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

From: Income inequality and privatisation: a multilevel analysis comparing prefectural size of private sectors in Western China

Fixed effects Model 1 Model 2 Model 3 Model 4
Coefficient (S.E.) Coefficient (S.E.) Coefficient (S.E.) Coefficient (S.E.)
Individual variables
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)
Occupation (ref. agriculture)
 Working class   .106 (.059) .097 (.057) .041 (.055)
 Service class   .507 (.073)*** .508 (.073)*** .424 (.064)***
Sector (ref. state)
 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)***
Prefecture variables
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)
Cross-level interaction
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 Variance component  
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)***
Correlation with the random intercept
Education     .051 (.164)
Working class     −.539 (.085)***
Service class     −.583 (.104)***
Private sector     −.034 (.169)
Hukou     −.304 (.131)***
Model fits
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
  1. Notes: ***p < .001; **p < .01; *p < .05; p < .01