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The Effect of Prior Police Contact on Victimization Reporting: Results from the Police–Public Contact and National Crime Victimization Surveys

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Abstract

Objectives

This study explores the association between past-year face-to-face contact with the police and subsequent victimization reporting. It also examines whether this relationship depends on the type of encounter (citizen-initiated contacts, routine vehicle stops, invasive encounters associated with being a suspect), its perceived justness, or victim characteristics. Among victims who did not notify the police, the reasons behind this decision are assessed to understand the mechanisms through which police encounters are related to reporting.

Methods

This research is the first to use longitudinal data that link the 2002, 2008, and 2011 Police Public Contact Surveys to the 2002–2014 National Crime Victimization Surveys. Multivariate logistic regression is used to examine the effect of prior contact on police notification among victims of personal (N = 1073) and household (N = 11,433) crimes.

Results

Prior contact with the police has no main effect on the reporting of personal crimes; however, the negative effects of police-initiated and unjust contact are amplified for the poor and African Americans. The reporting of household crimes varies based on prior police experiences and whether they were viewed as just. Personal crime victims with invasive contact are more likely than other contact groups to attribute non-reporting to fear of reprisal.

Conclusions

Using national data tells a complex story about how race/ethnicity, poverty, and recent experiences with the police interact to shape victims’ behavior. The intricacies of these findings suggest that efforts to increase reporting may need to be multifaceted and address victims’ concerns about safety and justness.

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Notes

  1. In 2005 new identifiers were created for all study participants as part of the redesign of the NCVS following the decennial census. Data from 2005 were excluded because it is not possible to link the PPCS with subsequent waves of the NCVS.

  2. The person sequence number is a variable that assigns a number to each person within the household during their first interview, ordering the individuals per household.

  3. To assess whether the PPCS provides a reliable measure for capturing police contact for NCVS victims, I used information about victims’ prior victimization experiences during a period that overlapped with the PPCS reference period. Of the 1210 people who reported being the victim of a personal crime in the 6 months prior to the PPCS interview, 78 reported the crime to the police and the police came when notified, but did not report having face-to-face contact with the police in the past year. The same is true for 881 of the 9329 victims of household crimes. The vast majority of these cases were matched without having to make any additional assumptions, i.e., they had unique characteristics that allowed them to be easily matched across datasets, so it is unlikely these discrepancies are due to the matching procedure.

  4. In a few cases, multiple incidents of the same type occurred in the same month and were the first incidents subsequent to the PPCS, making it impossible to determine which incident was most proximal to the PPCS interview. This issue affected 39 (0.6%) of the personal incidents and 444 (3.9%) of the respondents with household incidents. When this occurred, the first incident was selected at random. Analyses were replicated 10 times, each time, allowing for a different random selection of incidents and the findings remained the same.

  5. I repeated all analyses for household crimes using the household as the unit of analysis and measures of contact that took into account the collective experiences of the household members. The results were substantively the same (see “Appendix 1”). Analyses were also rerun limiting the sample to just the household reporter, thus omitting members of household who may have had police contact, but are unlikely to have been responsible for notifying the police, like dependents. Only one substantive difference emerged see footnote 20).

  6. Three victims of personal crime and thirty-four victims of household crimes are missing information on whether they perceived their contacts to be just, and these cases are excluded from the analysis that looks at perceptions of justness.

  7. In the full dataset, the vast majority (73.1%) of people with past-year contact had only one contact in the past year.

  8. There were other ways that people could have come into contact with the police such as in the course of a traffic accident or as a passenger in a traffic stop. People with these other types of contact are included in the no contact group because their interactions with the police do not fit cleanly with the other types. Additional analyses were done that used a five-category measure of contact in which these cases were included in an “other” contact group. The inclusion of this additional group does not change the findings that are presented here (available by request).

  9. Engel (2005) used this variable to capture procedural justice in her study of traffic stops; however, given the non-specificity associated with the term improper, I use this variable to capture a general measure of injustice.

  10. It is possible that the relationship between age and reporting is non-linear with young people more reluctant to report than adults and seniors. The analyses were replicated using a binary measure of age scored 1 if the respondent is 16–24 and 0 otherwise. I also explored including a quadratic measure of age to capture non-linearity. The results of these analyses remained the same as those presented here.

  11. This measure was created using household size- and year- specific poverty thresholds as determined by the U.S. Census. These thresholds can be found at the following site: http://www.census.gov/data/tables/time-series/demo/income-poverty/historical-poverty-thresholds.html.

  12. An alternate method of handling selection bias is to compute propensity scores and then use these as weights in the regression analysis; however, this method requires that the selection equation is properly specified. If relevant variables are omitted, which is likely to be the case, simulations indicate that, particularly for binary outcomes, weighting introduces bias in the estimation of treatment effects (i.e., effect of police contact) (Freedman and Berk 2008), and unweighted regression is recommended.

  13. This modeling strategy is best used to describe data and, because the data are produced by probability sampling from a defined population, to make statistical inferences. Causal inference requires a data generating model that is “nearly right” (e.g., there are no omitted variables) and that any given causal variables can be manipulated independently of other causal variables and the disturbance terms. These stringent requirements are unlikely to be met by these analyses and in fact are rare in criminology more generally (see Berk 2010, pp. 485–486).

  14. Unweighted results are presented for several reasons. First, Lohr and Liu (1994) compared weighted and unweighted estimates of the correlates of reporting using the NCVS and found very few differences between the weighted and unweighted estimates. Second, because of the split sample design in 2011, only 15% of the 2011 sample was given the 2008 survey. While the dataset used in this analysis contains far fewer cases from 2011 than the other years, these cases are weighted more heavily because their weights were computed to produce a population estimate for the United States in 2011. Therefore, the few cases from 2011 have a disproportionate influence and can distort the results. Results from the weighted analysis (excluding 2011 data) are substantively similar to those presented here and are included in “Appendix 2”. Two differences emerge. In the weighted results, the interaction between Latino and unjust contact for personal crimes is significant and mirrors the interaction effect observed for Blacks. The effects of invasive contact and invasive unjust contact on police notification for household victimizations are significant at alpha = 0.10, but not 0.05 (p = 0.065 and p = 0.060, respectively).

  15. In logistic regression, the dependent variable undergoes a non-linear transformation and therefore may not reflect the moderation effects that exist in the original data. To address this possibility, all interaction effects were re-estimated using linear probability models (see “Appendix 3”). The coefficients for the interaction terms in these models indicate the difference in the slope of the relationship between police contact and reporting associated with a one unit increase in the moderator, holding all other values constant. The results from the linear probability models mirror those obtained using logistic regression with one exception: For household victimizations, Latinos subjected to vehicle stops are less likely to report than those with no contact. This same level of suppression is not observed for Whites or Blacks who were drivers in traffic stops.

  16. Results for all analyses using multiple imputation along with more information about the imputation procedure are available from the author by request.

  17. This method is already incorporated into the administration of the NCVS. Questions on income only are asked every other interview. In the waves in which these are skipped, income information from adjacent interviews is substituted for the missing values.

  18. All predicted probabilities were estimated in Stata using the margins command. All other predictors in the model were set at their means.

  19. Odds ratios are computed as (e(b) − 1) × 100).

  20. When the sample is limited to the 5802 NCVS household reporters, findings from these analyses remain substantively unchanged with one exception—the coefficient for unjust invasive contact increases (b = −1.83, Robust SE = 0.58) and the difference in reporting between unjust and just invasive contact is now large and significant. The odds of living in a reporting household is 76.6% lower for those with unjust invasive contact relative to those with just encounters of this type (b = −1.45, Robust SE = 0.66, p = 0.03).

  21. Some categories were created by grouping multiple reasons of a similar type together in order to increase cell sizes. Police couldn’t do anything included the following: didn’t find out until too late; could not recover or identify property; and lack of proof. Police wouldn’t help included police wouldn’t think it was important enough, wouldn’t want to be bothered or get involved; police inefficient or ineffective, would arrive late or not at all; police would be biased, harass/insult respondent, cause respondent trouble; and offender was a police officer.

  22. Supplemental analyses indicate that the relationship between contact type and non-reporting due to fear of reprisal was observed only among the poor. In fact, among the poor, 37.5% of respondents with invasive contact indicated they did not report due to fear of reprisal, while none of the non-poor with this type of encounter provided this explanation. This analysis should be viewed as preliminary due to the small number of respondents who were poor, did not report, and had invasive contact (n = 8).

  23. The findings regarding the reason for not reporting remained the same when I replicated these analyses using multiple logistic regression models containing demographic and incident characteristics.

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Acknowledgements

This project was supported by Award No. #2014-R2-CX-0022, awarded by the National Institute of Justice, Office of Justice Programs, U.S. Department of Justice. The opinions, findings, and conclusions or recommendations expressed in this publication/program/exhibition are those of the author and do not necessarily reflect those of the Department of Justice. I would like to thank Janet Lauritsen for her thoughtful feedback and encouragement throughout the life of this project. I would also like to thank Stephanie Wiley and Rick Rosenfeld for their valuable comments, as well as my other colleagues at UMSL, who provided feedback at our faculty brownbag.

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Correspondence to Lee Ann Slocum.

Appendices

Appendix 1

See Table 5.

Table 5 Household-level analysis: logistic regression of police notification for household victimization on police contact

Appendix 2

See Tables 6, 7 and 8.

Table 6 Survey-weighted logistic regression results for personal victimizations excluding cases from 2011
Table 7 Survey-weighted logistic regression results for personal victimizations excluding cases from 2011, significant interaction effects
Table 8 Survey-weighted logistic regression results for household victimizations excluding cases from 2011

Appendix 3

See Tables 9, 10, 11 and 12.

Table 9 Linear probability model of police notification of personal victimization on contact, interactions between type of contact and poverty, race/ethnicity, and age
Table 10 Linear probability model of police notification of personal victimization on contact, interactions between unjust contact and poverty, race/ethnicity, and age
Table 11 Linear probability model of police notification of household victimization on contact, interactions between type of contact and poverty and race/ethnicity
Table 12 Linear probability model of police notification of household victimization on contact, interactions between unjust contact and poverty and race/ethnicity

Appendix 4

See Tables 13, 14.

Table 13 Logistic regression of police notification of personal victimization on contact, non-significant interaction effects
Table 14 Logistic regression of police notification of household victimization on contact, non-significant interaction effects

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Slocum, L.A. The Effect of Prior Police Contact on Victimization Reporting: Results from the Police–Public Contact and National Crime Victimization Surveys. J Quant Criminol 34, 535–589 (2018). https://doi.org/10.1007/s10940-017-9345-x

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