Driving Public Support: Support for a Law is Higher When the Law is Named After a Victim

Figures & data

Table

Research question	Hypothesis
1. Does naming a proposed law after a victim increase support for the law?	1. Compared to unnamed laws, named laws will have more support.
2. Among laws named after a victim, do characteristics of the named victim influence the level of support for the law?	2a. Compared to a law named after a White victim, a law named after an African American victim will have less support.
	2b. Compared to a law named after a woman or girl, a law named after a man or boy will have less support.
	2c. Compared to a law named after a child victim, a law named after an adult victim will have less support.
3. Among laws named after a victim, are there interaction effects between the characteristics of the named victim?	3a. Support for the law will be highest when the named victim is a White girl.
	3b. Support for the law will be lowest when the victim is an African American man.
4. Do the same factors that influence support for the law also influence support for the level of punishment?	4. The same victim naming factors that influence support for the law also influence support for the level of punishment.

Table 1. Descriptive statistics.

N = 1000 Variable	Mean	SD	%
Strong support for law			49.7%
Support for punishment	0.00	0.68
Vignette Conditions
Named law			88.8%
Child victim			43.5%
White victim			44.0%
Woman/Girl victim			43.9%
Name of law, victim age
Unnamed law			11.2%
Latisha’s law, child			10.1%
Latisha’s law, adult			12.1%
Trisha’s law, child			10.7%
Trisha’s law, adult			11.0%
DeShawn’s law, child			11.6%
DeShawn’s law, adult			11.0%
Shawn’s law, child			11.1%
Shawn’s law, adult			11.2%

Table 2. Predicting strong support for law and support for punishment.

	Strong support for law				Support for punishment
	b	SE	OR	Contrasts	β	SE	Contrasts
Name of law, victim age^1
a. Latisha’s law, child	0.82**	0.28	2.26		0.15	0.10
b. Latisha’s law, adult	0.85**	0.27	2.35	d	0.25**	0.09
c. Trisha’s law, child	0.49	0.27	1.63		0.17	0.10
d. Trisha’s law, adult	−0.09	0.28	.92	b	−0.01	0.09
e. DeShawn’s law, child	0.61*	0.27	1.84		0.14	0.10
f. DeShawn’s law, adult	0.36	0.27	1.44		0.07	0.10
g. Shawn’s law, child	0.24	0.27	1.27		0.18*	0.09
h. Shawn’s law, adult	0.51	0.27	1.66		0.20*	0.09
Constant	−0.44*	0.19	.65		−0.13	0.07
N	1,000				1,000
	Pseudo R^2 = .02				R^2 = .02
	Wald χ^2(8) = 22.92**				F(8,991) = 1.91

Note: Models present robust standard errors. Strong support for law model presents logistic regression estimates, and support for punishment model presents ordinary least squares estimates. The contrasts column indicates that marginal estimates are significantly different at an alpha level of .05 after applying a Bonferroni correction for multiple comparisons.

¹Ref. Unnamed law.

* p < .05; ** p < .01; *** p < .001.

Figure 1. Conditional marginal effects with 95% CI for each outcome.

Note: Conditional marginal effects for each law are in comparison to an unnamed law (0 on the Y axis).

Table 3. Probability of strong support for law: Predictive margins and differences in effects by victim characteristics.

Effect of victim gender
	Man/Boy		Woman/Girl		Man/Boy vs. Woman/Girl
	Margin	SE	Margin	SE	1st differences	SE	Contrasts
a. AA Adult	0.48*	0.05	0.60*	0.04	0.12	0.07	b
b. White Adult	0.52*	0.05	0.37*	0.05	−0.15*	0.07	a
c. AA Child	0.54*	0.05	0.59*	0.05	0.05	0.07
d. White Child	0.45*	0.05	0.51*	0.05	0.06	0.07
Effect of victim race
	African American		White		AA vs. White
	Margin	SE	Margin	SE	1st differences	SE	Contrasts
a. Man	0.48*	0.05	0.52*	0.05	0.04	0.07	b
b. Woman	0.60*	0.04	0.37*	0.05	−0.23*	0.06	a
c. Boy	0.54*	0.05	0.45*	0.05	−0.09	0.07
d. Girl	0.59*	0.05	0.51*	0.05	−0.08	0.07
Effect of victim age
	Adult		Child		Adult vs. Child
	Margin	SE	Margin	SE	1st differences	SE	Contrasts
a. Deshawn	0.48*	0.05	0.54*	0.05	0.06	0.07
b. Shawn	0.52*	0.05	0.45*	0.05	−0.07	0.07
c. Latisha	0.60*	0.04	0.59*	0.05	−0.01	0.07
d. Trisha	0.37*	0.05	0.51*	0.05	0.14	0.07

Note: Standard errors calculated using delta method. Contrasts indicate significant 2^nd order differences. Differences include Bonferroni correction for multiple comparisons. African American abbreviated AA.

* p < .05.

Table 4. Support for punishment: Predictive margins and differences in effects by victim characteristics.

Effect of victim gender
	Man/Boy		Woman/Girl		Man/Boy vs. Woman/Girl
	Margin	SE	Margin	SE	1st differences	SE	Contrasts
a. AA Adult	−0.06	0.07	0.13*	0.06	0.18	0.09	b
b. White Adult	0.07	0.06	−0.13*	0.06	−0.20	0.09	a
c. AA Child	0.01	0.07	0.02	0.07	0.01	0.09
d. White Child	0.05	0.05	0.04	0.07	−0.01	0.09
Effect of victim race
	African American		White		AA vs. White
	Margin	SE	Margin	SE	1st differences	SE	Contrasts
a. Man	−0.06	0.07	0.07	0.06	0.13	0.09	b
b. Woman	0.13*	0.06	−0.13*	0.06	−0.26*	0.09	a
c. Boy	0.01	0.07	0.05	0.05	0.04	0.09
d. Girl	0.02	0.07	0.04	0.07	0.02	0.10
Effect of victim age
	Adult		Child		Adult vs. Child
	Margin	SE	Margin	SE	1st differences	SE	Contrasts
a. Deshawn	−0.06	0.07	0.01	0.07	0.07	0.09
b. Shawn	0.07	0.06	0.05	0.05	−0.02	0.08
c. Latisha	0.13*	0.06	0.02	0.07	−0.11	0.09
d. Trisha	−0.13*	0.06	0.04	0.07	0.18	0.09

Note: Standard errors calculated using delta method. Contrasts indicate significant 2^nd order differences. Differences include Bonferroni correction for multiple comparisons. African American abbreviated AA.

* p < .05.