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# Gender Wage Gap in Turkey and Related Theories – Part 8

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## Chapter 3: Data and Descriptive Studies

### 3.1 Wage Structure Survey Data

In order to provide concrete evidence we shall be conducting regression and decomposition techniques using the data from the Wage Structure Survey conducted by TURKSTAT. This survey was conducted to collect a firm based data set so as to provide extensive information on the wages of the workers, worker’s demographic features along with other features of the firm.

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The wages of the workers is measured as on November 2006. However, the major limitation of the data provided by the survey is that since the data is collected at a firm base level, the wages of only formally based employees are collected and the data fails to cover the employees in the informal grade. However, despite some of the limitations in the sample data provided, Wage Structure Survey is the largest and most updated data set that provides full information on wages as the data contains survey of more than 300000 employees. It also contains detailed industry and occupational information as well as information on administrative posts and collective bargaining.

### 3.2: Detailed Description of Our Sample Data

In order to provide extensive validity and to make the sample more homogeneous, we have restricted the data analysis only for the employees that have worked full time i.e. those who are paid at least 30 hours per week excluding overtime in November 2006.

In Turkey, a majority of the workers are paid on the monthly basis, thus at the time of employing the regression analysis, we will be using the logarithm of gross monthly wage as our dependent variable. Next, upon our first inspection of the data, we found that there was serious amount of under-reporting of wages. The data for November 2006 indicates that almost 1/3 rd of the total employees surveyed are earning just the minimum wage of 531 TL. The data also reveals that only 17.3 percent of the employed individuals who work on full time basis are earning 500 TL to 600 TL per month.  However, note also that the same data shows that even though they were working formally and full time, 25.7 percent of the wages were below 500 TL.

This is because of the usual practice by the firms in Turkey, who report minimum wages of their employees so as to avoid the taxes. Now since it is almost possible to determine which employee were underreported and which were not, we choose to exclude these employees from our analysis. Further, if the employees are more likely to report the female workforce at the minimum wages than males, this will cause the gender wage gap in Turkey Labor Market to appear small than the data indicates. However, those employed at the minimum wage make up 32.6 percent of the females and 33.7 percent of the males in the data set. In other words, excluding those who make minimum wage in the data set, excludes relatively more males than females.

### 3.3: Descriptive Statistics

The results of the descriptive statistics are provided in the Table below. Important to note that there were significant differences in the characteristics of males and females. The females are young and thus are reported to have low work experience and job tenure. However, females in employment have higher education levels compared to males. For Instance, 38.07 percent of females have at least a college degree while only 20.78 percent male’s employees have equivalent education.

Table: Descriptive Statistics

 Male Female Nb of observations 141,767 40,298 Wages 1351 1347 Log wages 6.95 6.92 Basic Control Variables Age (years) 34.29 30.94 Experience(years) 17.64 12.64 Tenure(years) 5.22 4.04 Male (%) Female(%) Educational attainment Elementary education 43.96 25.17 High school 20.81 26.86 Vocational high school 14.44 9.91 University or higher 20.78 38.07 Total 100 100 Firm size 10 - 49 27.92 29.31 50 - 249 26.96 30.03 250 + 45.12 40.66 Total 100 100 Administrative post No 82.21 83.85 Yes 17.79 16.15 Total 100 100 Collective bargaining No 77.92 88.66 Yes 22.08 11.34 Total 100 100
 Male (%) Female(%) Industry C Mining and quarrying 2.45 0.40 DA Manufacture of food products, beverages and tobacco products 5.11 2.96 DB Manufacture of textiles and textile products 12.01 20.29 DC Manufacture of leather and leather products 0.82 0.59 DD Manufacture of wood and wood products 0.53 0.17 DE Manufacture of pulp, paper and paper products; publishing and printing 1.85 1.24 DF Manufacture of coke, re ned petroleum products and nuclear fuel 0.46 0.28 DG Manufacture of chemicals, chemical products and man-made  bres 2.53 2.29 DH Manufacture of rubber and plastic products 1.93 0.88 DI Manufacture of other non-metallic mineral products 3.84 1.75 DJ Manufacture of basic metals and fabricated metal products 7.78 2.03 DK Manufacture of machinery and equipment i.e. 4.67 1.99 DL Manufacture of electrical and optical equipment 2.72 3.24 DM Manufacture of transport equipment 4.43 1.20 DN Manufacturing i.e. 1.67 1.66 E Electricity, gas and water supply 2.54 0.75 F Construction 4.87 2.20 G Wholesale and retail trade; repair of motor vehicles and motorcycles 13.91 17.02 H Hotels and restaurants 3.65 3.48 I Transport, storage and communication 9.53 8.79 J Financial intermediation 1.92 5.32 K Real estate, renting and business activities 5.65 6.75 M Education 2.03 6.71 N Health and social work 1.33 6.42 O Other community, social and personal service activities 1.76 1.59 Total 100 100
 Occupation (ISCO 2 digits) 11 Legislators and Senior O  cials 0.02 0.01 12 Corporate Managers 5.77 5.58 13 General Managers 0.5 0.37 21 Physical, Mathematical and Engineering Science Professionals 2.93 2.67 22 Life Science and Health Professionals 0.54 1.73 23 Teaching Professionals 1.04 4.9 24 Other Professionals 2.27 5.94 31 Physical and Engineering Science Associate Professionals 6.65 4.82 32 Life Science and Health Associate Professionals 0.62 2.81 33 Teaching Associate Professionals 0.04 0.21 34 Other Associate Professionals 9.21 14.84 41 O  ce Clerks 7.33 14.4 42 Customer Services Clerks 1.61 6.17 51 Personal and Protective Services Workers 5.82 3.15 52 Models, Salespersons and Demonstrators 2.84 4.26 61 Market-Oriented Skilled Agricultural and Fishery Workers 0.36 0.09 71 Extraction Building Trades Workers 4.42 0.39 72 Metal, Machinery and Related Trades Workers 8.9 1.94 73 Precision, Handicraft, Printing and Related Trades Workers 2.24 1.39 74 Other Craft and Related Trades Workers 6.53 8.14 81 Stationary-Plant and Related Operators 2.75 0.5 82 Machine Operators and Assemblers 8.97 6.2 83 Drivers and Mobile-Plant Operators 5.16 91 Sales and Services Elementary Occupations 4.94 5.22 92 Agricultural, Fishery and Related Laborers 0.07 0.04 93 Laborers in Mining, Construction, Manufacturing and Transport 8.48 4.24 Total 100 100

However, as for the other variables, there is very less pronounced differences as female employees tend to work for smaller terms and they are less likely to be covered by collective bargaining agreements and likely to hold administrative posts. Overall, the raw data indicates that the gender gap is 3 percent. The diagram drawn below will help us in understanding the existing gender wage gap under different percentiles.

Figure: Gender Gap by Quantiles

### 3.4: Decomposition Results

The decomposition technique that we will be using in this research paper will if Machado and Mata which was developed by them during the year 2005. The said decomposition method is a natural extension to the traditional decomposition method of Blinder and Oaxaca, which they used, for quantile regression analysis. Just as this traditional decomposition method, even the Machado and Mata decomposition method will hold exactly as the quantile regression is linear.

Table: Quantile Regression Results by Gender

 5th 10th 25th 50th 75th 90th 95th OLS FEMALE (n=40,298) Age 0.0031 0.0048 0.0137 0.0344 0.0591 0.0768 0.0963 0.0589 (0.0002) (0.0003) (0.0009) (0.0021) (0.0035) (0.0045) (0.0069) (0.0022) Age2 -0.0000 -0.0000 -0.0002 -0.0004 -0.0007 -0.0007 -0.0009 -0.0007 (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0001) (0.0000) Tenure -0.0051 -0.00401 0.0209 0.0534 0.0625 0.0553 0.0508 0.0465 (0.0002) (0.0003) (0.0007) (0.0014) (0.0021) (0.0026) (0.0040) (0.0014) Tenure2 0.0906 0.1335 0.0704 -0.0728 -0.1345 -0.1415 -0.1332 -0.0777 (0.0010) (0.0014) (0.0034) (0.0066) (0.0104) (0.0130) (0.0202) (0.0067) HS 0.0087 0.0156 0.0644 0.1873 0.3433 0.4433 0.4724 0.2882 (0.0009) (0.0012) (0.0033) (0.0073) (0.0117) (0.0147) (0.0215) (0.0075) Voc HS 0.0140 0.0262 0.0900 0.2736 0.4273 0.4842 0.5062 0.3536 (0.0012) (0.0016) (0.0044) (0.0098) (0.0156) (0.0194) (0.0283) (0.0100) College 0.0309 0.0728 0.3282 0.6772 0.9475 1.1560 1.1869 0.7351 (0.0008) (0.0011) (0.0030) (0.0067) (0.0107) (0.0133) (0.0194) (0.0068) Constant 6.2243 6.1946 6.0065 5.6440 5.3117 5.1668 4.9688 5.1849 (0.0041) (0.0053) (0.0149) (0.0349) (0.0593) (0.0778) (0.1187) (0.0357) MALE (n=141,767) Age 0.0014 0.0028 0.0076 0.0163 0.0339 0.0496 0.0544 0.0314 (0.0001) (0.0002) (0.0005) (0.0010) (0.0014) (0.0021) (0.0026) (0.0010) Age2 -0.0000 -0.0000 -0.000 -0.0001 -0.0003 -0.000 -0.0004 -0.0003 (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) Tenure -0.0050 0.0003 0.0349 0.0685 0.0713 0.0611 0.0524 0.0531 (0.0000) (0.0001) (0.0004) (0.0006) (0.0009) (0.0013) (0.0016) (0.0006) Tenure2 0.1060 0.1230 0.0249 -0.1090 -0.1476 -0.1427 -0.1349 -0.0831 (0.0004) (0.0007) (0.0017) (0.0027) (0.0038) (0.0056) (0.0074) (0.0027) HS 0.0045 0.0114 0.0446 0.1210 0.1862 0.2373 0.2831 0.1622 (0.0003) (0.0006) (0.0018) (0.0034) (0.0048) (0.0067) (0.0085) (0.0034) Voc HS 0.0182 0.0495 0.1990 0.3222 0.3344 0.3646 0.4175 0.3135 (0.0004) (0.0007) (0.0021) (0.0040) (0.0057) (0.0080) (0.0101) (0.0040) College 0.0340 0.1172 0.3798 0.6751 0.9404 1.1272 1.1997 0.7055 (0.0003) (0.0005) (0.0017) (0.0033) (0.0047) (0.0066) (0.0082) (0.0033) Constant 6.2537 6.2250 6.1105 5.9760 5.8045 5.7404 5.7872 5.7378 (0.0018) (0.0031) (0.0094) (0.0183) (0.0262) (0.0374) (0.0480) (0.0182) 14 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Using the Machado-Mata method, we can generate the two counterfactual densities:

• Log wage density for females, where we assume that the female workforce had the men’s characteristics but were paid as women (Xm f )
• Log wage density for males, assuming that the men had women's characteristics, but were paid as men (Xf m).

In order to simplify the process for the reader of this thesis, we will be constructing the steps for decomposition of the results:

1. Estimating the coefficients of both male(m) and female(f) using the male data set and female data set from the given sample data.
2. Assuming that we again draw a random sample of 1000 women(with replacement) and using their characteristics to predict the wages using the estimated the coefficients for male and female, i.e. m and f, respectively.
3. Generating the two sets of predicted wages covering the whole distribution.
4. Calculating the marginal distribution of women’s wages and the marginal distribution of men’s wages that would obtain if their characteristics were distributed as women’s are.
5. Using these distributions, estimating the wage gap as the difference between the predicted wage at each quantile using the newly generated wage distribution for women and the counterfactual distribution for men.

Table: Results of Decomposition

 Gender Gap 5th 10th 25th 50th 75th 90th 95th Observed -0.0037 -0.0050 -0.0257 -0.0647 -0.0375 0.0221 0.0499 Female characteristics, Male returns Basic Controls -0.0094 -0.0339 -0.0740 -0.0899 -0.0992 -0.0668 -0.0476 BC, Industry -0.0094 -0.0307 -0.0627 -0.0722 -0.0684 -0.0453 -0.0451 BC, Ind., Occupation -0.0091 -0.0219 -0.0489 -0.0523 -0.0439 -0.0264 -0.0433 BC, Ind., Occ., Firm S. -0.0096 -0.0214 -0.0430 -0.0447 -0.0341 -0.0131 -0.0339 BC, Ind., Occ., Firm S., Admin. And Coll. Brag. -0.0086 -0.0157 -0.0350 -0.0370 -0.0322 -0.0101 -0.0350 Male characteristics, Female returns Basic Controls -0.0145 -0.0299 -0.0780 -0.1080 -0.1240 -0.0795 -0.0512 BC, Industry -0.0171 -0.0289 -0.0713 -0.0918 -0.0789 -0.0539 -0.0265 BC, Ind., Occupation -0.0138 -0.0253 -0.0602 -0.0767 -0.0669 -0.0545 -0.0515 BC, Ind., Occ., Firm S. -0.0177 -0.0290 -0.0582 -0.0609 -0.0443 -0.0353 -0.0377 BC, Ind., Occ., Firm S., Admin. And Coll. Brag. -0.0212 -0.0323 -0.0483 -0.0597 -0.0495 -0.0281 -0.0422

The above table displays the results of the decomposition for specific quantiles. The first row in the table recites the raw gender wage gap. The fist panel of the table provides the gender gap, which is calculated using the counterfactual of male returns to female characteristics. Similarly, the second panel in the table provides us with the gender wage gap using the counterfactual of female returns to the male workforce characteristics.

On comparing both the results, we can see that the results are qualitatively similar. Figure 3 and 4 which (included in the conclusion section) also provides us with similar results for all the included percentiles.

Figure 3 provides us with the results for the gender gap at each quantile for various combinations of counterfactuals constructed using male returns to female characteristics. We constructed such counterfactuals to find out if the male workforce were awarded with female characteristics, but were paid as males, what would be the gender wage gap at that position?

Thus, as we controlled the basic characteristics such as education, age and tenure, the gender wage gap is actually increased, especially at the top half of the distribution. At the media, the gender wage gap increases from 6.47 percent to 8.99 percent; at the 75th quantile, from 3.75 percent to 9.92 percent.

Going back to the raw data conclusion, where it was concluded that the female workforce have higher wages at the highest decile of the wage distribution, the decomposition results also refers to this part of distribution. For Instance, when we constructed a counterfactual wage distribution where we assumed that the female characteristics are awarded with mal returns, our results indicated that the gender wage gap becomes negative at the top decile of the wage distribution as well. The gender wage gap at the ninetieth percentile changes from 4.99 percent to -4.76 percent, using only basic controls. In absolute value, this refers to a change of 10 percent.

Controlling for the other characteristics in our analysis, as industry, occupation and the firm size, we can also explain some percentage of the gender wage gap, primarily between the 25th and 75th quantiles. The unexplained gap shrinks from 7.8 percent to 4.83 percent on the 25th, and from 9.92 percent to 3.22 percent on the 75th quantile.

Now, working on the reciprocal analysis, i.e. if female workforce had male characteristics but were rewarded as females, what will be the gender wage gap? Just as the previous decomposition carried for male workforce, controlling for the basic characteristics widens the gender gap. However, if we add controlling variables for firm levels characteristics, this will explain the smaller parts of the gender gap. But if all the controls are included, the gender gap does not seem to be much smaller than the observed gender gap. In fact, the gender wage gap widens at the lower and at the upper part of the distribution.

In nutshell, our decomposition results indicates that the gender gap in wages among female and male workforce in Turkey is the result of the differences in returns to the labor market characteristics. For males, assuming that they had female characteristics but were being paid male wages, controlling for the basic characteristics widens the gender gap. However, including the firm level controls helps in explaining some part of the gender wage gap. On the contrary, if we assume that the female workforce had male characteristics but were being paid female wages, again the gender gap was observed to widen. Even if we include all the controlling variables, we will fail to explain the observed gender gap.

## Conclusion

My thesis was focused to study the and analsye the wage difference between female and male workforce in the Turkish Labor Market. We started our thesis by commenting on the gender wage gap taking the general view. We then introduced some of the prominent economic theories related to the gender wage gap in the labor market. Further, we introduced some of the advanced statistical data relating to labor market in Turkey. By the end of the discussion over these statistical findings, we were having whole set of evidence that female workforce is suffering from occupation discrimination in the nation. Finally, in order to further validate the theoretical based evidences, we introduced regressiona analsysi and decomposition analysis for the sample data collected by Turkstat during November 2006.

Using the sample data for November 2006, we studied the gender gap along the wage distribution in Turkey by using the firm based sample data. Drawing a scatter plot for the relationship between gender wage gap distributions. May interesting patters emerge. The gender gap was very close to the lower end of the distribution. Furthermore, at the higher end of the distribution, the female gender indicated higher wages than men did.

We then conducted the Quantile gender regression to study the gender gap along with the wage distribution. We initiated our statistical analysis with the basic regression where we assumed that the wages for both the genders is at equal footing. We find that controlling for education widens the gender gap. Further, adding variables as industry, occupation and firm level, we find that such variables reduce the gender wage gap. The most significant reduction happens around the median. On the other hand, we find that there is a sizeable gender gap that cannot be accounted for because of the difference in the characteristics at the upper end of the wage distribution.

Figure 3: Machado-Mata Decomposition for Males

Figure 3: Machado-Mata Decomposition for Males

Figure 3: Machado-Mata Decomposition for Males

Figure 4: Machado-Mata Decomposition Results for Females

Further accounting for separate quantile regression for both the genders, we find that the features offer by the labor market, both for males and females are different. Thus, different returns for different genders indicates that the pooled regression results has the capability of providing biased outlook on the gender gap.

Thus, we decided to de-compose the results into different parts courtesy differences in labor market features and difference in returns. The decomposition results shows that 50 percent of the gender wage gap arises because of difference in returns. Here, we again accounted for an innovative decomposition by allocating female characteristics to the male gender workforce, this was done to find out if characteristics are changed, what would be the gender gap. As a result, controlling for education, we find that the gender gap is larger than what the raw data had indicated (3 percent).

Including other controls, we can explain around 50 percent of the gender gap around the median and even less at other parts of the distribution. Thus, our objective of finding out as what will be the gender gap if males are awarded with female characteristics, they are awarded by female returns. This counterfactual gender wage gap is even larger. Controlling for the gender gap and including the other controls can only reduce it to the raw gender gap. In other words, the analysis of decomposition indicates that gender wage gap originates majorly from difference in returns.

**Please note that the sample data set that we used in our thesis, allows us to study the industry and occupation as well as some other firm level characteristics. However, it does not provide any information on the selection criteria of both the genders. Another limitation which we have already discussed in our previous sections that since while the collection of data, only full employed individuals were involved, estimated results can include some sort of biasness originating from the source of data.

This means that these limitations could be important implication for the overall wage gap as the female employment rates are low in Turkey. For Instance, during 2006, only 16.3 percent of females were employed whereas the male employment was 61.7 percent. Hence, the results should be interpreted considering the limitations of the sample data.

Regression Results:

Quantile regressions by gender

Table : Quantile Regressions by Gender (Basic Control, Industry, Occupation, Firm Size)

MALE(n=141767)

 MALE 5th 10th 25th 50th 75th 90th 95th OLS Age 0.0047 0.0097 0.0157 0.0174 0.0235 0.0309 0.0374 0.0273 (0.0004) (0.0005) (0.0007) (0.0009) (0.0013) (0.0019) (0.0025) (0.0009) Age2 -0.0001 -0.0001 -0.0002 -0.0002 -0.0002 -0.0002 -0.0002 -0.0002 (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) Tenure -0.0025 0.0051 0.0273 0.0479 0.053 0.05 0.0446 0.041 (0.0003) (0.0004) (0.0005) (0.0006) (0.0008) (0.0012) (0.0016) (0.0006) Tenure2 0.0834 0.0797 0.0091 -0.0754 -0.1125 -0.1274 -0.1224 -0.0759 (0.0013) (0.002) (0.0021) (0.0026) (0.0033) (0.005) (0.0069) (0.0025) HS 0.0114 0.0215 0.0371 0.0506 0.0765 0.1063 0.1297 0.0776 (0.0013) (0.002) (0.0025) (0.0034) (0.0044) (0.0063) (0.0083) (0.0033) Voc HS 0.0354 0.066 0.1339 0.157 0.1639 0.191 0.2382 0.1746 (0.0015) (0.0024) (0.003) (0.004) (0.0052) (0.0075) (0.0099) (0.0039) College 0.0503 0.1062 0.2569 0.4094 0.5779 0.7122 0.791 0.4549 (0.0017) (0.0026) (0.0032) (0.0042) (0.0054) (0.0078) (0.0105) (0.0042) dind1 0.0535 0.0957 0.1829 0.2452 0.2751 0.3481 0.3485 0.2635 (0.0032) (0.005) (0.0062) (0.0084) (0.0108) (0.0157) (0.0206) (0.0083) dind2 0.048 0.0855 0.1583 0.2115 0.265 0.3715 0.4112 0.2457 (0.0026) (0.0041) (0.0051) (0.0068) (0.0088) (0.0127) (0.0167) (0.0067) dind4 0.0001 0.0094 0.0247 -0.0002 -0.0123 -0.0375 -0.0164 0.0044 (0.0049) (0.0079) (0.0099) (0.0135) (0.0174) (0.025) (0.0327) (0.0133) dind5 0.023 0.0268 0.0417 0.051 0.0887 0.1313 0.0908 0.0609 (0.006) (0.0096) (0.012) (0.0164) (0.0212) (0.0303) (0.0396) (0.0161) dind6 0.022 0.0513 0.1178 0.1456 0.176 0.2512 0.2945 0.1846 (0.0035) (0.0055) (0.0068) (0.0092) (0.0118) (0.017) (0.0222) (0.009) dind7 0.0875 0.1928 0.5262 0.5488 0.5498 0.6217 0.7074 0.5893 (0.0072) (0.0114) (0.014) (0.0189) (0.0245) (0.0355) (0.0468) (0.0187) dind8 0.0533 0.1104 0.1869 0.2416 0.3077 0.4155 0.3944 0.3003 (0.0036) (0.0057) (0.007) (0.0095) (0.0122) (0.0175) (0.0229) (0.0094) dind9 0.02 0.0393 0.0877 0.1264 0.2177 0.3449 0.3476 0.1837 (0.0033) (0.0053) (0.0065) (0.0089) (0.0115) (0.0163) (0.0213) (0.0088) dind10 0.0352 0.0591 0.1003 0.1188 0.1283 0.159 0.1571 0.142 (0.003) (0.0048) (0.006) (0.0083) (0.0107) (0.0154) (0.02) (0.0081) dind11 0.047 0.0842 0.1687 0.2299 0.2895 0.3369 0.3754 0.2642 (0.0025) (0.004) (0.005) (0.0068) (0.0087) (0.0125) (0.0163) (0.0067) dind12 0.0241 0.0438 0.0949 0.1635 0.2332 0.2449 0.2189 0.1799 (0.003) (0.0048) (0.006) (0.0081) (0.0105) (0.0152) (0.0199) (0.008) dind13 0.0418 0.0778 0.1416 0.1895 0.2443 0.3068 0.2816 0.2415 (0.0033) (0.0052) (0.0066) (0.0089) (0.0115) (0.0165) (0.0217) (0.0088) dind14 0.0529 0.1052 0.2066 0.2593 0.2962 0.3282 0.3181 0.2762 (0.0029) (0.0045) (0.0056) (0.0076) (0.0098) (0.0141) (0.0184) (0.0075) dind15 0.0313 0.041 0.0437 0.0407 0.038 0.0308 0.0161 0.0389 (0.0039) (0.0062) (0.0078) (0.0107) (0.0138) (0.0199) (0.0262) (0.0105) dind16 0.3508 0.4178 0.456 0.4422 0.4875 0.5045 0.4877 0.4537 (0.0027) (0.0043) (0.0053) (0.0071) (0.0092) (0.0133) (0.0175) (0.007) dind17 0.0258 0.0502 0.1235 0.1455 0.2153 0.3079 0.3203 0.1842 (0.0028) (0.0044) (0.0055) (0.0074) (0.0096) (0.0139) (0.0182) (0.0073)
 MALE 5th 10th 25th 50th 75th 90th 95th OLS dind18 0.0279 0.0495 0.1039 0.1402 0.2171 0.2972 0.3059 0.1966 (0.0022) (0.0035) (0.0043) (0.0058) (0.0075) (0.0108) (0.0141) (0.0057) dind19 0.0259 0.055 0.1249 0.1435 0.1815 0.2763 0.3086 0.1733 (0.0029) (0.0047) (0.0059) (0.008) (0.0103) (0.0148) (0.0194) (0.0079) dind20 0.0254 0.0612 0.1437 0.1772 0.2569 0.3386 0.3851 0.2407 (0.0024) (0.0038) (0.0047) (0.0063) (0.0082) (0.0119) (0.0155) (0.0063) dind21 0.2346 0.3164 0.3994 0.3981 0.4353 0.4827 0.4503 0.4363 (0.0031) (0.0049) (0.006) (0.0081) (0.0104) (0.0151) (0.02) (0.008) dind22 0.0324 0.0682 0.1649 0.222 0.3061 0.3939 0.4306 0.2922 (0.0027) (0.0043) (0.0054) (0.0073) (0.0094) (0.0137) (0.0181) (0.0072) dind23 0.0045 -0.0005 -0.0007 -0.0146 0.0228 0.0979 0.051 -0.033 (0.0049) (0.0077) (0.0099) (0.0136) (0.0179) (0.0265) (0.0357) (0.0134) dind24 0.0384 0.0632 0.1734 0.1962 0.2925 0.3303 0.2776 0.2456 (0.0048) (0.0073) (0.0092) (0.0125) (0.0155) (0.0215) (0.0281) (0.0123) dind25 0.0399 0.083 0.1665 0.2278 0.2754 0.3611 0.3741 0.2576 (0.0035) (0.0055) (0.0069) (0.0093) (0.0119) (0.0172) (0.0226) (0.0092) docc21 0.3645 0.1499 0.1416 0.2877 0.2889 0.3893 0.1858 0.2384 (0.023) (0.0363) (0.0474) (0.0653) (0.0839) (0.1154) (0.1514) (0.065) docc22 0.0289 0.0697 0.1582 0.3558 0.4843 0.5158 0.5216 0.3207 (0.0023) (0.0037) (0.0045) (0.0062) (0.008) (0.0115) (0.0151) (0.0061) docc23 0.0266 0.0505 0.0686 0.1494 0.1937 0.2489 0.2889 0.1321 (0.0055) (0.0086) (0.0108) (0.0148) (0.0192) (0.0275) (0.036) (0.0145) docc24 0.0359 0.0964 0.2521 0.3001 0.3298 0.2463 0.1645 0.2273 (0.0031) (0.0049) (0.0061) (0.0083) (0.0108) (0.0156) (0.0207) (0.0082) docc25 0.0849 0.1897 0.2353 0.3457 0.4994 0.4349 0.4833 0.3207 (0.0068) (0.0106) (0.0133) (0.0179) (0.022) (0.0307) (0.0403) (0.0177) docc26 0.0249 0.032 -0.0181 -0.0932 -0.1842 -0.3261 -0.2155 -0.0309 (0.006) (0.0095) (0.012) (0.0165) (0.0217) (0.032) (0.0432) (0.0163) docc27 -0.0031 0.0011 0.0131 0.0646 0.0988 0.0703 0.022 0.0367 (0.0032) (0.0051) (0.0064) (0.0087) (0.0112) (0.0162) (0.0212) (0.0085) docc28 0.012 0.0355 0.0633 0.0368 -0.006 -0.0421 -0.0479 0.0232 (0.0023) (0.0037) (0.0046) (0.0062) (0.008) (0.0116) (0.0154) (0.0061) docc29 0.0042 0.0335 0.0181 -0.0321 -0.09 -0.1176 -0.0697 -0.0486 (0.0064) (0.0099) (0.0124) (0.017) (0.022) (0.0313) (0.0413) (0.0167) docc210 0.0014 0.0306 0.0132 -0.0266 -0.0671 -0.1226 0.2977 0.0097 (0.0226) (0.0358) (0.0469) (0.0652) (0.0837) (0.1158) (0.1531) (0.0649) docc212 -0.0097 -0.0135 -0.0374 -0.0958 -0.1327 -0.1746 -0.194 -0.1055 (0.0022) (0.0035) (0.0043) (0.0059) (0.0076) (0.011) (0.0145) (0.0058) docc213 0.0104 -0.0015 -0.0603 -0.1381 -0.2054 -0.2311 -0.2151 -0.1498 (0.0035) (0.0054) (0.0067) (0.0091) (0.0117) (0.0167) (0.0221) (0.0089) docc214 -0.0113 -0.017 -0.0603 -0.1305 -0.2197 -0.3204 -0.346 -0.1658 (0.0024) (0.0039) (0.0049) (0.0066) (0.0084) (0.0121) (0.0158) (0.0065) docc215 -0.0103 -0.0119 -0.0576 -0.1348 -0.2321 -0.2521 -0.2847 -0.162 (0.0028) (0.0044) (0.0055) (0.0075) (0.0097) (0.0139) (0.0183) (0.0073) docc216 0.009 -0.0039 -0.0763 -0.1112 -0.1789 -0.2358 -0.0314 -0.1388 (0.0087) (0.0136) (0.017) (0.0232) (0.0299) (0.0427) (0.0564) (0.0229) docc217 0.0047 -0.0002 -0.0172 -0.0625 -0.1285 -0.1971 -0.2367 -0.0839 (0.0027) (0.0043) (0.0053) (0.0073) (0.0094) (0.0135) (0.0178) (0.0072) docc218 0.0026 0.0053 0.007 -0.0347 -0.0885 -0.16 -0.1988 -0.0534 (0.0023) (0.0036) (0.0045) (0.0061) (0.008) (0.0116) (0.0153) (0.0061) docc219 -0.0079 -0.0182 -0.048 -0.1266 -0.1847 -0.2514 -0.3085 -0.1427 (0.0037) (0.0058) (0.0072) (0.0098) (0.0127) (0.0183) (0.024) (0.0097)
 MALE 5th 10th 25th 50th 75th 90th 95th OLS docc220 -0.004 -0.0109 -0.041 -0.1082 -0.1845 -0.2297 -0.2836 -0.1267 (0.0028) (0.0044) (0.0054) (0.0073) (0.0093) (0.0133) (0.0173) (0.0072) docc221 0.0105 0.023 0.0031 -0.0623 -0.1285 -0.2077 -0.2439 -0.0859 (0.0032) (0.005) (0.0062) (0.0084) (0.0109) (0.0157) (0.0208) (0.0083) docc222 -0.0004 -0.0083 -0.0356 -0.11 -0.1888 -0.2251 -0.2543 -0.1184 (0.0024) (0.0038) (0.0047) (0.0064) (0.0083) (0.0119) (0.0157) (0.0063) docc223 0.0004 0.0048 -0.0232 -0.0976 -0.1882 -0.2705 -0.3059 -0.1362 (0.0026) (0.004) (0.005) (0.0068) (0.0088) (0.0127) (0.0167) (0.0067) docc224 -0.0246 -0.0455 -0.111 -0.1888 -0.2996 -0.3836 -0.4249 -0.2361 (0.0025) (0.004) (0.0049) (0.0067) (0.0086) (0.0126) (0.0167) (0.0066) docc225 -0.029 -0.0449 -0.159 -0.2025 -0.1863 -0.3722 -0.4712 -0.2377 (0.0118) (0.019) (0.0238) (0.0324) (0.0419) (0.0599) (0.0773) (0.032) docc226 -0.0061 -0.0103 -0.0426 -0.1122 -0.1974 -0.2645 -0.2983 -0.1425 (0.0023) (0.0037) (0.0046) (0.0063) (0.0081) (0.0117) (0.0153) (0.0062) d rmsize22 0.0177 0.0413 0.0772 0.108 0.1397 0.1382 0.1291 0.1394 (0.0012) (0.0019) (0.0024) (0.0032) (0.0042) (0.006) (0.0079) (0.0032) d rmsize23 0.0564 0.1112 0.2072 0.2666 0.278 0.2693 0.2482 0.2823 (0.0013) (0.002) (0.0023) (0.003) (0.0038) (0.0056) (0.0074) (0.003) constant 6.1322 5.9862 5.8201 5.8754 5.9159 5.962 6.0015 5.6694 (0.0068) (0.0104) (0.0131) (0.0183) (0.0245) (0.0367) (0.0493) (0.018)

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table 6: Quantile Regressions by Gender(Basic Control, Industry, Occupation,Firm Size)

FEMALE (n=40298)

 FEMALE 5th 10th 25th 50th 75th 90th 95th OLS age 0.0042 0.0079 0.0198 0.0325 0.0426 0.0563 0.0659 0.0485 (0.0004) (0.0007) (0.0013) (0.0018) (0.0031) (0.0042) (0.0055) (0.002) age2 -0.0001 -0.0001 -0.0002 -0.0004 -0.0004 -0.0005 -0.0006 -0.0005 (0) (0) (0) (0) (0) (0.0001) (0.0001) (0) tenure -0.003 -0.0001 0.02 0.0372 0.0478 0.0434 0.0408 0.0362 (0.0004) (0.0006) (0.001) (0.0012) (0.0019) (0.0026) (0.0033) (0.0013) Tenure2 0.0752 0.1042 0.035 -0.0592 -0.1186 -0.1079 -0.0994 -0.0749 (0.0017) (0.0028) (0.0047) (0.0056) (0.0093) (0.0126) (0.0166) (0.0062) HS 0.01 0.0221 0.0443 0.0944 0.1318 0.1633 0.1682 0.1229 (0.0017) (0.0029) (0.0055) (0.0072) (0.0119) (0.015) (0.0187) (0.008) Voc HS 0.0155 0.031 0.0774 0.1602 0.1868 0.2063 0.2084 0.1765 (0.0023) (0.0037) (0.0071) (0.0094) (0.0154) (0.0197) (0.025) (0.0104) College 0.0301 0.0675 0.1899 0.3671 0.506 0.6663 0.7052 0.4154 (0.002) (0.0033) (0.0062) (0.008) (0.0131) (0.0168) (0.0212) (0.0089) dind1 0.042 0.073 0.1901 0.2263 0.2327 0.2587 0.205 0.2437 (0.0062) (0.0103) (0.0195) (0.0261) (0.0429) (0.0548) (0.0664) (0.0291) dind2 0.0194 0.0464 0.1173 0.1589 0.2404 0.3076 0.3237 0.1975 (0.0032) (0.0054) (0.0101) (0.0133) (0.022) (0.0283) (0.0353) (0.0148) dind4 0.0095 0.0086 -0.0068 0.0121 -0.0172 -0.0181 -0.0101 -0.028
 FEMALE 5th 10th 25th 50th 75th 90th 95th OLS (0.0059) (0.01) (0.0192) (0.0256) (0.0425) (0.0539) (0.066) (0.0286) dind5 0.0129 -0.0236 0.0327 0.026 -0.0331 -0.1581 -0.2803 -0.0735 (0.0119) (0.0184) (0.0388) (0.0518) (0.0857) (0.1075) (0.134) (0.0581) dind6 0.0208 0.0335 0.0991 0.1617 0.1945 0.3298 0.2919 0.2075 (0.0042) (0.007) (0.0133) (0.0176) (0.0289) (0.037) (0.046) (0.0196) dind7 0.0541 0.086 0.4419 0.66 0.8778 0.932 0.8171 0.677 (0.0085) (0.0146) (0.0278) (0.0369) (0.0611) (0.0781) (0.0946) (0.0412) dind8 0.0333 0.0664 0.1566 0.2156 0.307 0.3362 0.3403 0.2897 (0.0039) (0.0068) (0.0127) (0.0169) (0.0278) (0.0352) (0.0438) (0.0189) dind9 0.023 0.0412 0.098 0.1221 0.1924 0.2115 0.2992 0.1702 (0.0048) (0.008) (0.0152) (0.0202) (0.0336) (0.043) (0.0534) (0.0226) dind10 0.0308 0.051 0.1164 0.1774 0.2089 0.2399 0.1712 0.1942 (0.0044) (0.0072) (0.014) (0.0186) (0.0305) (0.0388) (0.0484) (0.0207) dind11 0.0214 0.0511 0.1095 0.2005 0.2712 0.3196 0.3253 0.2347 (0.0038) (0.0065) (0.0124) (0.0165) (0.0272) (0.0344) (0.043) (0.0184) dind12 0.0187 0.0272 0.0635 0.1066 0.1599 0.1799 0.1599 0.1456 (0.0045) (0.0076) (0.0145) (0.0192) (0.0315) (0.0404) (0.0501) (0.0214) dind13 0.0274 0.0496 0.1041 0.1756 0.2059 0.1972 0.2201 0.1981 (0.0035) (0.0058) (0.0107) (0.0142) (0.0235) (0.0299) (0.0378) (0.0158) dind14 0.028 0.0475 0.1161 0.2084 0.2805 0.3074 0.2972 0.2481 (0.0048) (0.0081) (0.0154) (0.0205) (0.0336) (0.0425) (0.0533) (0.0228) dind15 0.0129 0.0247 0.0423 0.0463 0.0239 -0.0116 -0.0445 0.0257 (0.0048) (0.0082) (0.0158) (0.0211) (0.0348) (0.0445) (0.0556) (0.0235) dind16 0.2692 0.301 0.3305 0.3781 0.3433 0.3572 0.3396 0.3211 (0.0045) (0.0073) (0.0137) (0.0183) (0.0302) (0.0391) (0.0482) (0.0204) dind17 0.0137 0.027 0.0518 0.0877 0.1753 0.2199 0.1967 0.1196 (0.0038) (0.0063) (0.0121) (0.0161) (0.0265) (0.0339) (0.0422) (0.0179) dind18 0.0184 0.0397 0.0891 0.1473 0.2269 0.2528 0.2406 0.1975 (0.0022) (0.0037) (0.007) (0.0092) (0.015) (0.019) (0.0245) (0.0103) dind19 0.0094 0.0204 0.0605 0.0986 0.1409 0.1735 0.1408 0.1107 (0.0032) (0.0054) (0.0104) (0.0138) (0.0226) (0.029) (0.0369) (0.0154) dind20 0.0305 0.068 0.1589 0.2831 0.3474 0.4265 0.4493 0.2967 (0.0027) (0.0045) (0.0084) (0.011) (0.0179) (0.0227) (0.0288) (0.0122) dind21 0.2375 0.329 0.4329 0.4665 0.469 0.4813 0.4388 0.4553 (0.0028) (0.0045) (0.0082) (0.0105) (0.0169) (0.0216) (0.0275) (0.0117) dind22 0.0336 0.0668 0.1551 0.2641 0.4616 0.613 0.6083 0.3895 (0.0027) (0.0045) (0.0086) (0.0113) (0.0184) (0.0234) (0.0296) (0.0126) dind23 0.0109 0.0199 0.0651 0.0633 0.0756 0.0926 0.0477 0.0789 (0.004) (0.0067) (0.0127) (0.0165) (0.0265) (0.0345) (0.0439) (0.0184) dind24 0.0114 0.0294 0.0911 0.1427 0.1779 0.2158 0.1886 0.1696 (0.0033) (0.0055) (0.0102) (0.0134) (0.0217) (0.027) (0.0355) (0.0149) dind25 0.0278 0.0633 0.1341 0.2381 0.2454 0.2559 0.2475 0.2352 (0.0039) (0.0064) (0.0123) (0.0163) (0.0269) (0.0342) (0.0432) (0.0182) docc21 0.6208 0.2887 0.0176 -0.1314 -0.1383 -0.7082 -1.0388 -0.1245 (0.0123) (0.0278) (0.087) (0.1979) (0.1917) (0.1479) (0.1362) (0.2692) docc22 0.0246 0.0866 0.2322 0.4453 0.5795 0.5087 0.505 0.3675 (0.0024) (0.004) (0.0075) (0.01) (0.0166) (0.0214) (0.0267) (0.0112) docc23 0.0248 0.0508 0.0638 0.144 0.206 0.2506 0.1837 0.1142 (0.0067) (0.0109) (0.0213) (0.0284) (0.0469) (0.0601) (0.0717) (0.0317) docc24 0.0353 0.1041 0.2638 0.2679 0.2587 0.1137 0.0831 0.1937 (0.0034) (0.0056) (0.0107) (0.0144) (0.0239) (0.0307) (0.0383) (0.016) docc25 0.0742 0.1541 0.344 0.2805 0.2589 0.338 0.27 0.2409
 FEMALE 5th 10th 25th 50th 75th 90th 95th OLS (0.0046) (0.0075) (0.0143) (0.0187) (0.0307) (0.0383) (0.0492) (0.0208) docc26 0.0085 0.0188 -0.0084 0.0122 -0.0372 -0.1465 -0.1602 -0.0198 (0.0044) (0.0071) (0.0136) (0.0177) (0.0284) (0.0372) (0.0469) (0.0197) docc27 0.0018 0.0039 0.0377 0.1044 0.1107 0.0603 0.0483 0.0683 (0.0023) (0.0039) (0.0074) (0.0099) (0.0164) (0.0212) (0.0264) (0.011) docc28 0.0024 0.0082 -0.0009 -0.0148 -0.098 -0.2034 -0.2142 -0.0412 (0.0027) (0.0046) (0.0088) (0.0119) (0.0198) (0.0254) (0.0321) (0.0132) docc29 0.008 0.0286 0.0282 0.0336 0.0528 -0.0404 -0.0672 0.0082 (0.0041) (0.0068) (0.0124) (0.0165) (0.0268) (0.0339) (0.0443) (0.0184) docc210 0.0101 0.01 -0.0469 -0.0843 -0.0431 -0.0516 -0.0151 -0.0393 (0.0098) (0.0173) (0.0335) (0.0437) (0.0716) (0.0902) (0.109) (0.0489) docc212 -0.0029 -0.0056 -0.0356 -0.0976 -0.169 -0.2234 -0.2383 -0.1226 (0.0018) (0.003) (0.0056) (0.0075) (0.0123) (0.0159) (0.0196) (0.0083) docc213 0.0076 0.0073 -0.0219 -0.1147 -0.211 -0.2766 -0.3141 -0.1366 (0.0024) (0.0038) (0.0071) (0.0093) (0.0152) (0.0193) (0.024) (0.0104) docc214 -0.0032 -0.0009 -0.0302 -0.109 -0.2319 -0.3169 -0.3409 -0.1557 (0.0031) (0.0053) (0.0101) (0.0136) (0.0226) (0.0298) (0.0373) (0.0152) docc215 -0.0057 -0.0109 -0.0424 -0.1212 -0.2224 -0.2479 -0.2979 -0.1517 (0.0028) (0.0047) (0.0088) (0.0117) (0.0194) (0.0248) (0.0309) (0.013) docc216 0.0298 0.0402 -0.0947 -0.2299 -0.4285 -0.6018 -0.7572 -0.3061 (0.015) (0.0252) (0.0477) (0.0644) (0.1064) (0.1211) (0.1709) (0.0725) docc217 -0.0007 -0.0103 -0.0594 -0.1489 -0.2433 -0.2923 -0.3628 -0.1629 (0.0084) (0.0139) (0.0268) (0.0357) (0.0596) (0.0764) (0.0891) (0.04) docc218 0.0016 0.0007 -0.0388 -0.013 -0.1105 -0.2207 -0.2765 -0.0926 (0.0044) (0.0074) (0.014) (0.0187) (0.0311) (0.0401) (0.0496) (0.0208) docc219 -0.0127 -0.0074 -0.0526 -0.1323 -0.2526 -0.3306 -0.4513 -0.1871 (0.0051) (0.0087) (0.0164) (0.0218) (0.0361) (0.0461) (0.0579) (0.0243) docc220 -0.0034 -0.0056 -0.0329 -0.087 -0.1962 -0.2861 -0.3576 -0.1298 (0.0029) (0.005) (0.0094) (0.0124) (0.0202) (0.0252) (0.0319) (0.0138) docc221 0.0121 0.0223 -0.0259 -0.0657 -0.0959 -0.0784 -0.1123 -0.0868 (0.0077) (0.0133) (0.0254) (0.0342) (0.0561) (0.071) (0.085) (0.0382) docc222 0.0018 0.001 -0.0319 -0.0919 -0.2036 -0.318 -0.3808 -0.1417 (0.0029) (0.005) (0.0095) (0.0127) (0.0207) (0.0265) (0.0337) (0.0141) docc224 -0.0115 -0.0187 -0.0775 -0.1804 -0.3615 -0.5226 -0.5936 -0.2887 (0.0029) (0.0046) (0.0087) (0.0114) (0.019) (0.0248) (0.0309) (0.0127) docc225 -0.0027 -0.0301 -0.1741 -0.3799 -0.5409 -0.2951 -0.4245 -0.2953 (0.0212) (0.0354) (0.067) (0.0897) (0.1493) (0.1924) (0.2402) (0.1022) docc226 -0.0044 -0.0047 -0.0382 -0.1062 -0.2156 -0.2692 -0.3241 -0.1381 (0.0033) (0.0055) (0.0105) (0.014) (0.0231) (0.0297) (0.0368) (0.0156) d rmsize22 0.0122 0.0283 0.0698 0.0984 0.1182 0.1161 0.1368 0.1421 (0.0013) (0.0022) (0.0041) (0.0055) (0.0091) (0.0118) (0.0147) (0.0061) d rmsize23 0.0302 0.0677 0.1557 0.2125 0.2202 0.2008 0.1861 0.2438 (0.0014) (0.0023) (0.0041) (0.0053) (0.0088) (0.0114) (0.0141) (0.0058) constant 6.168 6.0636 5.7871 5.6103 5.6068 5.6017 5.6167 5.2995 (0.0071) (0.0118) (0.0227) (0.0314) (0.0556) (0.0749) (0.0985) (0.0349)

Note: Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

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## References

Anker, Richard (1998) Gender and Jobs: Sex Segregation of occupations in the world. International Labor Office Geneva. Page 16.

Moro, Andrea. Statistical Discrimination. Link: http://www.andreamoro.net/perm/papers/palgrave-statistical-discrimination.pdf

Palaz, Serap (2002) Discrimination against women in Turkey: a review of the theoretical and empirical literature. Balikesir Universitesi, Bandirma I.I.B.F. Page 4.

Internet  article  from  the  site  of  RTBot.  Statistical  Discrimination  (Economics).  Link:

http://www.rtbot.net/Statistical_discrimination_(economics)

George J. Barjos (2010) Labor Economics. McGrew Hill International Edition-Singapore. Fifth Edition. Page 380.

Schwab, Stewart (1986) Is Statistical Discrimination Efficient? . Cornell law Faculty Publications. PageLink: http://scholarship.law.cornell.edu/cgi/viewcontent.cgi?article=1026&context=facpub

Ronald G. Ehrenberg-Robert S. Smith (2012) Modern Labor Economics: Theory and Public Policy. Boston-Pearson publication. Eleventh edition. Page 420.

George J. Barjos (2010) Labor Economics. McGrew Hill International Edition-Singapore. Fifth Edition. Page 402.

Reich, Michael—Gordon, M. David—Edwards C. Richard (1973) Dual Labor Market: A Theory of Labor Market Segmentation. Page 359-365.

Internet article from the site of Eurofound (14 August, 2009) Dual Labour Market. Link:

http://www.eurofound.europa.eu/emire/IRELAND/DUALLABOURMARKET-IR.htm

Palaz, Serap (2002) Discrimination against women in Turkey: a review of the theoretical and empirical literature. Balikesir Universitesi, Bandirma I.I.B.F. Page 3.

Parlaktuna, Inci (2010) Analysis of Gender-based Occupational Discrimination in Turkey.Ege Academic Review. Volume 10, Number 4, October 2010