Table 4. Regression results and diagnostics. Coefficients with standard errors in parantheses. Bold estimates have 95% confidence or greater. Max variance inflation factors (VIF) is the largest variance inflation factor for any predictor in the given model. A VIF of 10 or greater is considered an indication of multicolinearity (Fox and Monette 1992). ^{†}A randomeffects poisson was overdispersed (dev/df = 2.17; Lindsey 1999), while a negative binomial model without village was not (dev/df = 1.29). Villagelevel variance were calculated with a mixed effects poisson. ^{‡}The diversity predictor variable used for workdays was number of castes with 10 or more households.



Workdays 
Adequacy 
Fairness 

Family 
negative
binomial^{†} 
binomial 
binomial 
Intercept 
1.58 (0.83) 
3.43
(1.03) 
2.45 (1.31) 
Villagelevel 
LNPOP 
0.77
(0.15) 
0.25 (0.13) 
0.57
(0.19) 
DISTANCE 
0.01
(0.01) 
0.00 (0.01) 
0.02
(0.01) 
CASTES^{‡} 
0.26
(0.08) 
0.16
(0.06) 
0.11 (0.09) 
WEALTHGINI 
0.03
(0.01) 
0.05
(0.02) 
0.11
(0.02) 
Householdlevel 



AGE 
0.00 (0.00) 
0.00 (0.00) 
0.00 (0.01) 
EDUC 
0.01 (0.02) 
0.01 (0.02) 
0.02 (0.02) 
HHSIZE 
0.06 (0.04) 
0.06 (0.05) 
0.15
(0.06) 
LNWEALTH 
0.01 (0.04) 
0.01 (0.04) 
0.22
(0.06) 
FRAC 
1.14
(0.44) 
0.27 (0.44) 
2.56
(0.64) 
DALIT 
0.03 (0.23) 
0.15 (0.25) 
1.24
(0.58) 
CHANNEL 
0.38
(0.17) 
0.96
(0.18) 
3.36
(0.28) 
DALIT*CHANNEL 
0.12 (0.31) 
0.36 (0.33) 
1.95
(0.67) 
Village
variance 



Full Model 
0 
0.001 
0

Model Fit &
Diagnostics 



ML
pseudoR² 
0.22 
0.28 
0.94 
DF 
241 
243 
243 
Deviance 
310 
143 
473 
Max VIF 
7.9 
7.2 
7.2 


