Table 2 presents average wage by occupation, gender, and urban/rural division. On average, individuals earn RMB 28,658 in 2011. Mean wages of reported occupations had a wide range. From high to low of the annual wage, they were Senior Professional and Manager (RMB 42,081); Junior Professional and Officer (RMB 35,741); Skilled Workers (RMB 29,264); Normal Workers (RMB 21,674); Farmer, Fisherman, Hunter (RMB 8,788), respectively. Recall gender differences in occupational distribution, women had higher percentage in the high earning occupations which were Senior Professional and
Men Women Men Women
Observations 3997 57.7% 42.3% 47.4% 52.6% 56.5% 43.5% 58.8% 41.2% Marital Status Married 87.4% 88.0% 86.5% 84.0% 90.3% 85.7% 81.8% 89.9% 90.9% Not Married 12.6% 12.0% 13.5% 16.0% 9.7% 14.3% 18.2% 10.1% 9.1% Age 41 43 39 41 42 43 38 44 40 Years of Education 11.2 11.1 11.4 12.3 10.2 12.1 12.5 10.2 10.2 T-test Occupations
Senior Professional and Manager 26.4% 25.4% 27.8% 32.3% 21.1% 29.9% 35.5% 21.6% 20.5%
Junior Professional and Officer 11.6% 9.0% 15.2% 14.3% 9.2% 11.5% 18.0% 6.9% 12.6%
Skilled Workers 16.0% 22.6% 6.9% 14.9% 17.0% 22.9% 4.4% 22.2% 9.4%
Normal Workers 31.3% 28.2% 35.6% 29.5% 32.8% 26.7% 33.3% 29.4% 37.7%
Farmer, Fisherman, Hunter 9.9% 10.2% 9.6% 2.7% 16.4% 2.9% 2.4% 16.4% 16.4%
Others 4.8% 4.7% 4.9% 6.2% 3.5% 6.0% 6.5% 3.6% 3.4%
Chi2 Test
Involved in Housework (last week)
Buying Food 56.30% 43.3% 74.2% 50.0% 47.3% 45.3% 73.2% 41.5% 75.2%
Preparing Food 48.50% 32.6% 70.3% 48.0% 46.1% 38.0% 65.5% 28.0% 74.9%
Washing Clothes 47.00% 21.4% 82.0% 54.4% 51.5% 25.5% 77.2% 17.8% 86.4%
Cleaning House 52.90% 30.8% 83.0% 57.3% 55.4% 34.8% 80.0% 27.3% 86.0%
Cared for Children 16.60% 14.00% 20.1% 15.3% 17.8% 13.7% 17.4% 14.2% 22.7%
Number of housework involved 2.2 1.4 3.3 2.2 2.2 1.6 3.1 1.3 3.5
T-test
Urban Rural
Percentage Men Women
Total
Variables Urban Rural
0.3* 2.1*** 0.4** 0.0
197.6*** 274.3*** 131.0*** 78.0***
Manager and Junior Professional and Officer. But women also had much low proportion working as the middle level paid occupation – Skilled Workers.
In the year of 2011, the average earning of women was 84% of that of men and women. The wage gap was RMB 4,965. The average earning difference between rural and urban was larger than the gender wage difference. Specifically, average earning in rural areas was 71% of that in urban and the mean earning in rural China was
significantly RMB 9,713 less than urban China in China. In rural China, the gender wage gap was more serious and women got 74% of the earning of men. While this gap in urban China was less than the average level, women’s earing was 91% of men’s. The earning in rural areas was 71% of that in urban and the mean earning in rural China was
significantly RMB 9,713 less than urban China, which became the most obvious wage gap in all comparisons. More importantly, women paid less than men crossing all the occupations in both rural and urban China except the Farmer Fisherman Hunter in urban areas. Specifically, women received RMB 4,440 less than men in Senior Professional and Manager (with significance level at 0.08); RMB 4,160 less in Junior Professional and Officer; RMB 9,056 significant less in Skilled Workers; RMB 4,933 significant less in Normal Workers; RMB 3,765 significant less in Farmer, Fisherman, Hunter; RMB 11,562 significant less in the other occupations.
Table 2. Average Wage by Occupation, Gender, and Urban/Rural Division
Note: *p<0.05; **p<0.01; ***p<0.001.
ii. Correlation Analysis
Table 3 shows the correlation analysis results of household work involvement and ln(wage) by gender. It suggests that females’ wages negatively related to household duties involvement and that relationship is statistically significant, while males’ wage, were not significantly associated with household duties involvement.
Senior Professional and Manager Junior Professional and Officer Skilled Workers Normal Workers Farmer, Fisherman , Hunter Others Total ¥28,658.2 ¥42,081.0 ¥35,741.4 ¥29,263.7 ¥21,674.8 ¥8,787.8 ¥27,973.1 By Gender Men ¥30,758.8 ¥44,057.8 ¥38,030.6 ¥30,914.2 ¥24,031.4 ¥10,314.7 ¥32,954.7 Women ¥25,793.5 ¥39,618.0 ¥33,870.2 ¥21,828.0 ¥19,098.1 ¥6,550.1 ¥21,392.5 Ratio 0.84 0.90 0.89 0.71 0.79 0.64 0.65 Gap -0.16 -0.10 -0.11 -0.29 -0.21 -0.36 -0.35 T test 4965.3*** 4439.70.08 4,160.4 9056.2*** 4933.3*** 3764.5** 11562.2*
By Urban and Rural
Urban ¥33,766.2 ¥47,869.7 ¥35,318.5 ¥30,031.4 ¥24,158.6 ¥9,201.7 ¥31,003.7 Men ¥35,208.6 ¥49,944.2 ¥35,789.1 ¥31,139.4 ¥26,437.1 ¥7,901.2 ¥35,895.9 Women ¥31,895.4 ¥45,576.8 ¥34,923.1 ¥22,370.3 ¥21,760.7 ¥11,323.7 ¥25,076.7 Ratio 0.91 0.91 0.98 0.72 0.82 1.43 0.70 Gap -0.09 -0.09 -0.02 -0.28 -0.18 0.43 -0.30 T test 3313.3* 4,367.4 866.0 8769.10.056 4676.4* -3,422.5 10,819.2 Rural ¥24,053.2 ¥34,240.5 ¥36,322.1 ¥28,663.0 ¥19,687.7 ¥8,727.3 ¥23,198.8 Men ¥26,906.6 ¥37,087.0 ¥41,221.4 ¥30,715.3 ¥22,169.2 ¥10,685.2 ¥28,743.4 Women ¥19,980.6 ¥29,913.7 ¥32,466.2 ¥21,633.9 ¥16,890.3 ¥5,902.4 ¥14,786.2 Ratio 0.74 0.81 0.79 0.70 0.76 0.55 0.51 Gap -0.26 -0.19 -0.21 -0.30 -0.24 -0.45 -0.49 T test 6926.0*** 7173.3* 8755.1 9081.4** 5278.9*** 4782.7*** 13957.2* Ratio of Urban and Rural 0.71
Gap of Urban and Rural -0.29 T test of Urban and Rural 9713.0***
Average wage
Table 3. Correlation of Wage and Household Work by Gender
iii. Stepwise Regression Model
Table 4 demonstrates the regression results of the stepwise regression models. In model 1(the base model), the independent variables are gender (as primary interested variable), demographic information and working hour (as the control variables). Based on model 1, other wage indicators (education, occupation, time usage, regions) are added as explanatory variables in model 2. Based on model 2, the interaction term “gender and region” is introduced into model 3. In model 1, independent variables only explained 9% of the variance on ln(wage). After adding the wage determinants, a greater percentage of the variance on ln(wage) was explained by all of the independent variables in model 2 (R2 =38%). In model 3, the R2 remained around 38%, which suggests that the interaction term did not influence the proportion reduction error.
The results of model 1 suggest that the female wage was significantly 24.35% less than the male wage by controlling the age, marital status, and working hours. However,
females and males may have different characteristics in wage determinants (education, occupations, household involvements and regions), and those difference could cause the gender wage difference as well. By considering those wage determinant variables into the model, the size of gender wage gap might reduce or disappear
Lnwage Sig.
Male
Household Work Involvement 0.014 0.499
Female
The next step was to adding the wage indicators in model 2, which suggests that education is positively related to wage. Specifically, a one year increase in education associated with a 6.72% increase in wage while holding all the other variables constant in this model. Additionally, Household duties involvement negatively affected wage. One more household task involvement related to 2.86% decrease in wage. Moreover,
occupation difference also caused wage difference. To be specific, “Junior Professional and Officer”, “Skilled workers”, “Normal Workers”, “Farmer Fishman Hunter” were paid significantly less than “Senior Professional and Manager”. Comparing to “Senior Professor and Manager”, “Junior Professor and Officer” earned 10.95% less, “Skilled workers” earned 17.55% less, ‘Normal Workers” earned 34.10% less, “Farmer Fishman and Hunter” earned 79.38% less.
Comparing to the results indicated in model 1, the gender wage gap reduced by
approximately 6% by controlling all the wage factors (education, occupations, household involvements, regions) in model 2. However, females were still paid significantly 18.78% less than males after considering individuals’ age, marital status, working hours,
education, occupations, household work involvements and regions, which indicated that the gender discrimination existed in the Chinese labor market.
Based on model 2, model 3 added the interaction term “gender and region” in order to compare the size of gender wage gaps between rural China and urban China. As
aforementioned in the method section (the coefficient of gender variable determine the size of gender wage gap in urban China. The coefficient of gender variable and the coefficient of interaction term determine the size of the gender wage gap in rural China), females’ wages were 12.19% less than males’ wages in urban areas. However, the gender
wage gap in rural areas was much larger than this gap in urban areas as female’s wages were 24.9% [(eβ1+β13-1)*100 =0.878 * 0.855 *100= 24.9%] less than that of males in rural areas. In other words, the gender wage gap in rural areas was 12.7% [eβ1 * (eβ13-1)*100 = 0.878 *0.14*100) =12.7%] larger at significance level than that in urban areas by
controlling all the other explanatory variables. This indicated that gender discrimination in rural areas was more serious than that in urban areas.
Table 4. The Results of the Stepwise Regression Models
Note: *p<0.05; **p<0.01; ***p<0.001.
iv. Prediction
Based on the results of Model 3, Table 5 presented the predicted annual wage by gender, urban/rural division, and occupations under the conditions: the population are all married; age, working hours, education, domestic duties involvement took the
corresponding sample mean value (age =41 years old, working hour =8 hours, Education = 11 years, Domestic Duties Involvement =2.2). The predicted result represented that
β (eβ - 1)*100 β (eβ - 1)*100 β eβ *100 (eβ - 1)*100
Gender(female) -0.279*** -24.35 -0.208*** -18.78 -0.130** 87.81 -12.19
Working hour 0.143*** 15.37 0.040*** 4.08 0.040*** - 4.08
Age -0.013*** -1.29 -0.002 -0.20 -0.002 - -0.20
Mariral Status (Married) 0.117** 12.41 0.208*** 23.12 0.211*** - 23.49
Education - - 0.065*** 6.72 0.065*** - 6.72
Junior Professional and Officer - - -0.118* -11.13 -0.116* - -10.95
Skilled Workers - - -0.2*** -18.13 -0.193*** - -17.55
Normal Workers - - -0.42*** -34.30 -0.417*** - -34.10
Farmer, Fisherman, Hunter - - -1.579*** -79.38 -1.579*** - -79.38
Others - - -0.321*** -27.46 -0.321*** - -27.46
Domestic Duty Involvements - - -0.031*** -3.05 -0.029** - -2.86
Region(Rural) - - -0.16*** -14.79 -0.096** - -9.15
Female*Rural - - - - -0.156** 85.56 -14.44
Cons 9.259*** - 9.288*** - 9.251*** - -
R2 F
Variable Names Model 1 Model 2 Model 3
0.090 0.380 0.380
women were paid less than men in both rural and urban China, and women received less wage than men among all the occupation categories. The size of the gender wage gap in rural China is 12.7% more than that in urban China. All this predicted information was consistent with the aforementioned result.
Table 5. The Predicted Annual Wage by Gender, Urban/Rural Division, and Occupations
Note: The value was predicted under the condition that the population are all married; Age, Working hours, Education, Domestic Duties Involvement took the corresponding sample mean value (age =41 years old, working hour =8 hours, Education = 11 years, Domestic Duties Involvement =2.2).