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Table 2 OLS analysis and quantile regression estimation for model 1

From: Does digital technology reduce health disparity? Investigating difference of depression stemming from socioeconomic status among Chinese older adults

Variables

Dependent variable: depression (sample n = 8853)

OLS

Quantile regression

0.5

0.6

0.7

0.8

0.9

(1)

(2)

(3)

(4)

(5)

(6)

Individual socioeconomic status

 Father’s education

−0.197

−0.181

−0.344**

−0.175

−0.093

− 0.098

(0.132)

(0.145)

(0.170)

(0.194)

(0.243)

(0.294)

 SHR-16

−0.444***

− 0.460***

− 0.486***

−0.521***

− 0.649***

− 0.676***

(0.055)

(0.061)

(0.073)

(0.086)

(0.102)

(0.121)

 Education

−0.739***

−0.730***

− 0.873***

−1.080***

−1.670***

−0.776*

(0.174)

(0.250)

(0.253)

(0.373)

(0.320)

(0.413)

 Income

−0.665***

−0.628***

− 0.753***

−0.865***

− 1.100***

−1.280***

(0.046)

(0.055)

(0.060)

(0.075)

(0.090)

(0.106)

 Hukou

−0.348**

− 0.310*

− 0.347*

− 0.505**

−0.283

− 0.365

(0.164)

(0.164)

(0.198)

(0.220)

(0.293)

(0.344)

Other

 Age

0.018**

0.011

0.015

0.013

0.017

0.041**

(0.007)

(0.008)

(0.009)

(0.011)

(0.013)

(0.016)

 Gender

−1.330***

−1.290***

−1.400***

− 1.980***

−2.230***

−2.660***

(0.133)

(0.157)

(0.181)

(0.222)

(0.249)

(0.299)

 Marital

−1.400***

−1.380***

− 1.490***

−1.920***

− 2.000***

− 2.680***

(0.161)

(0.218)

(0.235)

(0.308)

(0.294)

(0.409)

 Constant

16.100***

15.000***

17.800***

21.600***

26.700***

30.700***

(0.744)

(0.874)

(0.969)

(1.210)

(1.410)

(1.680)

 Observations

8853

8853

8853

8853

8853

8853

 R2

0.110

     

 Pseudo R2

 

0.595

0.596

0.595

0.606

0.601

  1. a standardize coefficients are reported; standard errors in parentheses
  2. b ***p < 0.01, **p < 0.05, *p < 0.1