Table 3.

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

	Base	Spatial Lag	Controls
Constitutional ban t-1	0.828 **	0.979 ***	0.178
	[0.251]	[0.235]	[0.315]
Policy one	−0.253	−0.074	−0.386
	[0.381]	[0.365]	[0.423]
Policy two t-1	−1.23 ***	−0.967 **	−1.750 **
	[0.304]	[0.33]	[0.531]
Policy three	0.469	0.531	0.695 a
	[0.553]	[0.545]	[0.843]
Policy three t-1	0.697	0.752	0.950 a
	[0.738]	[0.723]	[1.090]
Policy three t-2	−1	−1.32	1.130 a
	[0.895]	[0.836]	[1.060]
Spatial lag		0.647 ***	0.339
		[0.180]	[0.197]
Racial hate crime			130534.8 ***
			[24531.3]
Religion hate crime			157151.1
			[84183.0]
Disability hate crime			50233.4
			[223641.2]
Violent crime inc			−1083.5
			[576.5]
Property crime inc			−43.3
			[45.3]
Citizen ideology (CI)			0.025
			[0.017]
Unemployment			−0.045
			[0.083]
Democratic governor			0.208
			[0.530]
% Democratic legisl.			1.880
			[2.590]
Government ideology			−0.003
			[0.018]
Constant	4.62 ***	1.48	5.31
	[0.229]	[0.891]	[3.00]
N	637	637	637
Multiple Imputation			x

¹Spatial lag has a one-to-one relationship (not one-to-one million).

^aThese 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