES, Structure of Earnings Survey.
Unexplained gap at selected percentiles of the wage distribution, 2006 and 2014.Data source: Author's calculations based on the SES 2006 and 2014.Notes: (i) Specifications (2a)–(2d) are as set out in the text. (ii) Each point estimate and associated 95% CI are estimated using the RIF-BO method to decompose the GWG using male coefficients as the baseline. (iii) “Unexplained gap” is that part of the observed GWG that remains unexplained after controlling for the productivity-related characteristics included in each specification. BO, Blinder-Oaxaca; CI, confidence interval; GWG, gender wage gap; RIF, Recentered Influence Function; RIF-BO, BO decomposition that relies on the RIF regression; SES, Structure of Earnings Survey.

Figure B1

Sensitivity analysis, MM decomposition of the GWG across the distribution, 2006.Data source: Author's calculations based on the SES 2006.Notes: (i) Specifications (2a)–(2d) are as set out in the text. (ii) MM decomposition method is used to decompose the GWG using male coefficients as the baseline. (iii) “Estimated GWG” is the estimated raw GWG using the conditional quantile regression model and integrating over the set of covariates. “Explained gap” is that part of the observed gap that is due to gender differences in productivity-related characteristics included in the specification. “Unexplained gap” is that part of the observed gap that remains unexplained after controlling for the productivity-related characteristics. GWG, gender wage gap; MM, Machado-Mata; SES, Structure of Earnings Survey.
Sensitivity analysis, MM decomposition of the GWG across the distribution, 2006.Data source: Author's calculations based on the SES 2006.Notes: (i) Specifications (2a)–(2d) are as set out in the text. (ii) MM decomposition method is used to decompose the GWG using male coefficients as the baseline. (iii) “Estimated GWG” is the estimated raw GWG using the conditional quantile regression model and integrating over the set of covariates. “Explained gap” is that part of the observed gap that is due to gender differences in productivity-related characteristics included in the specification. “Unexplained gap” is that part of the observed gap that remains unexplained after controlling for the productivity-related characteristics. GWG, gender wage gap; MM, Machado-Mata; SES, Structure of Earnings Survey.

Figure B2

Sensitivity analysis, MM decomposition of the GWG across the distribution, 2014.Data source: Author's calculations based on the SES 2014.Notes: (i) Specifications (2a)–(2d) are as set out in the text. (ii) MM decomposition method is used to decompose the GWG using male coefficients as the baseline. (iii) “Estimated GWG” is the estimated raw GWG using the conditional quantile regression model and integrating over the set of covariates. “Explained gap” is that part of the observed gap that is due to gender differences in productivity-related characteristics included in the specification. “Unexplained gap” is that part of the observed gap that remains unexplained after controlling for the productivity-related characteristics. GWG, gender wage gap; MM, Machado-Mata; SES, Structure of Earnings Survey.
Sensitivity analysis, MM decomposition of the GWG across the distribution, 2014.Data source: Author's calculations based on the SES 2014.Notes: (i) Specifications (2a)–(2d) are as set out in the text. (ii) MM decomposition method is used to decompose the GWG using male coefficients as the baseline. (iii) “Estimated GWG” is the estimated raw GWG using the conditional quantile regression model and integrating over the set of covariates. “Explained gap” is that part of the observed gap that is due to gender differences in productivity-related characteristics included in the specification. “Unexplained gap” is that part of the observed gap that remains unexplained after controlling for the productivity-related characteristics. GWG, gender wage gap; MM, Machado-Mata; SES, Structure of Earnings Survey.

Raw and adjusted GWG at selected percentiles of the wage distribution

Year and specificationRIF-OLS regressions (percentile of the wage distribution)OLS
5th10th25th50th75th90th95thMean
2006
(1) Raw GWG0.001 (0.001)−0.000 (0.001)−0.000 (0.001)0.020*** (0.005)0.056*** (0.016)0.029*** (0.016)−0.009 (0.021)0.024*** (0.007)
(2) Adjusted GWG
a. Personal characteristics0.001 (0.001)0.001 (0.001)0.001 (0.001)0.042*** (0.004)0.107*** (0.012)0.077*** (0.012)0.071*** (0.017)0.051*** (0.005)
b. Personal and work-related characteristics0.001 (0.001)0.001 (0.001)0.001 (0.001)0.037*** (0.004)0.100*** (0.010)0.072*** (0.012)0.077*** (0.017)0.048*** (0.004)
c. Personal, work-related, observed firm and observed establishment characteristics0.000 (0.001)0.001 (0.001)0.001 (0.001)0.040*** (0.004)0.089*** (0.009)0.070*** (0.011)0.102*** (0.016)0.048*** (0.004)
d. Personal and work-related characteristics and establishment fixed effects0.001* (0.000)0.001*** (0.000)0.001*** (0.000)0.026*** (0.002)0.082*** (0.006)0.111*** (0.008)0.179*** (0.012)0.051*** (0.002)
2014
(1) Raw GWG0.132*** (0.031)0.011*** (0.002)0.011*** (0.002)0.138*** (0.010)0.097*** (0.015)0.046* (0.020)0.084*** (0.021)0.084*** (0.009)
(2) Adjusted GWG
a. Personal characteristics0.114*** (0.030)0.011*** (0.002)0.011*** (0.002)0.175*** (0.008)0.172*** (0.011)0.143*** (0.016)0.160*** (0.019)0.122*** (0.007)
b. Personal and work-related characteristics0.075* (0.029)0.009*** (0.002)0.009*** (0.002)0.155*** (0.007)0.154*** (0.010)0.138*** (0.016)0.162*** (0.019)0.108*** (0.006)
c. Personal, work-related, observed firm and observed establishment characteristics0.114*** (0.030)0.011*** (0.002)0.012*** (0.002)0.146*** (0.007)0.143*** (0.010)0.124*** (0.015)0.158*** (0.018)0.106*** (0.006)
d. Personal and work-related characteristics and establishment fixed effects0.038 (0.023)0.006*** (0.001)0.006*** (0.001)0.113*** (0.004)0.132*** (0.007)0.170*** (0.011)0.222*** (0.015)0.095*** (0.004)

Number of employees to be sampled in establishments (SES second stage of sampling) by size band of the establishment

Establishment size bandNumber of employees to be sampled
1–19All
20–4920
50–9925
100–29935
250–49940
500–99950
1,000–2,49975
2,500–4,999125
5,000 and over150

List of selected gender equality policies, Turkey 2000–2018

YearPolicyDescription
2001Amendment to the Constitution (Article 41)Added “based on the equality between spouses” to the previous version of the article, which stated that “Family is the foundation of the Turkish society”.
2002New Civil Code (Law no: 4721)Increased the legal marriage age to 18 for men (from 17) and women (from 15); established the equality of men and women in the family, including abolishment of the term “head of household” and of needing the permission of spouses to work; introduced the legal basis for the sharing of marital assets in case of divorce; granted the same heredity rights to children born out of wedlock as to those of legitimate birth.
2003New Labor Law (Law no: 4857)Set the legal basis for equal pay for equal work; reinforced the equal treatment principle for women and men; decreased the restrictions on temporary employment; provided a legal basis for atypical employment; lifted the ban on employment of women in night shifts of manufacturing establishments; extended paid maternity leave from 12 weeks to 16 weeks (8 weeks before and 8 weeks after childbirth); introduced breastfeeding leave to a total of one and a half hours for mothers of children below the age of one; obliged companies employing 100–150 female workers to provide comprehensive nursery rooms for children under the age of one and companies employing >150 female workers to provide comprehensive childcare centers and pre-school facilities for children under the age of six.
2003Family Court Law (Law no: 4787)Established Family Courts in districts with >100,000 population to enforce the Civil Code and ensure gender equality.
2004Amendments to the Constitution (Articles 10 and 90)Amendments to Article 10 reinforced equal rights of women and men and emphasized the responsibility of the State to ensure equality. Amendments to Article 90 ensured the supremacy of international laws (including the Convention on the Elimination of All Forms of Discrimination against Women in 1985 and the European Social Charter in 1996) in the case of a conflict between Turkish law and international treaties with regards to human rights.
2005New Penal Code (Law no: 5237)Defined sexual harassment in work; reinforced prohibition of gender-based discrimination.
2006Social Insurance and General Health Insurance Act (Law no: 5510)Merged different security systems into one structure; adopted harmonized legislation for maternity and breastfeeding leaves and for the pension plans regulating the working conditions for women.
2008Employment Package (Law no: 5763)Introduced a direct employment subsidy program (with a limit that corresponds to the minimum wage level) with a one-year participation period (extended to June 30, 2010, with Law 5838) by reducing the employers’ contribution to social security payment of new hires for 5 years, covering 100% of social security payments of new employees for the first year and reducing its participation by 20% each year for the following 4 years. Targeted young men (ages between 18 and 29) and women >18 who were not employed as a tax-registered workers in the preceding 6 months.
2010Amendment to the Constitution (Article 10)Added “Measures taken for this purpose shall not be interpreted as contrary to the principle” to Article 10.
2011Amendments to the Labor Law (Law No. 6111)Enforced the rules of maternity leave for women (mandatory for 16 weeks and fully paid by the social security system); introduced the right to use paid leaves for breastfeeding; ruled out terminating job contracts on grounds of pregnancy; brought new regulations for part-time work; incorporated domestic help workers into the social security system; extended maternity leave to 12 months for civil servants and 6 months for others on an unpaid basis; granted 10 days’ voluntary paid paternity leave to civil servants whose wives give birth. Extended the period (for the period between March 2011 and December 2015) and coverage (both men and women of all ages and relaxing the subsidy limit covering the total social security payment amount regardless of the wage level) of the 2008 employment subsidy program.
2016Amendments to the Labor Law (Law no: 6663)Introduced unpaid maternity leave to mothers as half of their weekly working hours for 60 days for the first birth, 120 days for the second birth, and 160 days for following births; introduced the right to request part time work for the period between when maternity leave ends until the beginning of the month after the child's compulsory education begins for both parents whose spouse is also working; extended the coverage of maternity leave and the right to request part-time work to couples or individuals adopting a child under 3 years’ old.

Decomposition of the GWG at the mean

Specification
(2a)(2b)(2c)(2d)
2006
Raw GWG0.024*** (0.006)0.024*** (0.006)0.024*** (0.006)0.024*** (0.006)
Explained gap−0.023*** (0.004)−0.025*** (0.004)−0.024*** (0.005)−0.016*** (0.001)
[−97.2%][−104.8%][−103.1%][−65.9%]
Unexplained gap0.047*** (0.005)0.049*** (0.004)0.048*** (0.004)0.039*** (0.001)
[197.2%][204.8%][203.1%][165.9%]
2014
Raw GWG0.084*** (0.008)0.084*** (0.008)0.084*** (0.008)0.084*** (0.008)
Explained gap−0.035*** (0.005)−0.024*** (0.005)−0.019*** (0.006)−0.008*** (0.002)
[−41.9%][−29.1%][−22.8%][−9.0%]
Unexplained gap0.119*** (0.007)0.108*** (0.006)0.103*** (0.006)0.092*** (0.003)
[141.9%][129.1%][122.8%][109.0%]

Sample means within establishments

20062014
AllMalesFemalesAllMalesFemales
Personal characteristics
Male0.71––0.67––
Educational attainment
  Primary school and below0.260.280.170.160.160.15
  Primary education and secondary school0.150.160.100.140.150.10
  High school0.270.260.310.320.320.33
  Vocational high school0.090.090.100.070.080.06
  Higher education0.230.220.320.310.290.37
Potential labor market experience (years)16.6817.6614.2517.0917.7415.76
Work-related characteristics
Tenure (years)3.203.292.932.862.972.61
Full-time0.990.990.990.980.980.98
Permanent contract0.970.970.970.940.940.95
Overtime work0.110.110.80.150.160.12
Occupation distribution
Legislators, senior officials, and managers0.070.080.060.060.080.05
Professionals0.080.070.120.100.100.13
Technicians and associate professionals0.180.170.200.090.090.08
Clerks0.140.010.280.170.130.28
Service, shop and market sales’ workers0.130.140.110.260.250.26
Skilled agricultural and fishery workers0.000.000.000.000.000.00
Craft and related trade workers0.160.170.080.110.120.03
Plant and machine operators and assemblers0.110.130.040.090.110.04
Elementary occupations0.130.130.120.120.110.13
Average number of employees per establishment1914517125

Components of the BO decomposition of the mean GWG, 2006

Specification
(2a)(2b)(2c)(2d)
Raw GWG0.024*** (0.006)0.024*** (0.006)0.024*** (0.006)0.024*** (0.006)
Explained gap−0.023*** (0.003)−0.025*** (0.004)−0.024*** (0.005)−0.016*** (0.001)
[−97.2][−104.8%][−103.1%][−65.9%]
Unexplained gap0.047*** (0.005)0.049*** (0.004)0.048*** (0.004)0.039*** (0.001)
Explained[197.2][204.8%][203.1%][165.9%]
Personal characteristics
Education−0.138*** (0.004)−0.119*** (0.003)−0.106*** (0.003)−0.067*** (0.001)
[−580.0%][−503.1%][−447.8%][−282.4%]
Potential labour market experience0.115*** (0.003)0.054*** (0.002)0.054*** (0.002)0.046*** (0.001)
[482.8%][225.8%][227.7%][192.1%]
Work-related characteristics
Tenure 0.038*** (0.002)0.024*** (0.002)0.016*** (0.000)
[160.6%][101.1%][68.7%]
Permanent contract 0.000 (0.000)−0.000 (0.000)−0.000*** (0.000)
[0.6%][−0.8%][−0.8%]
Full-time −0.005*** (0.001)−0.006*** (0.001)−0.006*** (0.000)
[−20.3%][−23.8%][−24.5%]
Overtime work 0.007*** (0.001)0.003*** (0.000)−0.005*** (0.000)
[31.4%][12.7%][−19.1%]
Observed firm and establishment characteristics
Firm size 0.001 (0.001)
[3.9%]
Collective agreement coverage 0.012*** (0.001)
[49.3%]
Industry of the establishment −0.006* (0.002)
Unexplained [−25.5%]
Characteristics0.006 (0.011)−0.010 (0.051)−0.031 (0.051)0.056** (0.022)
[26.3%][−42.8%][−132.1%][237.8%]
Constant0.041*** (0.010)0.059 (0.051)0.080 (0.052)−0.017 (0.022)
[170.9%][247.6%][335.2%][−71.9%]

Raw and adjusted GWG at the mean

SpecificationRaw GWGAdjusted GWG
(1)(2a)(2b)(2c)(2d)
20060.024*** (0.007)0.051*** (0.005)0.048*** (0.004)0.048*** (0.004)0.051*** (0.002)
20140.084*** (0.009)0.122*** (0.007)0.108*** (0.006)0.106*** (0.006)0.095*** (0.004)
Personal characteristicsNoYesYesYesYes
Work-related characteristicsNoNoYesYesYes
Observed firm and establishment characteristicsNoNoNoYesNo
Establishment fixed effectsNoNoNoNoYes

Sample means for establishments

20062014
Firm and establishment characteristics
Firm size
  10–49 employees0.570.41
  50–249 employees0.200.16
  250–499 employees0.080.13
  500–999 employees0.060.13
  1000+ employees0.100.16
Collective agreement coverage of the establishment4.740.05
Industry of the establishment
  Mining and quarrying0.010.01
  Manufacturing0.350.24
  Electricity, gas, and water supply0.020.02
  Construction0.050.05
  Wholesale and retail trade0.240.31
  Hotels and restaurants0.050.06
  Transport, storage, and communication0.080.05
  Financial intermediation0.060.05
  Real estate, renting and business activities0.060.11
  Education0.040.03
  Health and social work0.020.03
  Other social and personal service activities0.020.04
Number of establishments12,8747,867

SES sample coverage by year

YearSESaTotal number of employeesb(1)HLFScTotal employment(2)HLFSTotal number of employees (formal and informal)(3)HLFSTotal number of employees (formal)(4)(3)/(2)(4)/(3)(1)/(4)
20065,986,24720,423,00012,028,0008,242,0000.590.690.73
20149,651,01725,932,00017,125,00013,777,0000.660.800.70

Sample means across all establishments

20062014
AllMalesFemalesAllMalesFemales
Personal characteristics
Educational attainment
  Primary school and below0.270.310.190.170.190.15
  Primary education and secondary school0.150.160.110.150.180.11
  High school0.240.230.280.280.270.29
  Vocational high school0.100.110.090.080.100.06
  Higher education0.230.190.330.310.270.40
Potential labor market experience (years)17.0918.2314.0117.4018.3015.32
Work-related characteristics
Tenure (years)3.673.923.033.183.392.70
Full-time0.990.990.990.980.980.98
Permanent contract0.960.960.970.930.940.93
Overtime work0.140.150.110.200.220.15
Firm and establishment characteristics
  Firm size (number of employees)
    10–490.460.460.460.320.320.32
    50–2490.240.240.240.190.200.18
    250–4990.100.100.100.170.170.17
    500–9990.080.080.080.160.160.17
    1000+0.120.120.110.150.150.16
  Collective agreement coverage of the establishment0.100.120.060.100.120.08
  Industry of the establishment
    Mining and quarrying0.010.020.010.020.020.01
    Manufacturing0.420.440.370.330.360.25
    Electricity, gas, and water supply0.030.030.010.020.020.01
    Construction0.040.050.020.040.040.02
    Wholesale and retail trade0.190.190.200.230.230.23
    Hotels and restaurants0.050.050.040.060.060.06
    Transport, storage, and communication0.070.070.060.060.060.06
    Financial intermediation0.040.030.070.040.030.06
    Real estate, renting and business activities0.060.060.070.110.100.13
    Education0.040.030.070.040.030.06
    Health and social work0.030.020.060.040.020.08
    Other social and personal service activities0.020.020.020.030.030.03
  Occupation
    Legislators, senior officials, and managers0.060.060.050.060.060.05
    Professionals0.080.060.130.120.100.16
    Technicians and associate professionals0.170.160.210.090.100.09
    Clerks0.120.090.220.150.110.24
    Service workers; shop and market sales’ workers0.110.110.090.190.180.22
    Skilled agricultural and fishery workers0.0020.0030.0010.0010.0010.001
    Craft and related trade workers0.190.210.110.130.170.05
    Plant and machine operators and assemblers0.130.150.060.130.150.07
    Elementary occupations0.140.150.130.130.120.13
Region
  Istanbul–––0.270.250.30
  West Marmara–––0.070.070.07
  Aegean–––0.110.110.12
  East Marmara–––0.120.120.10
  West Anatolia–––0.100.100.11
  Mediterranean–––0.090.090.08
  Central Anatolia–––0.060.060.05
  West Black Sea–––0.040.040.05
  East Black Sea–––0.040.040.05
  Northeast Anatolia–––0.020.030.02
  Central East Anatolia–––0.030.040.03
  Southeast Anatolia–––0.040.050.04
Number of observations241,361175,93865,423132,23591,88140,354
Number of establishments12,8747,867

Sensitivity analysis, BO decomposition of the mean GWG using the female coefficients as the baseline

Specification
(2a)(2b)(2c)(2d)
2006
Raw GWG0.024*** (0.006)0.024*** (0.006)0.024*** (0.006)0.024*** (0.006)
Explained gap−0.041*** (0.003)−0.031*** (0.004)−0.032*** (0.005)−0.012*** (0.001)
[−174.8%][−131.3%][−133.6%][−48.8%]
Unexplained gap0.065*** (0.005)0.055*** (0.004)0.055*** (0.004)0.035*** (0.002)
[274.8%][231.3%][233.6%][148.8%]
2014
Raw GWG0.084*** (0.008)0.084*** (0.008)0.084*** (0.008)0.084*** (0.008)
Explained gap−0.045*** (0.005)−0.024*** (0.005)−0.023*** (0.006)0.001 (0.002)
[−54.1%][−28.1%][−28.0%][1.7%]
Unexplained gap0.129*** (0.007)0.108*** (0.006)0.107*** (0.006)0.083*** (0.003)
[154.1%][128.1%][128.0%][98.3%]

Sensitivity analysis, PSM estimation of the GWG

Specification20062014
Without occupation and industryTreatedControlsDifferenceRobust S.E.TreatedControlsDifferenceRobust S.E.
Unmatched1.1271.151−0.024***0.0031.2031.287−0.084***0.004
Matched1.1271.155−0.028***0.0031.2031.286−0.084***0.004
With industryTreatedControlsDifferenceS.E.TreatedControlsDifferenceS.E.
Unmatched1.1271.151−0.024***0.0031.2031.287−0.084***0.004
Matched1.1271.148−0.021***0.0031.2031.287−0.084***0.005
With industry and occupationTreatedControlsDifferenceS.E.TreatedControlsDifferenceS.E.
Unmatched1.1271.151−0.024***0.0031.2031.287−0.084***0.004
Matched1.1271.146−0.020***0.0041.2031.268−0.065***0.005

Sensitivity analysis, adjusted GWG, 2006

SpecificationSelected percentiles of the wage distributionOLS
10th25th50th75th90thmean
(2a) personal characteristics
  Benchmark0.001 (0.001)0.001 (0.001)0.042*** (0.004)0.107*** (0.012)0.077*** (0.012)0.051*** (0.005)
  Sample (aged 18–64 years)0.001 (0.001)0.001 (0.001)0.045*** (0.005)0.107*** (0.012)0.076*** (0.012)0.051*** (0.005)
  Weighteda0.001 (0.001)0.001 (0.001)0.091*** (0.011)0.125*** (0.022)0.072* (0.029)0.064*** (0.010)
  Conditional quantile regression0.000 (0.000)−0.000 (0.000)0.027*** (0.003)0.056*** (0.005)0.064*** (0.008)0.051*** (0.005)
(2b) personal and work-related characteristics
  Benchmark0.001 (0.001)0.001 (0.001)0.037*** (0.004)0.100*** (0.010)0.072*** (0.012)0.048*** (0.004)
  Sample (aged 18–64 years)0.001 (0.001)0.001 (0.001)0.040*** (0.004)0.100*** (0.010)0.071*** (0.012)0.048*** (0.004)
  Weighteda0.001 (0.001)0.001 (0.001)0.088*** (0.010)0.127*** (0.019)0.070** (0.025)0.064*** (0.008)
  Conditional quantile regression0.001 (0.001)0.004*** (0.000)0.024*** (0.002)0.057*** (0.004)0.075*** (0.008)0.048*** (0.004)
  Excluding overtime0.001 (0.001)0.001 (0.001)0.045*** (0.004)0.109*** (0.011)0.074*** (0.012)0.053*** (0.004)
  Including occupation0.001 (0.001)0.001 (0.001)0.052*** (0.004)0.133*** (0.010)0.067*** (0.011)0.057*** (0.004)
(2c) personal, work-related, observed firm and establishment characteristics
  Benchmark0.001 (0.001)0.001 (0.001)0.040*** (0.004)0.089*** (0.009)0.070*** (0.011)0.048*** (0.004)
  Sample (aged 18–64 years)0.000 (0.001)0.000 (0.001)0.041*** (0.004)0.088*** (0.009)0.070*** (0.011)0.048*** (0.004)
  Weighteda0.000 (0.001)0.000 (0.001)0.083*** (0.009)0.105*** (0.016)0.063** (0.022)0.057*** (0.007)
  Conditional quantile regression0.005*** (0.001)0.011*** (0.002)0.024*** (0.002)0.043*** (0.004)0.056*** (0.006)0.048*** (0.004)
  Excluding overtime0.001 (0.001)0.001 (0.001)0.044*** (0.004)0.089*** (0.009)0.068*** (0.011)0.049*** (0.004)
  Including occupation0.001 (0.001)0.001 (0.001)0.048*** (0.004)0.101*** (0.009)0.053*** (0.011)0.048*** (0.004)
  Including detailed industryc−0.000 (0.001)−0.000 (0.001)0.027*** (0.004)0.062*** (0.009)0.058*** (0.011)0.036*** (0.004)
(2d) personal and work-related characteristics and establishment fixed effects
  Benchmark0.001*** (0.000)0.001*** (0.000)0.026*** (0.002)0.082*** (0.006)0.111*** (0.008)0.051*** (0.002)
  Sample (aged 18–64 years)0.001*** (0.000)0.001*** (0.000)0.027*** (0.002)0.082*** (0.006)0.110*** (0.008)0.051*** (0.002)
  Weighteda0.001*** (0.000)0.001*** (0.000)0.045*** (0.005)0.079*** (0.012)0.088*** (0.013)0.050*** (0.004)
  Conditional quantile regression0.004*** (0.001)0.010*** (0.001)0.023*** (0.001)0.043*** (0.002)0.074*** (0.004)0.051*** (0.002)
  Excluding overtime0.001*** (0.000)0.001*** (0.000)0.027*** (0.002)0.075*** (0.006)0.101*** (0.008)0.048*** (0.002)
  Including occupation0.001*** (0.000)0.001*** (0.000)0.028*** (0.002)0.075*** (0.006)0.077*** (0.008)0.042*** (0.002)

Variable definitions

VariableTypeDefinition
Log hourly gross wageContinuousConstructed by the author: the monthly gross wage (the sum of monthly basic wages, overtime payments, payments for shift work/night work, and other regular payments paid to employees) in the reference montha divided by monthly paid hours (the sum of contractual working hours pertaining to basic wage and overtime hours worked) during the reference month (deflated using the Consumer Price Index base year 2003, in 2003 TRY)
GenderBinaryRecoded by the author as follows: {0female1male \left\{ {\matrix{ 0 & {{\rm{female}}} \hfill \cr 1 & {{\rm{male}}} \hfill \cr } } \right.
Educational attainmentCategoricalThe highest successfully completed level of education which the employee has received according to the International Standard Classification of Education (ISCED 97 in 2006 and ISCED 2011 in 2014). {1primary school and below2primary education and seconday eduction3high school4vocational high school5higher education \left\{ {\matrix{ 1 \hfill & {{\rm{primary}}\,{\rm{school}}\,{\rm{and}}\,{\rm{below}}} \hfill \cr 2 \hfill & {{\rm{primary}}\,{\rm{education}}\,{\rm{and}}\,{\rm{seconday}}\,{\rm{eduction}}} \hfill \cr 3 \hfill & {{\rm{high}}\,{\rm{school}}} \hfill \cr 4 \hfill & {{\rm{vocational}}\,{\rm{high}}\,{\rm{school}}} \hfill \cr 5 \hfill & {{\rm{higher}}\,{\rm{education}}} \hfill \cr } } \right.
Potential labor market experienceContinuousConstructed by the author: age–years of schooling–6. Years of schooling are taken as 5 years for primary school and below, 8 years for primary education and secondary education, 11 years for high school and vocational high school, and 14 years for higher education following Cebeci (2015).
TenureContinuousThe total length of service in the firm to which the establishment belongs in the reference month (in years).
Full-time employmentBinaryContractual working time. Recoded by the author as: {0if part-time1if full time \left\{ {\matrix{ 0 & {{\rm{if}}\,{\rm{part{\text -}time}}} \hfill \cr 1 & {{\rm{if}}\,{\rm{full}}\,{\rm{time}}} \hfill \cr } } \right.
Type of employment contractBinaryType of employment contract of the worker. Recoded by the author as: {0if temporary (fixed-term) contact1if permanent contact \left\{ {\matrix{ 0 \hfill & {{\rm{if}}\,{\rm{temporary}}\,\left( {{\rm{fixed {\text -} term}}} \right)\,{\rm{contact}}} \hfill \cr 1 \hfill & {{\rm{if}}\,{\rm{permanent}}\,{\rm{contact}}} \hfill \cr } } \right.
Overtime workBinaryConstructed by the author based on the monthly hours of overtime work during the reference month. Recoded by the author as: {0if total monthly hours of overtime work is zero1if total monthly hours of overtime work greater than zero \left\{ {\matrix{ 0 \hfill & {{\rm{if}}\,{\rm{total}}\,{\rm{monthly}}\,{\rm{hours}}\,{\rm{of}}\,{\rm{overtime}}\,{\rm{work}}\,{\rm{is\, zero}}} \hfill \cr 1 \hfill & {{\rm{if}}\,{\rm{total}}\,{\rm{monthly}}\,{\rm{hours}}\,{\rm{of}}\,{\rm{overtime}}\,{\rm{work}}\,{\rm{greater}}\,{\rm{than}}\,{\rm{zero}}} \hfill \cr } } \right.
Firm sizeCategoricalSize of the firm (in terms of number of employees) to which the establishment belongs. {1if 10−492if 50−2493if 250−4994if 500−9995if 1000 and more \left\{ {\matrix{ 1 \hfill & {{\rm{if}}\,10 - 49} \hfill \cr 2 \hfill & {{\rm{if}}\,50 - 249} \hfill \cr 3 \hfill & {{\rm{if}}\,250 - 499} \hfill \cr 4 \hfill & {{\rm{if}}\,500 - 999} \hfill \cr 5 \hfill & {{\rm{if}}\,1000\,{\rm{and\, more}}} \hfill \cr } } \right.
Collective agreement coverageBinaryCollective agreement coverage of the establishment. Recoded by the author as: {0if no collective pay aggrement exists in the establishment1otherwise \left\{ {\matrix{ 0 \hfill & {{\rm{if}}\,{\rm{no}}\,{\rm{collective}}\,{\rm{pay}}\,{\rm{aggrement}}\,{\rm{exists}}\,{\rm{in}}\,{\rm{the}}\,{\rm{establishment}}} \hfill \cr 1 \hfill & {{\rm{otherwise}}} \hfill \cr } } \right.
IndustryCategoricalPrincipal economics activity of the establishment coded according to the Statistical Classification of Economic Activities in the European Community (NACE Rev. 1.1 in 2006 and NACE Rev. 2 in 2014). Recoded by the author at section level according to NACE Rev. 2 (12 categories).
OccupationCategoricalOccupation of the employee in the reference month coded according to the ISCO88 in 2006 and ISCO 08 in 2014. Recoded by the author at one-digit ISCO 08 level (9 categories)
RegionbCategoricalRegion of the establishment codded according to the NUTS-1 – only available in 2014 (12 categories)

Sensitivity analysis, adjusted GWG, 2014

SpecificationSelected percentiles of the wage distributionOLS
10th25th50th75th90thMean
(2a) Personal characteristics
  Benchmark0.011*** (0.002)0.011*** (0.002)0.175*** (0.008)0.172*** (0.011)0.143*** (0.016)0.122*** (0.007)
  Sample (aged 18–64 years)0.011*** (0.002)0.012*** (0.002)0.177*** (0.008)0.171*** (0.011)0.140*** (0.016)0.123*** (0.007)
  Weighteda0.015** (0.005)0.013** (0.004)0.140*** (0.013)0.132*** (0.019)0.064 (0.033)0.092*** (0.012)
  Conditional quantile regression0.038*** (0.006)0.025*** (0.002)0.096*** (0.005)0.148*** (0.007)0.153*** (0.009)0.122*** (0.007)
(2b) Personal and work-related characteristics
  Benchmark0.009*** (0.002)0.009*** (0.002)0.155*** (0.007)0.154*** (0.010)0.138*** (0.016)0.108*** (0.006)
  Sample (aged 18–64 years)0.009*** (0.002)0.010*** (0.002)0.157*** (0.007)0.153*** (0.010)0.135*** (0.016)0.108*** (0.006)
  Weighteda0.012** (0.005)0.011** (0.004)0.121*** (0.012)0.110*** (0.016)0.048 (0.033)0.074*** (0.011)
  Conditional quantile regression0.037*** (0.005)0.041*** (0.003)0.068*** (0.004)0.129*** (0.007)0.162*** (0.008)0.108*** (0.006)
  Excluding overtime0.010*** (0.002)0.010*** (0.002)0.164*** (0.007)0.152*** (0.010)0.126*** (0.016)0.111*** (0.006)
  Including occupation0.011*** (0.002)0.011*** (0.002)0.154*** (0.007)0.153*** (0.010)0.113*** (0.016)0.103*** (0.006)
(2c) Personal, work-related, observed firm and establishment characteristics
  Benchmark0.011*** (0.002)0.012*** (0.002)0.146*** (0.007)0.143*** (0.010)0.124*** (0.015)0.106*** (0.006)
  Sample (aged 18–64 years)0.011*** (0.002)0.012*** (0.002)0.148*** (0.007)0.141*** (0.010)0.121*** (0.015)0.106*** (0.006)
  Weighteda0.014*** (0.004)0.013*** (0.004)0.122*** (0.012)0.120*** (0.018)0.094** (0.032)0.090*** (0.012)
  Conditional quantile regression0.058*** (0.006)0.052*** (0.004)0.077*** (0.004)0.112*** (0.006)0.141*** (0.008)0.106*** (0.006)
  Excluding overtime0.012*** (0.002)0.012*** (0.002)0.149*** (0.007)0.136*** (0.010)0.109*** (0.015)0.105*** (0.006)
  Including occupation0.012*** (0.002)0.012*** (0.002)0.138*** (0.007)0.125*** (0.009)0.081*** (0.015)0.092*** (0.006)
  Including detailed industryb0.010*** (0.002)0.010*** (0.001)0.127*** (0.007)0.122*** (0.009)0.116*** (0.015)0.095*** (0.005)
  Including regionc0.013*** (0.002)0.012*** (0.002)0.160*** (0.007)0.148*** (0.009)0.123*** (0.015)0.113*** (0.006)
(2d) Personal and work-related characteristics and establishment fixed effects
  Benchmark0.006*** (0.001)0.006*** (0.001)0.113*** (0.004)0.132*** (0.007)0.170*** (0.011)0.095*** (0.004)
  Sample (aged 18–64 years)0.006*** (0.001)0.006*** (0.001)0.114*** (0.004)0.131*** (0.007)0.168*** (0.011)0.095*** (0.004)
  Weighteda0.007* (0.003)0.006** (0.002)0.103*** (0.008)0.119*** (0.016)0.150*** (0.033)0.089*** (0.012)
  Conditional quantile regression0.030*** (0.004)0.039*** (0.003)0.061*** (0.003)0.099*** (0.004)0.147*** (0.006)0.095*** (0.004)
  Excluding overtime0.007*** (0.001)0.007*** (0.001)0.115*** (0.004)0.124*** (0.007)0.152*** (0.011)0.094*** (0.004)
  Including occupation0.006*** (0.001)0.006*** (0.001)0.102*** (0.004)0.109*** (0.007)0.113*** (0.011)0.076*** (0.004)

Components of the BO decomposition of the mean GWG, 2014

Specification
(2a)(2b)(2c)(2d)
Raw GWG0.084*** (0.008)0.084*** (0.008)0.084*** (0.008)0.084*** (0.003)
Explained gap−0.035*** (0.005)−0.024*** (0.005)−0.019*** (0.006)−0.008*** (0.002)
[−41.9%][−29.1%][−22.8%][−9.0%]
Unexplained gap0.119*** (0.007)0.108*** (0.006)0.103*** (0.006)0.092*** (0.003)
Explained[141.9%][129.1%][122.8%][109.0%]
Personal characteristics
Education−0.115*** (0.005)−0.109*** (0.005)−0.101*** (0.004)−0.066*** (0.002)
[−137.0%][−129.6%][−119.7%][−79.2%]
Potential labour market experience0.080*** (0.003)0.051*** (0.002)0.054*** (0.002)0.045*** (0.001)
[95.2%][61.2%][64.9%][54.0%]
Work-related characteristics
Tenure 0.028*** (0.002)0.023*** (0.002)0.017*** (0.001)
[33.5%][27.0%][20.6%]
Permanent contract 0.000 (0.001)0.000 (0.001)0.000 (0.000)
[0.3%][0.3%][0.6%]
Full-time −0.000 (0.000)−0.000 (0.000)−0.001*** (0.000)
[−0.3%][−0.3%][−1.3%]
Overtime work 0.005*** (0.001)−0.001 (0.001)−0.003*** (0.000)
[5.8%][−1.2%][−3.7%]
Observed firm and establishment characteristics
Firm size −0.002 (0.002)
[−1.9%]
Collective agreement coverage 0.003*** (0.001)
[3.8%]
Industry of the establishment 0.004 (0.003)
Unexplained [4.3%]
Characteristics0.054** (0.017)0.133* (0.056)0.072 (0.059)0.110** (0.033)
[64.0%][157.9%][85.6%][130.8%]
Constant0.065*** (0.018)−0.024 (0.057)0.031 (0.060)−0.018 (0.033)
[77.8%][−28.8%][37.2%][−21.8%]
DOI: https://doi.org/10.2478/izajodm-2021-0014 | Journal eISSN: 2520-1786
Journal RSS Feed
Language: English
Accepted on: Jun 15, 2021
|
Published on: Oct 18, 2021
Published by: Sciendo
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
gender wage gap,
within- and between-establishments,
wage distribution
Related subjects:
Business and economics,
Political economics,
Microeconomics,
Macroeconomics,
Economic policy,
Mathematics and statistics for economists,
Econometrics

© 2021 Ezgi Kaya, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 License.

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