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Table 2 Statistical descriptions

From: Impact of minimum wage on gender wage gaps in urban China

 

1995

2002

2007

 

Male

Female

F-M

Male

Female

F-M

Male

Female

F-M

Monthly wage

536

462

86.2%

1045

852

81.5%

1722

1271

73.8%

Education

 Elementary school or less

3.6%

5.6%

1.9%

2.2%

2.5%

0.4%

2.2%

2.2%

0.0%

 Junior high school

28.1%

32.7%

4.6%

24.3%

21.7%

−2.7%

20.4%

17.0%

−3.4%

 Senior high school

38.9%

44.2%

5.3%

37.7%

45.1%

7.4%

35.3%

40.4%

5.1%

 College

18.7%

12.5%

−6.2%

23.0%

23.0%

0.0%

24.5%

27.7%

3.2%

 University

10.7%

5.1%

−5.6%

12.8%

7.7%

−5.1%

17.6%

12.7%

−5.0%

Years of experience

28

27

−2

30

27

−3

31

28

−3

Han

95.4%

95.3%

0.0

96.0%

95.9%

−0.1%

97.4%

96.9%

−0.5%

Married

87.1%

87.9%

0.8%

89.1%

86.6%

−2.5%

88.4%

86.4%

−2.0%

Ownership

 SOE

86.2%

76.9%

−9.3%

70.0%

64.5%

−5.5%

59.4%

49.7%

−9.7%

 COE

12.0%

20.7%

8.7%

5.5%

9.0%

3.5%

5.1%

7.0%

1.9%

 Foreign/Private Firm

1.5%

1.7%

0.2%

23.4%

23.9%

0.5%

29.3%

30.3%

1.0%

 Others

0.2%

0.7%

0.5%

1.2%

2.6%

1.4%

6.2%

13.1%

6.8%

Occupation

 Manager

17.3%

6.3%

−11.0%

19.8%

9.3%

−10.5%

6.7%

2.8%

−4.0%

 Engineer

19.5%

22.2%

2.6%

17.8%

23.8%

6.0%

33.3%

37.5%

4.2%

 Clerical staff

22.5%

23.4%

0.9%

20.4%

22.8%

2.4%

20.5%

19.1%

−1.4%

 Manufacturingl worker

37.7%

41.4%

3.7%

32.6%

23.1%

−9.4%

23.9%

11.7%

−12.2%

 Others

2.9%

6.7%

3.8%

9.4%

21.0%

11.5%

15.6%

28.9%

13.3%

Industry

 Agriculture, forestry, fisheries

2.1%

1.3%

−0.8%

1.3%

1.3%

0.0%

1.0%

0.7%

−0.3%

 Manufacturing

43.3%

41.7%

−1.6%

26.4%

23.3%

−3.1%

22.5%

15.1%

−7.4%

 Mining

1.2%

0.9%

−0.3%

2.1%

0.8%

−1.3%

1.5%

0.6%

−1.0%

 Construction

3.3%

2.7%

−0.6%

4.2%

2.2%

−2.0%

4.1%

1.8%

−2.3%

 Transportation/communication

5.9%

4.3%

−1.6%

10.2%

5.1%

−5.1%

13.6%

6.5%

−7.1%

 Wholesale,retail and catering

12.1%

17.3%

5.2%

9.9%

15.4%

5.5%

11.1%

18.6%

7.5%

 Real estate

3.3%

4.0%

0.7%

6.0%

4.4%

−1.6%

7.7%

5.3%

−2.4%

 Health and Social Welfare

3.6%

5.8%

2.3%

4.0%

6.7%

2.7%

3.2%

5.6%

2.4%

 Education Arts and Culture

6.4%

8.0%

1.6%

8.4%

9.8%

1.3%

8.4%

10.8%

2.4%

 Technical Services

2.7%

2.1%

−0.6%

9.3%

15.6%

6.4%

2.6%

1.6%

−1.0%

 Financial Industry

1.8%

2.1%

0.3%

2.4%

3.0%

0.6%

2.9%

4.0%

1.1%

 Public administration and social organizations

13.6%

9.1%

−4.5%

13.6%

10.2%

−3.4%

15.1%

15.9%

0.8%

 Others

0.7%

0.5%

−0.1%

2.1%

2.2%

0.1%

6.4%

13.5%

7.1%

Samples

5002

4629

 

5473

4398

 

8272

6703

 
  1. Source: Calculated using CHIP1995, 2002 and 2007
  2. Notes:
  3. 1) the gender wage gaps = female wage mean values/male wage mean values
  4. 2) the gender gaps of another variables = female variable mean values-male variable mean values