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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