[Retracted] How Does Digital Finance Affect People’s Income: Evidence from China
Table 2
The effects of digital financial development on residents’ income.
(1)
(2)
(3)
(4)
(5)
(6)
DF
0.0071
0.0057
0.0038
0.0022
0.0120
(0.0002)
(0.0002)
(0.0002)
(0.0002)
（0.0000）
Spherical distance to Hangzhou
−0.0170
（0.0003）
Age
0.0928
0.0919
0.1631
0.1184
1.2370
0.0860
(0.0039)
(0.0040)
(0.0044)
(0.0052)
(0.1063)
(0.0033)
Age squared
−0.0015
-0.0015
−0.0022
−0.0016
−0.0200
−0.0010
(0.0000)
(0.0000)
(0.0001)
(0.0001)
（0.0010）
（0.0000）
Gender
0.5424
0.5470
0.4751
0.3517
−5.1060
0.5740
(0.0160)
(0.0159)
(0.0184)
(0.0174)
（0.5397）
(0.0170)
Years of education
0.0759
0.0724
0.0461
0.0284
0.1380
0.0740
(0.0024)
(0.0024)
(0.0028)
(0.0028)
(0.0754)
(0.0023)
Urban
0.3845
0.3276
0.0616
0.0353
3.3010
0.3510
(0.0182)
(0.0185)
(0.0176)
(0.0171)
(0.5691)
(0.0178)
Married
0.2809
0.2808
0.1041
0.0665
−4.0620
0.2930
(0.0224)
(0.0224)
(0.0219)
(0.0212)
（0.7621）
(0.0234)
Rural household registration
−0.0192
-0.0152
−0.0014
−0.0054
0.2400
−0.0210
(0.0094)
(0.0080)
(0.0047)
(0.0052)
(0.1739)
（0.0054）
CPC
0.0511
0.1852
0.1641
0.1375
87.3870
−0.4000
(0.0235)
(0.0256)
(0.0255)
(0.0232)
0.5846
（0.0578）
Health levels
0.0859
0.0877
0.0714
0.0427
−0.5850
0.0890
(0.0077)
(0.0077)
(0.0074)
(0.0073)
（0.2417）
(0.0074)
Provincial fixed effects
No
Yes
Yes
Yes
Occupational fixed effects
No
No
Yes
Yes
Industry fixed effects
No
No
No
Yes
_cons
5.5599
5.9377
4.8680
6.4764
136.9770
5.0050
(0.0861)
(0.1115)
(0.1318)
(0.1657)
(2.5893)
(0.1035)
Observations
23293
23293
19411
14579
23,074
23,074
Adjusted R2
0.6235
0.6284
0.6672
0.3115
0.59
0.611
F-statistic
2336.7700
2336.7700
Wu-Hausman
75.6437
75.6437
0.0000
0.0000
,, and denote passing the 1%, 5%, and 10% significance tests, respectively; values in parentheses below the coefficients are robust standard errors; dependent variable indicators are logarithmic values of residents’ income, and regressions control for the province, occupation, and industry individual effects.
