Table 3 Multilevel regression coefficients for individual and aggregated factors on log income

Men

.190 (.013)***

.189 (.013)***

.188 (.013)***

Age

.003 (.001)***

.004 (.001)***

.003 (.001)***

Age squared

−.001 (.000)***

−.001 (.000)***

−.001 (.000)***

Majority

.048 (.034)

.056 (.033)^†

.063 (.033)^†

Married

.126 (.033)***

.127 (.033)***

.125 (.033)***

Urban hukou

.227 (.038)***

.220 (.041)***

.238 (.049)***

Household size

−.048 (.007)***

−.048 (.007)***

−.046 (.008)***

Education

.042 (.002)***

.041 (.002)***

.038 (.003)***

Has been sick

−.123 (.016)***

−.120 (.015)***

−.114 (.015)***

Normal BMI

.003 (.013)

.002 (.013)

.001 (.013)

 Working class

.106 (.059)^†

.097 (.057)^†

.041 (.055)

 Service class

.507 (.073)***

.508 (.073)***

.424 (.064)***

 Agriculture

−.650 (.079)***

−.646 (.080)***

−.690 (.069)***

 Collective

−.327 (.093)**

−.330 (.092)**

−.358 (.093)**

 Private

−.357 (.052)***

−.393 (.052)***

−.424 (.053)***

 Organisation

−.791(.102)***

−.791(.123)***

−.805(.109)***

 Others

−.956 (.123)***

−.951 (.124)***

−1.028 (.124)***

Working hour

.002 (.000)***

.002 (.000)***

.003 (.000)***

Privatisation

−.006 (.003)^†

−.011 (.004)**

−.014 (.003)***

Average pref. income

.072 (.027)*

.085 (.026)**

.062 (.016)**

Average pref. education

−.010 (.026)

−.001 (.025)

−.002 (.020)

% Han Chinese

−.264 (.099)**

−.230 (.095)*

−.148 (.070)*

Average household size

−.111 (.046)*

−.093 (.045)*

−.085 (.031)**

% Living in cities

−.129 (.267)

−.256 (.246)

−.030 (.168)

Education × privatisation

.000 (.000)

.000 (.000)

Working class × privatisation

.010 (.006)

.011 (.006)^†

Service class × privatisation

.005 (.007)

.009 (.007)

Private sector × privatisation

.010 (.005)^†

.008 (.005)

Hukou × privatisation

−.005 (.005)

−.001 (.006)

Intercept

7.751 (.046)***

8.232 (.097)***

8.223 (.099)***

8.290 (.091)***

Random effects

σ _u

.516 (.034)***

.221 (.020)***

.215 (.020)***

.858 (.019)***

σ _e

1.298 (.004)***

1.166 (.025)***

1.165 (.026)***

1.150 (.025)***

σ _education

.018 (.003)***

\( {\sigma}_{\mathsf{working}\ \mathsf{class}} \)

.423 (.045)***

\( {\sigma}_{\mathsf{service}\ \mathsf{class}} \)

.382 (.054)***

\( {\sigma}_{\mathsf{private}\ \mathsf{sector}} \)

.395 (.062)***

σ _hukou

.421 (.090)***

σ _intercept

.279 (.027)***

Education

.051 (.164)

Working class

−.539 (.085)***

Service class

−.583 (.104)***

Private sector

−.034 (.169)

Hukou

−.304 (.131)***

ICC

.136

.035

.033

.358

Log likelihood

−84011.38

−78577.15

−78525.45

−78120.29

 2LL

168022.76

157154.3

157050.3

156240.58

 2LL change

10868.46

−104

809.72

Snijders/Bosker R2 (level 1/2)

.278/.793

.281/.803

.281/.803

AIC

168028.8

157208.3

157114.9

156344.6

N (obs.)

49873

49873

49873

49873

N (groups)

128

128

128

128