Table 2.

Prais-Winsten Fixed Effects Models of Reported Hate Crime Incidence (estimates per one million individuals, standard errors in brackets)¹

	Base	Spatial Lag	Controls
Constitutional ban t-1	1.06 ^***	1.11 ^***	0.185
Constitutional ban t-1	[0.261]	[0.26]	[0.304]
Partner. reco.	0.278	0.317	0.812 ^a
Partner. reco.	[0.861]	[0.857]	[0.838]
Partner. reco. t-1	0.913	0.899	0.826 ^a
Partner. reco. t-1	[1.1]	[1.1]	[1.09]
Partner. reco. t-2	0.881	0.874	1.01 ^a
Partner. reco. t-2	[1.13]	[1.13]	[1.05]
Hate crime t-1 (HC)	−0.994 ^**	−0.893 ^**	−0.907^*
Hate crime t-1 (HC)	[0.344]	[0.342]	[0.376]
Emp. non-discrim.	−0.837	−0.675	−1.11 ^b
Emp. non-discrim.	[0.733]	[0.728]	[0.762]
Emp. non-discrim. t-1	−0.417	−0.5	−0.808 ^b
Emp. non-discrim. t-1	[0.707]	[0.703]	[0.734]
Spatial lag		0.32	0.309
Spatial lag		[0.207]	[0.192]
Racial hate crime			128678.7^***
Racial hate crime			[24587.4]
Religion hate crime			153269.5
Religion hate crime			[83652.1]
Disability hate crime			53361
Disability hate crime			[224167]
Violent crime inc.			−1118
Violent crime inc.			[570.8]
Property crime inc.			−63.9
Property crime inc.			[44.6]
Citizen ideology			0.026
Citizen ideology			[0.017]
Unemployment rate			−0.069
Unemployment rate			[0.083]
Democratic governor			0.325
Democratic governor			[0.524]
% Democratic legisl.			1.44
% Democratic legisl.			[1.99]
Government ideology			−0.005
Government ideology			[0.017]
Constant	5.63 ^***	4.09 ^***	7.05 ^**
Constant	[0.272]	[1.02]	[2.94]
N	637	637	637
Multiple Imputation			x

Spatial lag has a one-to-one relationship (not one-to-one million).

^a–b

These variables are jointly statistically significant using a Wald test. Because Prais-Winsten models (xtpcse) are not available using Stata’s mi ice suite, we calculate the Wald test independently for each imputed data set and consider a result significant if it is statistically significant at the p<0.05 level in at least two-thirds of the imputed data sets.

p<0.05,

^**

p<0.01,

^***

p<0.001