[Retracted] How Does Digital Finance Affect People’s Income: Evidence from China
Table 3
Robustness test estimation results.
(1)
(2)
(3)
L. Coverage breadth
0.0021
(0.0002)
L. Use depth
0.0029
(0.0003)
L. Digitization level
0.0015
(0.0001)
Age
0.1185
0.1176
0.1188
(0.0052)
(0.0052)
(0.0052)
Age squared
−0.0016
−0.0016
−0.0016
(0.0001)
(0.0001)
(0.0001)
Gender
0.3512
0.3525
0.3500
(0.0174)
(0.0174)
(0.0174)
Years of education
0.0283
0.0288
0.0286
(0.0028)
(0.0028)
(0.0028)
Urban
0.0355
0.0353
0.0335
(0.0171)
(0.0172)
(0.0171)
Married
0.0655
0.0663
0.0674
(0.0212)
(0.0212)
(0.0212)
Rural household registration
−0.0053
−0.0053
−0.0055
(0.0052)
(0.0052)
(0.0051)
CPC
0.1584
0.1271
0.1420
(0.0220)
(0.0259)
(0.0225)
Health levels
0.0426
0.0425
0.0426
(0.0073)
(0.0073)
(0.0073)
Provincial fixed effects
Yes
Yes
Yes
Occupational fixed effects
Yes
Yes
Yes
Industry fixed effects
Yes
Yes
Yes
_cons
6.5102
6.3908
6.5663
(0.1652)
(0.1691)
(0.1637)
Observations
14579
14579
14579
Adjusted R2
0.3113
0.3102
0.3118
,, 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.
