Abstract
Objectives
This research examines whether the information about different wrongful conviction rates would affect death penalty opinion. It is the first experiment to examine how different estimates of the rate of wrongful conviction, rather than general information of innocence, affect views about capital punishment.
Methods
I use Amazon Mechanical Turk to conduct the survey experiment. Five hundred two respondents were randomized into different groups to receive different information about wrongful conviction rate.
Results
People who were informed of a wrongful conviction rate of 4.1% were significantly less likely to support the death penalty compared to people who were told no information of the wrongful conviction rate. But knowing a wrongful conviction rate of 1% did not affect people’s death penalty support.
Conclusions
Information about different wrongful conviction rates had different effects on death penalty support. An accurate estimate of the wrongful conviction rate plays an important role in altering death penalty opinion.
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Notes
See: Furman v. Georgia, 408 U.S. 238, 92 S. Ct. 2726, 33 L. Ed. 2d 346, 1972b, pp.364. “But, if this information needs supplementing, I believe that the following facts would serve to convince even the most hesitant of citizens to condemn death as a sanction: capital punishment is imposed discriminatorily against certain identifiable classes of people; there is evidence that innocent people have been executed before their innocence can be proved; and the death penalty wreaks havoc with our entire criminal justice system.”
Respondents were told that some percentage of people who were convicted of murder were actually innocent. Not all murders are punishable by the death penalty. Here, I use wrongful conviction rates in murder as a proxy for the wrongful conviction rates in capital cases to reduce the cognitive burden for respondents.
The variance inflation factor (VIF) and the condition index were used to assess multicollinearity among the covariates. None of the variables had a VIF higher than 2, indicating no concern of collinearity. A collinearity problem is indicated when a condition index above the threshold value (30 was used here) accounts for a substantial proportion of variance for two or more coefficients. There was one condition index above the threshold value, and there was only one coefficient associated with it. Therefore, the condition indices raised no concern of collinearity.
Different people may interpret the rates provided in different ways. The question wording did not label these wrongful conviction rates as high or low, unacceptable, or acceptable, allowing respondents to interpret these rates in their own ways.
Given this is an “age of information,” it is possible that the respondents may be aware of the wrongful conviction rate. The national polls and the survey results in the current study both indicate the general public did not know the rate without researcher informing them and tended to greatly overestimate the wrongful conviction rate. For example, the average estimated wrongful conviction rates from national polls were between 10 and 12% (Louis Harris & Associates, 1999; Gallup Organization, 2000, 2003, 2005), far higher than the scholarly estimated rates. The survey used in the current study contains a question asking people’s perception of wrongful conviction rate (“In this country, out of every 100 people convicted of murder, about how many do you think are actually INNOCENT?”) before the treatment and the death penalty question. The average response was 27.1%, again far higher than the scholarly estimated rates. The responses of the control group did not differ from those of other groups. The respondents’ estimates did not affect their death penalty support. The differences between their estimation and the experiment manipulation also did not affect their death penalty support.
0.027% is an estimated wrongful conviction rate in felony cases according to Justice Scalia. I borrowed it here to represent a very low (close to 0) wrongful conviction rate for murder cases.
I reestimated the models using ordered logistic regression (Model 2.2 in Table S2) and multinomial logistic regression (Model 2.3 in Table S2) and got similar results. Though the p values and significance were slightly different from the main model, the direction and the size of the effects were similar.
I reestimated the models using multiple imputation (m = 10) at the item level and got similar results (Model 2.4 in Table S2). Though the p value and significance were slightly different from the main model, the direction and the size of the effect were similar.
Bivariate logistic regression (Table S1 in the Supplementary) yielded an identical result (the likelihood chi-square test statistic = 3.51, p = 0.3189). Compared to the main model with control variables (Table 2), the coefficients in the bivariate model slightly decreased but were still similar in size. The coefficient for the group exposed to a wrongful conviction rate of 4.1% was not significant at the 0.05 level but was significant at the 0.1 level (p = 0.075).
Supplementary analysis (Model 2.1 in Table S2) reports the same regression analysis for respondents who passed the attention check. The direction and size of the effects of key independent variables were similar to the main model.
Standardized coefficients indicate that empathy for criminals and racial resentment were the strongest predictors of death penalty support.
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Wu, S. The effect of wrongful conviction rate on death penalty support: a research note. J Exp Criminol 18, 871–884 (2022). https://doi.org/10.1007/s11292-021-09467-w
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DOI: https://doi.org/10.1007/s11292-021-09467-w