Introduction
The educational advantage is the primary channel to increase human capital. At the micro level, does the increase in education level increase the population’s income to the same extent?
This paper selects data on the education index as well as the income index for six provinces or municipalities directly under the central government of China, namely Beijing, Guangzhou, Shanghai, Jiangsu, Yunnan, and Gansu, in 2019, and observe the years of schooling children aged six and the mean year’s schooling population aged 25+ for residents of these cities.
Setup
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Import data
/shared-libs/python3.7/py-core/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3258: DtypeWarning: Columns (35) have mixed types.Specify dtype option on import or set low_memory=False.
interactivity=interactivity, compiler=compiler, result=result)
10
AFG
Afghanistan
11
AFG
Afghanistan
12
AFG
Afghanistan
13
AFG
Afghanistan
14
AFG
Afghanistan
15
AFG
Afghanistan
16
AFG
Afghanistan
17
AFG
Afghanistan
18
AFG
Afghanistan
19
AFG
Afghanistan
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 54668 entries, 0 to 54667
Data columns (total 37 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 iso_code 54668 non-null object
1 country 54668 non-null object
2 year 54668 non-null int64
3 GDLCODE 54668 non-null object
4 level 54668 non-null object
5 region 54668 non-null object
6 continent 54668 non-null object
7 sgdi 54668 non-null object
8 shdi 54668 non-null float64
9 shdif 54668 non-null object
10 shdim 54668 non-null object
11 healthindex 54668 non-null float64
12 healthindexf 54668 non-null object
13 healthindexm 54668 non-null object
14 incindex 54668 non-null float64
15 incindexf 54668 non-null object
16 incindexm 54668 non-null object
17 edindex 54668 non-null float64
18 edindexf 54668 non-null object
19 edindexm 54668 non-null object
20 esch 54668 non-null float64
21 eschf 54668 non-null object
22 eschm 54668 non-null object
23 msch 54668 non-null float64
24 mschf 54668 non-null object
25 mschm 54668 non-null object
26 lifexp 54668 non-null float64
27 lifexpf 54668 non-null object
28 lifexpm 54668 non-null object
29 gnic 54668 non-null float64
30 gnicf 54668 non-null object
31 gnicm 54668 non-null object
32 lgnic 54668 non-null float64
33 lgnicf 54668 non-null object
34 lgnicm 54668 non-null object
35 pop 54668 non-null object
36 mfsel 54668 non-null int64
dtypes: float64(9), int64(2), object(26)
memory usage: 15.4+ MB
0
iso_code
System
1
country
System
2
year
System
3
GDLCODE
System
4
level
System
5
region
System
6
continent
System
7
shdi
Human Development
8
sgdi
Gender Development
9
shdif
Gender Development
Prepare data
54661
ZWE
Zimbabwe
54662
ZWE
Zimbabwe
54663
ZWE
Zimbabwe
54664
ZWE
Zimbabwe
54665
ZWE
Zimbabwe
54666
ZWE
Zimbabwe
Descriptive statistics
count
49606
49606
mean
0.64
0.74
std
0.17
0.15
min
0.17
0.07
25%
0.51
0.65
50%
0.66
0.78
75%
0.77
0.86
max
0.98
1
count
1953
1953
mean
0.7
0.8
std
0.16
0.12
min
0.23
0.43
25%
0.58
0.72
50%
0.72
0.82
75%
0.82
0.89
max
0.98
1
count
32
32
mean
0.75
0.87
std
0.05
0.04
min
0.61
0.76
25%
0.73
0.86
50%
0.75
0.87
75%
0.77
0.89
max
0.9
0.95
261
Afghanistan
2019
262
Afghanistan
2019
263
Afghanistan
2019
264
Afghanistan
2019
265
Afghanistan
2019
266
Afghanistan
2019
267
Afghanistan
2019
268
Afghanistan
2019
650
Angola
2019
651
Angola
2019
9212
China
2019
9213
China
2019
9214
China
2019
9215
China
2019
9216
China
2019
9217
China
2019
9218
China
2019
9219
China
2019
9220
China
2019
9221
China
2019
9212
China
2019
9220
China
2019
9221
China
2019
9230
China
2019
9236
China
2019
9239
China
2019
Exploratory data analysis
Regression analysis
Conclusion
The inequality of income distribution due to education in China is still relatively high in terms of unfair and unreasonable components.
We should focus on enhancing the inequality of educational opportunities.
We should promote the equal distribution of primary education and strengthen the policy support for compulsory education in poor areas.