Table 4.

Average marginal effects from multinomial logistic regression model for support for foreign funding of election-monitoring NGOs.

	Support		Indifferent		Oppose
	AME	SE	AME	SE	AME**	SE
Education (general secondary)
University	−.016	.022	−.098**	.027	.114**	.029
Specialized sec.	−.040*	.023	−.036	.027	.076	.029
Lower vocational	−.011	.033	.068*	.039	−.056	.044
Less than sec.	−.040	.056	−.068	.067	.108*	.064
Woman	.008	.015	.060**	.019	−.068**	.020
Age(−18)	.000	.001	−.002	.001	.002*	.001
Income quintile (bottom)
Second quintile	−.005	.025	.047	.031	−.042	.033
Third quintile	.047*	.027	.029	.031	−.076**	.033
Fourth quintile	.034	.028	−.041	.033	.007	.036
Top quintile	.088**	.036	.010	.040	−.098**	.044
Income missing	.014	.025	.121**	.033	−.136**	.034
Work status (working for hire)
Self-employed	.014	.038	−.003	.045	−.011	.045
Military/Police	−.421**	.159	.135	.130	.286**	.133
Not working	−.005	.017	−.015	.020	.020	.021
Locality type (medium city or small town)
Moscow	.136**	.024	.088**	.033	−.224**	.036
St.Petersburg	.060	.039	−.038	.056	−.021	.062
Other large city	−.012	.021	−.017	.026	.030	.027
Rural/village	−.004	.020	−.023	.024	.027	.025
Household size	−.020**	.006	−.016**	.008	.036**	.008
2015 vs. 2012	.068**	.016	.086**	.020	−.153**	.022

^*p < .05, one-tailed.

^**p < .05, two-tailed.

AME = Average marginal effect. SE = Standard error.