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Abstract
We propose and test a new methodology to assess the public’s criminal justice spending priorities. Respondents are asked to trade‐off alternative crime prevention and control policies as well as a potential tax rebate. In a nationally representative sample, we found overwhelming support for increased spending on youth prevention, drug treatment for nonviolent offenders, and police. However, the median respondent would not allocate any new money to building more prisons and would not request a tax rebate if the money were spent on youth prevention, drug treatment, or police. At the margin, we estimate the public would receive $3.07 in perceived value by spending $1.00 on youth prevention; $1.86 in value for every dollar spent on drug treatment; and $1.76 for a dollar spent on police. However, the public would not spend more on prisons, deriving only 71 cents in value for every tax dollar spent.
Acknowledgments
This project was supported by Grant No. 1999‐CE‐VX‐0001, awarded by the National Institute of Justice, Office of Justice Programs, US Department of Justice. Points of view in this document are those of the authors and do not necessarily represent the official position or policies of the US Department of Justice. Special thanks to the survey team at Roper Starch: Kevin Bray, Project Director; Kathleen Barringer, Project Manager; Robert Benford; and Nicolas A. Holt, PhD. Additional research assistance was provided by Gabrielle Chapman, Achintya Ray, and Mihir Shah. Comments received from seminar participants at the University of Darmstadt and the University of York (UK) are greatly appreciated.
Notes
1. See http://www.icpsr.umich.edu/GSS/.
2. We thank Glenn Blomquist for suggesting this basic approach to us when he served as a member of the expert panel that assisted us in developing our survey methodology for the entire NIJ‐funded project.
3. Arnold and Wyrick (Citation1982) propose a similar referendum at the national level that would allow for a tax rebate.
4. The 43 percent rate is based on a very conservative methodology accepted by CASRO (Council of American Survey Research Organizations). Another common method is to compare actual completions to refusals. In that case, our 1,300 respondents are compared to 928 who refused to participate, resulting in a response rate of 58 percent.
5. A comparison of our sample to the US Census, however, indicates that we might have underrepresented Latinos and the very poor. Whereas 10.0 percent of the 18 and older US population is Latino, only 6.4 percent of our sample (and 4.8 percent of our weighted sample) is Latino. One part of that difference is apparently due to language barriers. Sixty‐four individuals who were originally contacted were deemed ineligible due to language barriers. If all of these individuals were Latino, for example, that would represent 4.9 percent of our sample, which would bring our sample up to the estimated population ratio. There is also some noticeable difference in reported household income. The main difference appears to be in the percentage of our sample that report household income below $15,000. While 16.5 percent of the US household population reportedly has an income under $15,000, only 9.0 percent of our sample reported that level of income (9.6 percent of the weighted sample). One potential reason for this difference is that 13.8 percent of our sample refused to provide detailed household income information (as compared to the typical refusal rate for most of the questions in our survey that was well below 5 percent). If these refusals are clustered at the low end of the income distribution, our sample might look much more like the US population. Finally, we note that since the lowest income families will be those without telephones, and 5.9 percent of US households do not have telephones, this could account for the bulk of the difference in our sample.
6. See http://www.icpsr.umich.edu/GSS/.
7. To assess whether any particular interviewer had systematically different responses, we included a dummy variable for each interviewer in regression equations explaining responses. None of the interviewer dummy variables had any significant explanatory power in these regressions.
8. To assess news media effect, we searched and coded all crime‐related stories that appeared in national or major regional newspaper headlines by date. Including these dates as dummy variables in regression analyses explaining responses led to no significant explanatory effects. Similarly, we included a time trend in some regression equations. Interestingly, we found that there was some slight increase in the number of respondents in the highest income categories and in urban areas over time. We attribute this to our interviewing technique whereby we were persistent in calling back people until we found someone home. We view this as a positive feature of our survey design as it results in a more representative respondent population.
9. In our original survey draft, the question read “money to expand prisons and increase the time criminals serve in jail.” In our focus groups, we learned that some respondents were more interested in expanding the “use” of prison as a sanction‐i.e., providing a higher likelihood of prison for a short time, and thus wanted “more prisons” for other reasons. After further discussions with focus group members and our research team, we decided to opt for the more general “more prison” language without distinguishing between longer prison sentences versus more frequent prison sentences. While it is possible that a segment of the population would like to spend more money on prisons to make them less crowded and more humane, the question starts out explaining the goal is to either fund “crime control strategies” or to give back money to taxpayers. Thus, while someone might want to increase prison to reduce overcrowding, that is unlikely to be a crime control strategy.
10. In an earlier question of the survey, respondents were asked to decide upon an appropriate sentence for specific offenders. In those scenarios, alternatives to incarceration were offered. In addition, respondents were randomly informed of the cost of a year in prison. We found no statistical difference in responses to this part of the survey based on whether or not the respondent was provided information about the cost of incarceration.
11. Approximately 4.4 percent of respondents answered “refused” or “don’t know” to the initial questions about whether or not they would allocate any money to a particular program. An additional 0.4 percent ultimately dropped out due to failure to provide dollars or percentages that added up to 100 percent of the total. Thus, the figures in the table represent the responses of the 95 percent of respondents who fully answered these questions.
12. The log transformation (while adding 1 before the transformation to allow for zero responses) allows us to use OLS to estimate a dependent variable that is otherwise bounded by 0 and 1.
13. Not surprisingly, there is some correlation between those who “worry” about crime and many of the other demographic variables in Table . For example, males worry less about crime than females, and Blacks worry more than non‐Blacks. However, none of these correlations are very high (e.g., pearson correlations never exceed 0.2), and excluding the “worry” variable does not significantly change any of the other results shown in Table .
14. This is also true in our sample. For example, 28.2 percent of females responded that they worry “a little or a lot” about themselves “or a loved one becoming a victim of a crime,” compared to 22.1 percent for males (p < .01).
15. For example, according to Bureau of Justice Statistics (Citation2004), the risk of violent victimization in 2002 was about 20 per 1,000 for White males, 25 for White females, 27 for Black females, and 29 for Black males. Similarly, the rate for those 18–34 is over 40 per 1,000, compared to about 15 per 1,000 for those age 35–64 and only three per 1,000 for those age 65+.
16. It is possible that there is a differential rate within the male‐age categories based on the fact that young males have a particularly high risk of victimization. However, interaction terms between age categories and gender were not significant.
17. Note that one explanation for this finding is that the lowest‐income individuals do not pay income taxes and thus might not expect to receive any part of the tax dollar rebate.
18. Critics of public surveys often note that misperceptions about crime or other social ills might lead respondents to respond differently than they would if they had full knowledge (see, for example, Golash & Lynch, Citation1999). Presumably, that is one of the benefits of representative democracy. One response is to dismiss our approach. Another, and we believe more appropriate, response is to acknowledge that we now have important information on the public’s demand for crime prevention and control strategies based on the public’s perception of crime rates and the costs and benefits of alternative policies. Further studies might improve on our knowledge by conducting more in‐depth surveys that provide more context and background information to respondents. Moreover, if policymakers are concerned about these perceptions, public education campaigns might be warranted.
19. For example, Durham, Elrod, and Kinkade (Citation1996) critique the Gallop Poll’s measure of the public’s support for the death penalty by employing a similar technique in one county in Florida. They use vignettes of specific homicides and find different demands for the death penalty depending upon the scenario.