[Retracted] How Does Digital Finance Affect People’s Income: Evidence from China
Table 5
The effects of digital financial development on residents’ income: grouped by industry in which residents are employed.
（1）
（2）
（3）
Primary industry
Secondary industry
Tertiary industry
DF
0.0046
0.0017
0.0023
(0.0021)
(0.0003)
(0.0002)
Age
0.1238
0.0893
0.1436
(0.0469)
(0.0082)
(0.0070)
Age squared
−0.0017
−0.0013
−0.0019
(0.0005)
(0.0001)
(0.0001)
Gender
0.2551
0.4558
0.3582
(0.1549)
(0.0225)
(0.0198)
Years of education
0.0685
0.0254
0.0547
(0.0202)
(0.0032)
(0.0032)
Urban
0.2340
0.0081
0.0775
(0.1614)
(0.0221)
(0.0257)
Married
0.2586
0.1533
0.0027
(0.2144)
(0.0317)
(0.0285)
Rural household registration
−0.0597
−0.0269
−0.0004
(0.1805)
(0.0035)
(0.0038)
CPC
−0.1416
0.1236
0.1880
(0.2677)
(0.0363)
(0.0300)
Health levels
0.0745
0.0488
0.0343
(0.0603)
(0.0100)
(0.0107)
Provincial fixed effects
Yes
Yes
Yes
_cons
6.3621
7.6002
6.0974
(1.1543)
(0.1940)
(0.1621)
Observations
239
6308
8079
Adjusted R2
0.3715
0.2223
0.3053
,, 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.
