Area Differences and Time Trends in Crime Reporting: Comparing New York to Other
Metropolitan Areas
Min Xie
School of Criminology and Criminal Justice
Arizona State University
[June, 2011]
Introduction
For the past three decades, New York City has seen significant changes in crime and the
ways in which police interact with the public to engage them in problem solving and crime
prevention. Unfortunately, while much has been written about the significant crime drop in New
York City, little is known about changes over time in the public’s crime-reporting behavior in
this urban center, let alone how crime reporting in New York compares with other urban areas
such as Los Angeles and Chicago. This paper addresses these gaps in the literature. Using the
National Crime Victimization Survey (NCVS) metropolitan area database (1979-2004), it
specifically examines three sets of questions:
(1) To what extent do individual metropolitan areas exhibit significant area differences in
crime reporting?
(2) Have New Yorkers followed a national trend and become more willing to report
crimes to the police during the past few decades?
(3) When victims are asked about the reasons for reporting and not reporting, to what
extent are their decisions to report related to their perceptions of the police? Have the
victims’ perceptions of the police changed over time in New York, as well as in other
metropolitan areas?
In this investigation, we are particularly interested in comparing the patterns of reporting
for the major metro areas of New York, Los Angeles, Chicago, Philadelphia, and Detroit. These
areas were selected because they were the five largest metropolitan areas in the U.S., populationwise, when the National Crime Survey (NCS, the predecessor of the NCVS) was first conducted
in 1973. With the exception of Detroit (whose population declined as indicated by the 2010 U.S.
census), these metropolitan areas remained high in their ranks over the past few decades (U.S.
1
Census Bureau, 2010). A comparison of these areas will help us to see the New York experience
in national context. Most importantly, the paper will demonstrate that New York has a lower
level of reporting than many MSAs, but there are indications that victim-police relations are
improving. The data show that, over time, victims in New York (white, black, and Hispanic) are
less likely to express concerns about the helpfulness of the police.
Prior Research: Understanding Crime Trends and the Reporting of Crime
From 1979 to 2004, crime in the United States fluctuated. After an extended period of
increase in index crime rates from the mid-1960s to 1970s, the year 1979 was followed by a brief
period of decline in the early 1980s, another upturn from the mid-1980s, and then a sustained
period of decline from the 1990s and well into the 2000s (Blumstein and Wallman, 2006;
Zimring, 2007).1 The New York metropolitan area showed a strong decline in crime over the
1990s (see Figure 1; New York City accounted for 91% of the crimes recorded by the Police in
the New York metropolitan area). The other metropolitan areas also experienced reductions in
crime, although the extent varied by place and crime type. Philadelphia and Detroit, for
example, showed only minor or no significant change in the rates of robbery and aggravated
assault for the entire study period. Los Angeles and Chicago, in contrast, reported a more
substantial reduction in these types of crime.
[Figure 1 about here]
Victims’ willingness to report crime to the police is important for understanding crime
trends, and this is especially the case since the reporting of crime is likely to vary over time and
space. In New York City, changes in police tactics and strategy may influence the public’s
perception of the police. From the late 1980s, there has been an increased focus on controlling
1
For long-term trends in violent and property crimes, see the website maintained by Dr. Richard
Rosenfeld at http://www.crimetrends.com.
2
minor offenses to improve the quality of life (for reviews on police reforms in New York City,
see Bratton and Knobler, 1998; Kelling and Sousa, 2001; Silverman, 1999). Large numbers of
new police officers were added to the city streets through the Safe Streets program (Greene,
1999). The changes also include the implementation of Compstat, which, by holding precinct
commanders accountable for activities at the precinct level, helped to refocus the attention of the
police on solving community problems (Weisburd et al., 2003). These reforms brought changes
to the interaction between the police and the public. Greene (2000:319), for example, pointed
out that there is a “delicate relationship between the police and those policed.” Police efforts to
clean up city streets may encourage communities to participate in neighborhood watches, citizen
patrols, and other crime prevention activities. Yet, aggressive police tactics may have hidden
costs as it may increase friction between the police and poor, minority neighborhoods (Fagan et
al., 2010; Meares, 1998). These studies suggest that police-citizen relations in New York may
have been changing over time. Although no research has examined specifically the temporal
patterns of crime reporting in New York, given changes in policing over the past few decades, it
is likely that the nature of crime reporting has changed across time.
In order to understand crime reporting in New York, we turn to research on crime
reporting at the national level. Our focus is to see whether there is evidence in the research
literature that substantial changes have occurred in victims’ crime-reporting behavior, and if so,
what the long-run patterns of crime reporting look like.
In perhaps the most direct evidence of this issue, Baumer and Lauritsen (2010) found
that, from 1973 to 2005 based on data from the NCS and NCVS, victims became more likely to
report to the police. Prior to their investigation, studies of rape and violence against women have
observed that historical and social contexts may determine what factors influence victims’
3
decisions to call the police (Bachman, 1993; Gartner and Macmillan, 1995). There is evidence,
although not always consistent, that the reporting of rape and sexual assault may increase over
time because of legal reforms and changes in how people perceive these crimes and hold more
liberal gender views (see discussions of these issues in Baumer, Felson, and Messner, 2003;
Clay-Warner and Burt, 2005; Felson and Pare, 2005; Jensen and Karpos, 1993; Orcutt and
Faison, 1988). Baumer and Lauritsen (2010) drew on these insights, but used data to analyze the
temporal patterns of reporting for a broader set of violent and property crimes. Increases in
reporting observed in their study were widespread: With the exception of robbery, their data
showed general increases in reporting for burglary, motor vehicle theft, larceny, and various
forms of violence (sexual or non-sexual assaults, stranger and nonstranger violence, violence
against women or men, and violence experienced by members of different racial and ethnic
groups). For most crimes, the increase in reporting is most pronounced for the period from the
mid-to-late 1980s to 2005, the last year of data for their study.
In addition to showing that there is an upward trend in the reporting of crime, Baumer
and Lauritsen (2010) raised an important issue for crime-reporting research, that is, though the
aggregate rates of reporting are useful indicators of victims’ willingness to report crime, trends in
reporting could be described better by accounting for changes across time in the nature of crime
and the redesign to the NCVS in 1992. In our study, we used a similar strategy to analyze crime
reporting in New York and other MSAs. If data in these locations follow a similar time path to
that of the national data (i.e., there is an upward trend in crime reporting, particularly in more
recent years), the police data could have underestimated the magnitude of crime decline in these
MSAs. Because MSAs are embedded in varied legal, social, and cultural backgrounds, their
residents may respond to different cues when making decisions to call the police. It is an
4
empirical question, then, as to whether MSAs in our sample have distinctive patterns of crime
reporting.
Data and Sample
The NCS-NCVS is a major national household survey that has served as the nation’s
primary source of information on criminal victimization, particularly for crimes not reported to
the police, since 1973. The 1979-2004 MSA database is a special subset of the NCS-NCVS
national data created by the Bureau of Justice Statistics (BJS) and the U.S. Census Bureau to
allow the estimation of sub-national victimization rates for the 40 largest MSAs (Lauritsen and
Schaum, 2005; U.S. Department of Justice, 2007). Appendix 1 presents maps of our target
MSAs, along with a map of the full sample (N = 40 MSAs). As shown in the maps, each MSA
encompasses both the central city and the surrounding counties. The 40 MSAs account for 40
percent of the U.S. population. From 1979 to 2004, incidents in the five largest MSAs accounted
for approximately 30 percent of incidents in the NCS-NCVS MSA database.
General Rates of Reporting: New York Versus Other MSAs
Trends in reporting can be examined, first, using aggregate rates of reporting. To place
the New York experience in a historical context, Figure 2 compares New York and the rest of the
MSAs in terms of reporting rates across time for various types of crime. The reporting rates
were based on the respondents’ statements of whether a crime was made known to the police.
Because of instability in rates due to small numbers, we smoothed the annual rates by calculating
3-year moving averages to reduce the influence of year-to-year fluctuations (a similar strategy
was used by Lauritsen and Schaum, 2005, in their analysis of the NCVS crime rates).
[Figure 2 about here]
5
From Figure 2, as one might expect, we observe that the reporting of crime in the MSAs
follows a hierarchy by crime type. Among violent crimes, simple assaults had the lowest rates of
reporting, while the reporting rates for aggravated assaults and robberies were higher.2 Among
property crimes, the reporting rates were the highest for motor vehicle thefts, followed by
burglaries and then thefts. This pattern was consistent over time, and was replicated in New
York and other MSAs (the graphs for Los Angeles, Chicago, Philadelphia, and Detroit are not
shown here).
The most important feature of Figure 2, however, is that crime reporting rates tend to be
lower in New York than in other metropolitan areas combined (burglaries and aggravated
assaults are exceptions). Additional analyses (in which we disaggregated reporting rates by race
and ethnicity) suggest that this pattern was not just a problem for racial minorities such as blacks
and Hispanics. Non-Hispanic whites in New York particularly have shown lower rates of
reporting than whites in other MSAs, especially in the post-redesign NCVS period (figures not
shown).
To make the patterns easier to see, Table 1 summarizes the results of analysis in which
we used binary logistic regression models to test whether the area differences in reporting, as
seen in Figure 2, are statistically significant. The analyses used crime incidents as the unit of
analysis, police notification as the dependent variable (coded 1 if the incident was reported to the
police and 0 if not), a dichotomous indicator for location as the independent variable (coded 1 for
New York and 0 for other MSAs), and the year in which the incident occurred as a control
variable. Corresponding to Figure 2, we estimated the models separately for each type of crime,
victim race (white, black, and Hispanic), and time period (NCS versus NCVS). Several patterns
2
Due to sample size limitations, rape and sexual assault were not considered in this study.
6
are clearly visible in Table 1. First, in the NCS period (mainly the 1980s), the main differences
between New York and other MSAs lie in the lower rates of reporting in New York among
blacks and Hispanics for robbery, burglary, and motor vehicle theft, whereas in the NCVS period
(from the early 1990s to 2004), the differences for blacks and Hispanics decreased, and whites
became the driving force of the differences between New York and other MSAs, as whites in
New York showed lower rates of reporting in many forms of crimes except for burglary and
motor vehicle theft. Burglary is worth noting because it is the only type of crime that has higher
rates of reporting in New York compared to other MSAs, especially among whites. We next
incorporate the characteristics of crimes to see whether these area differences persist and whether
there are systematic changes over time in the reporting of crime in New York and other MSAs.
[Table 1 about here]
Area Differences and Time Trends in Reporting
After seeing the aggregate patterns of reporting in New York, the second part of the
analysis focuses on the time trends and area differences in reporting between New York and the
other MSAs. Following Baumer and Lauritsen (2010), the analysis accounts for (1) changes
over time in the nature of crime (see Appendix 2 for a description of variables used in the
analysis that represent the characteristics of the incident, victim, and offender), and (2) the
effects of the 1992 NCVS redesign. The redesign effects occur because the redesigned NCVS
resulted in a significantly lower percentage of crimes reported to the police than the NCS, and
these effects could confound the effects of time in reporting explored in this paper if not taken
into account. Baumer and Lauritsen (2010) developed redesign weights using data from the
NCVS phase-in period in which the full sample was divided into two parts, one was administered
the NCS procedure, and the other the NCVS procedure. These weights were used in this study to
7
remove the effects of the redesign on estimated likelihood of reporting. The results are presented
in Table 2.
[Table 2 about here]
Table 2 answers one of our research questions: Are the rates of police notification lower
in New York after adjustment for other known characteristics of crime? In this table, eight
models (one for each type of crime) were estimated using all incidents from the 40 MSAs. In all
but two models (4 and 6), the estimated coefficients for the dichotomous variable New York are
negative and statistically significant, meaning that crimes occurring to New York residents had a
lower probability of being reported to the police than crimes in other MSAs. Using other MSAs
as the reference group, for example, the probabilities of reporting are lower in New York by 12
percent for robbery, 11 percent for simple assault, 4 percent for motor vehicle theft, and 19
percent for theft, with the remaining explanatory variables set at their means as described in
Appendix 2.
Of the five largest MSAs, New York and Los Angeles tend to have reporting rates that
are largely comparable: In supplementary analyses, we used Los Angeles as the reference group
and found only one significant difference, that is, New York showed a significantly higher
likelihood of reporting than Los Angeles in the incidence of burglary. Chicago, Philadelphia,
and Detroit, in contrast, tend to have a higher likelihood of reporting than New York and Los
Angeles (in additional analysis, we found that this pattern was particularly evident in simple
assault and theft). Thus, research needs to go beyond crime characteristics to explain the low
rates of reporting in New York and Los Angeles. The results make New York (and Los Angeles
as well) an interesting case study for future research.
8
Table 2 also tells us the nature of time trends in reporting for the full MSA sample. Here
we are interested in whether there are significant year effects on the likelihood of reporting, net
of control variables.3 To help visualize the magnitude of change, we calculated the predicted
probabilities of reporting from 1979 to 2004, using the estimated coefficients and the mean
characteristics of each crime type. As Figure 3 shows, when the data are pooled across MSAs,
there are discernible time trends in crime reporting that have similar shapes to those of the
national trends as reported by Baumer and Lauritsen (2010) which suggests that crime reporting
has increased over time during the study period. Similar to the national data, the patterns of
change in the MSAs varied somewhat across crime types. Violent crimes, in general, showed
changes of larger magnitude than property crimes. For robbery, the likelihood of police
notification initially decreased before it began to increase in the late 1980s (see figure 3c). The
change was the least visible in burglary throughout the study period (see figure 3d), even though
the coefficients for time (year squared and year cubed) were statistically significant for this type
of crime.
[Figure 3 about here]
Because New York is our target area, we estimated separate models for reporting in
New York, comparing the results to Los Angeles, Chicago, Philadelphia, and Detroit, using the
same modeling strategy as was used in Table 2. Table 3 reports the results from the regressions
(to conserve space, we only report the fitted values for the year effects). Figure 4 illustrates the
estimated trends, making it easier to see how New York is different from Los Angeles and the
other MSAs. Specifically, we found that the coefficients for time in New York were statistically
3
To avoid collinearity between the linear and nonlinear trends (i.e., year squared and year
cubed), time was centered in our analysis at the midpoint of the observation interval (i.e., the
year of 1992).
9
significant for robbery and theft, but not for assault, burglary, and motor vehicle theft (in Figure
4a, we use solid and dashed lines to distinguish between significant and insignificant year
effects). As Figure 4a indicates, after an initial decrease, the reporting of robbery began to
increase in New York in the mid-1980s; after a long period of gradual increase, the rise leveled
off around 2000 and decreased slightly afterwards. This recent drop in reporting is more visible
in New York in the violence sample where robbery and assault are combined (see Figure 4b). Of
the five largest MSAs, New York is the only MSA that showed some decline in the reporting of
violent crimes in recent years. For property crimes, theft is the only crime in New York for
which there was a statistically discernible increase in reporting after a relatively long duration of
gradual decline (from the early 1980s to the mid-1990s). In comparison, as Figure 4c shows, no
other MSAs (except for Los Angeles) showed declines in the reporting of property crimes during
the study period. These results suggest that, even though there are increases in reporting in New
York, increase is not a dominant feature for New York for the period studied. This finding, once
again, makes New York an interesting case for studying the reporting of crime.
[Table 3 and Figure 4 about here]
Victims’ Reasons for Reporting and not Reporting
Because of changes in policing in New York, we have noted that there might be changes
in how people perceive the police, which, in turn, may influence the patterns of crime reporting.
In the NCVS, victims who called the police are asked about their reasons for reporting. If the
police were not called, the victims are asked to indicate reasons for not reporting, including their
perceptions of how the police might act, had the police been notified. We explored these data to
see if they identify potential explanations for the patterns of crime reporting observed in this
study.
10
Table 4 provides a description of information available in the MSA database (the listed
reasons are core items available in the NCS-NCVS for the study period). Most importantly, the
table shows that there is a significant difference between New York and other MSAs in the
proportion of victims who reported that they did not report the crime to the police because
“police wouldn’t help.”
[Table 4 about here]
At first glance, as Table 4 indicates, when all years of data are combined, New Yorkers
are much more likely than victims of other MSAs to report that they failed to report crimes
because “police wouldn’t help.” When we consider the time trends, however, it is clear that
there have been significant changes over time in New York in victims’ perceptions of the police
(see Figure 5). Compared to other MSAs, the drop in New York in the proportion of nonreporters who thought that “police wouldn’t help” is impressive. After reaching a peak value of
28 percent in the early 1990s (see Figure 5a), the frequencies at which this reason was used to
explain a victim’s failure to report crime declined sharply in New York, finally falling below the
average level of other MSAs. More importantly, as Figure 5b indicates, the magnitude of change
in New York, during the period of decline, is similar for whites, blacks, and Hispanics. Los
Angeles also showed a steady decline, but the decline was less steep, particularly when we
examine the trends for whites and Hispanics (see Figure 5c). In Chicago, Philadelphia, and
Detroit, the pattern is less clear.
[Figure 5 about here]
Figure 5 reflects the pattern of change for a mixture of violent and property crimes. In
previous research, Felson and colleagues (2002) suggested that characteristics of crime (such as
the presence of weapon, physical injury, the relationship between the victim and offender) may
11
influence the chance a victim will think “police wouldn’t help” and therefore not call the police.
Thus, similar to how we analyzed temporal changes in the likelihood of reporting, temporal
changes in victims’ perceptions of the police should be examined by taking into account
differences over time in crime characteristics. Using a similar strategy, we modeled victims’
motives (coded 1 if the victims cited “police wouldn’t help” as a reason for not reporting, and 0
otherwise), using binary logistic regression models and explanatory variables outlined in
Appendix 2. Like the analysis in the previous section, our objective is to assess whether there
are significant time effects in those equations, net of the control variables. 4
Table 5 presents the key findings of the evaluation (note that the table reports only the
fitted values for the year effects in New York; the coefficients for the control variables are
omitted). By comparing panel B to panel A, we can see that, even after controlling for changes
in the nature of crime, statistically significant year effects were observed for property crimes
(especially burglary and theft). For violent crimes, time effects were reduced to insignificant
levels when adjusting for crime characteristics. Figure 6 displays the nature of these patterns by
plotting the predicted probabilities of victims reporting “police wouldn’t help” (other variables
were set to mean values). Violent crimes are indicated with a dashed line, as the slight
downward trend did not reach a significant level (see Figure 6a). The patterns for property
crimes are more interesting (see Figure 6b). Burglary victims showed a steady, yet more gradual
decline in the probability of believing that “police wouldn’t help.” Theft victims, in contrast,
showed first an increase in this belief during the 1980s and then a sharp decrease from the early
4
In this analysis, we examined the full sample of victims, both non-reporters and reporters.
Reporters were coded 0 on the dependent variable, that is, we assume that victims who called the
police would expect the police to take their reports seriously and offer the needed help. In
unreported analysis, we also analyzed the data by focusing exclusively on the non-reporters. The
analysis yielded similar conclusions regarding the patterns of temporal changes in victims’
perceptions of the police.
12
1990s to 2004. Victims of motor vehicle thefts did not show significant changes over time and
are thus omitted from Figure 6b. Because thefts account for the majority of property crimes, it is
not surprising that property crimes, when pooled together, have a trend of “police wouldn’t help”
that looks similar to the curve of thefts. In general, the results suggest that offenses of lesser
severity (such as thefts relative to burglaries and property crimes relative to violent crimes) are
more prone to changes in victims’ perceptions of the police. A race-specific analysis indicates
that the decline in victims’ belief that “police wouldn’t help” is common to all victims (white,
black, or Hispanic). Figure 6c illustrates this point by showing the pattern of change, by race and
ethnicity, for victims of property crimes.
[Table 5 and Figure 6 about here]
The uniqueness of the New York experience is most clearly shown in Figure 7. We
noted above that Los Angeles is the only another MSA among the top five MSAs that exhibited
discernible declines in the aggregate proportions of victims not reporting because “police would
not help.” Figure 7 compares New York and Los Angeles in terms of the predicted probabilities
of victims expressing this opinion for the four types of crime listed in Figure 6. It is apparent
that, for all of the crimes listed, New York is characterized with a higher starting level but a
greater decline in victims’ belief that “police wouldn’t help,” after we factor in the characteristics
of the crimes. We also conducted similar analyses using data from Chicago, Philadelphia,
Detroit, and the rest of the MSAs. The general pattern is the same: No other MSA rivaled New
York in its decreasing likelihood of police being perceived as unhelpful.
[Figure 7 about here]
For comparison purposes, we examined other reasons for not reporting and found that in
most cases, New York and the rest of the MSAs displayed a similar level and trend in why
13
victims failed to call the police (see Figures 8b, 8c, and 8d). In one exception (see Figure 8a),
New York showed a lower likelihood of using non-police agencies or other informal
mechanisms to handle crime when the police are not notified. This finding, combined with our
finding that New York has a lower likelihood of crimes being reported to the police, suggests
that compared to other MSAs in the sample, New York residents might carry a higher burden of
crime, as a larger proportion of their victimizations received no assistance from the police or
other officials. Although fewer and fewer New Yorkers perceive the police as unhelpful, how to
encourage them to contact the police when crime occurs is still a challenging issue.
[Figure 8 about here]
Summary and Future Research
The NCS-NCVS MSA database provides an important opportunity to assess crime
reporting behavior in the New York metropolitan area. Returning to the questions from the
introduction, some key findings can be summarized as follows:
(1) Compared to many MSAs, New York has a low likelihood of reporting. This pattern
varies somewhat by crime type, with burglary showing a higher likelihood of
reporting, especially among white victims. Of the five largest MSAs, the likelihood
of reporting is most comparable between New York and Los Angeles.
(2) Despite a national trend toward increased reporting, New York showed some, but no
widespread, increases in the likelihood of reporting. The data, indeed, showed some
decline in the reporting of violent crimes in the early 2000s. In contrast, other large
MSAs such as Los Angeles, Chicago, Philadelphia, and Detroit have shown more
evident increases in the reporting of violent crimes.
14
(3) New York residents - whites, blacks, and Hispanics - demonstrated a common
downward trend in failing to report crime because “police wouldn’t help.” This
pattern is most evident in the 1990s and 2000s, and is more pronounced for thefts and
burglaries than for violent crimes. Of the largest MSAs, New York’s change is the
largest in magnitude.
For law enforcement agencies, our findings offer both bad news and good news. The bad
news is that, with the exception of burglary, New York has lower-than-average rates of
reporting, and it lags behind other MSAs in achieving increases in reporting rates. The good
news is that there are improvements in how the public view the police, as increasingly fewer
victims indicate that they did not report crime because “police wouldn’t help.” The proportion of
victims who failed to report because “police couldn’t do anything” also decreased. These data
indicate that, over time, confidence in the police increased. Reasons for failing to report that are
not related to the police become increasingly more important in victims’ decisions to not to
report crime.
Our study suggests promising avenues for future research. Because New York showed
the largest change in how victims think the police might help them deal with crime, future
research might wish to identify the sources of this change. One might start by asking whether
there have been systematic changes over time in policing resources, policies, and activities in
specific areas or against particular types of crime. The ways in which thefts and burglaries are
handled in New York are particularly worth exploring because (1) thefts and burglaries showed
the most visible changes in victims’ perceptions and (2) burglaries are also unique in that, unlike
other crimes, the reporting of burglaries is higher in New York than in other MSAs. Prior
research has noted that police priorities (and the public’s view on what these priorities should be)
15
can vary from place to place and from time to time (e.g., Sherman, 1990; Skogan, 1996). A
catalog of current and historical differences between law enforcement agencies in their
approaches to handling thefts and burglaries may offer the first clue to the mechanisms
responsible for the observed patterns of reporting.
The efficiency and trustworthiness of the police are, of course, but one aspect of
American life that may influence the reporting of crime. Other factors, such as social norms for
self-help, the access to non-police organizations, degree of urbanization, and community
cohesiveness, may all have contributed to differences between places in police notification. In
this study, our emphasis was to assess the magnitude of area differences in reporting by
removing any differences in crime characteristics. Models in our analysis can be easily
expanded to include MSA-level characteristics that theory suggests should influence crime
reporting, as long as the data are available (for discussions on macro-level factors of crime
reporting, see, e.g., Baumer and Lauritsen, 2010; Goudriaan, Lynch, and Nieuwbeerta, 2004;
Soares, 2004). Based on our analysis, New York and Los Angeles form an interesting pair:
Both have comparably low rates of reporting despite physical distance between the two MSAs,
and yet they also show different patterns of change in both reporting and the victims’ perceptions
of the police, which may be related to their differences in social and political contexts. As a
starting point, one may use the two urban centers to guide future research on contextual factors
that explain area differences in crime reporting.
Finally, the patterns of reporting observed in this study provide additional information
about police-recorded crime trends. For metropolitan areas where victims show increased
willingness to seek police intervention (examples include Chicago and Detroit where the
probabilities of reporting increased by approximately 50 percent in violent crimes over the study
16
period), the police-recorded crime statistics may under-estimate the magnitude of crime decline,
or over-estimate the magnitude of crime increase, depending on which time period is analyzed.
In New York, the reporting for burglary, motor vehicle theft, and non-sexual assault has
remained relatively stable after adjusting for changes in crime characteristics. The policerecorded trends for these crimes, therefore, would be more accurate than the trends observed for
robbery and theft (in which the data showed significant changes in the likelihood of reporting),
assuming that police recording practices do not change. The point here is that the analysis of
reporting patterns helps us understand the quality of police-recorded crime statistics. At the
national level, researchers have developed and continue to develop techniques for studying
divergence or convergence between the police- and survey-based crime statistics (see, e.g.,
Lynch and Addington, 2007; McDowall and Loftin, 2007; Rosenfeld, 2007). Clearly, we need
more studies at the sub-national level (for existing work, see Cook and McDonald, 2010; Langan
and Durose, 2004; and especially Lauritsen and Schaum, 2005). The NCS-NCVS MSA database
can serve as a resource for further analysis in this area.
17
Figure 1. UCR Crime Rates (per 1,000 population), 1979 to 2004, Target MSAs
45
New York
Robbery
Aggravated Assault
Burglary
Theft
MVT
40
35
30
25
20
15
10
5
0
79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
45
Los Angeles
40
35
30
25
20
15
10
5
0
50
45
40
35
30
25
20
15
10
5
0
79 81 83 85 87 89 91 93 95 97 99 01 03
45
Philadelphia
(City)
79 81 83 85 87 89 91 93 95 97 99 01 03
45
40
40
35
35
30
30
25
25
20
20
15
15
10
10
5
5
0
0
79 81 83 85 87 89 91 93 95 97 99 01 03
Chicago
Detroit
79 81 83 85 87 89 91 93 95 97 99 01 03
Notes: MSA boundaries are displayed in Appendix 1. Chicago data covered only the city of Chicago.
18
40%
30%
20%
40%
30%
20%
40%
30%
20%
40%
30%
20%
0%
79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
50%
50%
0%
10%
60%
60%
New York
Other MSAs
70%
70%
10%
80%
80%
100%
90%
Burglary
0%
90%
100%
0%
79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
50%
50%
10%
60%
60%
10%
70%
70%
New York
Other MSAs
80%
80%
100%
90%
Robbery
90%
100%
Other MSAs
New York
New York
Other MSAs
79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
MVT
79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Aggravated
Assault
Figure 2. Crime Reporting Rates by Crime Type, Expressed as 3-Year Moving Averages
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
New York
Other MSAs
New York
Other MSAs
19
79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Theft
79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Simple
Assault
Figure 3. Probability that a Victim with Mean Characteristics Calls the Police, 40 MSAs (1979 – 2004)
3a. Violent crimes
3b. Property crimes
0.60
0.60
0.55
0.55
0.50
0.50
0.45
0.45
0.40
0.40
0.35
0.35
0.30
0.30
0.25
Violent Crimes
0.20
0.25
0.20
79 81 83 85 87 89 91 93 95 97 99 01 03
79 81 83 85 87 89 91 93 95 97 99 01 03
3c. Violent crimes by type
3d. Property crimes by type
1.00
1.00
0.90
0.90
0.80
0.80
0.70
0.70
0.60
0.60
0.50
0.50
0.40
0.40
0.30
0.30
0.20
0.10
Property Crimes
Robbery
Aggravated Assault
Simple Assault
79 81 83 85 87 89 91 93 95 97 99 01 03
0.20
0.10
MVT
Burglary
Theft
79 81 83 85 87 89 91 93 95 97 99 01 03
20
Figure 4. Probability that a Victim with Mean Characteristics Calls the Police, 5 Largest MSAs (1979 – 2004)
4a. New York, by crime type
1.00
MVT
0.90
0.80
0.70
Burglary
0.60
0.50
Robbery
0.40
Assault
0.30
Theft
0.20
79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
4b. New York vs. other MSAs: Violent crimes
0.60
0.55
0.50
0.45
0.40
0.35
0.30
0.25
New York
Los Angeles
Chicago
Philadelphia
Detroit
0.20
79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
4c. New York vs. other MSAs: Property crimes
0.60
0.55
0.50
0.45
0.40
0.35
0.30
0.25
0.20
New York
Los Angeles
Chicago
Philadelphia
Detroit
79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
21
5%
5%
Blacks
Hispanics
Blacks
79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Whites
5e. Philadelphia
79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Whites
5b. New York
0%
5%
10%
15%
20%
25%
30%
35%
40%
0%
5%
10%
15%
20%
25%
30%
35%
40%
Blacks
Hispanics
Blacks
22
79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Whites
5f. Detroit
79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Whites
5c. Los Angeles
Note: Philadelphia and Detroit did not have enough Hispanic victims to support the analysis; Annual proportions are expressed as 3-year moving averages.
79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
5%
5%
0%
10%
10%
Hispanics
15%
15%
Blacks
20%
20%
Whites
25%
25%
0%
30%
30%
40%
35%
5d. Chicago
35%
40%
79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
10%
10%
0%
15%
15%
0%
20%
20%
Other MSAs
25%
25%
New York
30%
30%
40%
35%
5a. 40 MSAs
35%
40%
Figure 5. Proportions of Non-Reporting Victims Who Failed to Report Because “Police Wouldn’t Help” (3-Year Moving Averages)
0.12
0.10
0.08
0.06
0.04
0.02
0.00
0.12
0.10
0.08
0.06
0.04
0.02
0.00
79 81 83 85 87 89 91 93 95 97 99 01 03
0.14
0.14
0.18
0.20
0.16
Property crimes
Violent crimes
0.16
0.18
0.20
6a. All races
Theft
Burglary
79 81 83 85 87 89 91 93 95 97 99 01 03
6b. All races
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
0.20
23
79 81 83 85 87 89 91 93 95 97 99 01 03
White
Black
Hispanic
6c. Property crimes, by race-ethnicity
Figure 6. Probability of Victims with Mean Characteristics Believing “Police Wouldn’t Help,” New York (1979 – 2004)
Figure 7. Probability of Victims with Mean Characteristics Believing “Police Wouldn’t Help,”
New York versus Los Angeles (1979 – 2004)
0.20
Property Crimes
0.20
0.18
0.18
0.16
0.16
0.14
0.14
0.12
0.12
0.10
0.10
0.08
0.08
0.06
0.06
0.04
0.04
0.02
New York
Los Angeles
0.00
0.02
Los Angeles
Theft
79 81 83 85 87 89 91 93 95 97 99 01 03
0.20
0.18
0.18
0.16
0.16
0.14
0.14
0.12
0.12
0.10
0.10
0.08
0.08
0.06
0.06
0.04
0.04
0.02
New York
0.00
79 81 83 85 87 89 91 93 95 97 99 01 03
0.20
Violent Crimes
New York
Los Angeles
0.00
0.02
Burglary
New York
Los Angeles
0.00
79 81 83 85 87 89 91 93 95 97 99 01 03
79 81 83 85 87 89 91 93 95 97 99 01 03
24
Figure 8. Other Reasons for Not Reporting Crime to the Police, 1979-2004 (3-Year Moving Averages)
8a. Dealt with another way
(reported to another official; handled informally)
8b. Not important enough to respondent
(minor crime)
45%
40%
40%
35%
35%
Percentage of Non-Reporters
Percentage of Non-Reporters
30%
25%
20%
15%
10%
30%
25%
20%
15%
10%
5%
5%
New York
New York
Other MSAs
0%
79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
8c. Police couldn’t do anything
(e.g., can’t recover property)
8d. Other reason
40%
40%
35%
35%
30%
30%
Percentage of Non-Reporters
Percentage of Non-Reporters
Other MSAs
0%
25%
20%
15%
25%
20%
15%
10%
10%
5%
5%
New York
New York
Other MSAs
0%
Other MSAs
0%
79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
25
Table 1. Area Differences in Rates of Reporting: New York Versus Other MSAs (1979 – 2004)
All Races
Whites
Blacks
Hispanics
(All Years)
NCS
NCVS
NCS
NCVS
NCS
NCVS
Robbery
−
ns
−
−
ns
ns
ns
Aggravated Assault
ns
ns
−
ns
ns
ns
ns
Simple Assault
−
ns
−
ns
ns
ns
ns
Burglary
+
+
+
−
ns
−
+
Motor Vehicle Theft
−
ns
ns
−
ns
−
ns
Theft
−
ns
−
ns
−
ns
ns
Notes: “+” means that reporting rates are statistically significantly higher in New York than in
other MSAs (the actual coefficients are not reported, but available upon request); “−” means that
reporting rates are statistically significantly lower in New York than in other MSAs; and “ns”
means no significant difference (all using .05 level of significance). The incident weights were
used to account for unequal probabilities of selection and observation.
26
Table 2. Results of Logistic Regression Models for Reporting, 40 MSAs (1979-2004)
Independent Variables
Year
Year squared
Year cubed
MSA Location
New York
Los Angeles
Chicago
Philadelphia
Detroit
Other MSA
Control Variables
Incident characteristics
Robbery
Assault
Burglary
Motor vehicle theft
Theft
Attempted crime
Multiple offenders
Gun
Other weapon
Physical force
Serious injury
Minor injury
Property loss
(1)
Violent
Crimes
(2)
Property
Crimes
.023 *
(.002)
.001 *
(.000)
--
.006 *
(.001)
--
-.213 *
(.059)
-.276 *
(.053)
.061
(.063)
-.086
(.082)
-.081
(.061)
--
.101 *
(.039)
-----.420 *
(.041)
.318 *
(.036)
.891 *
(.053)
.225 *
(.038)
.342 *
(.041)
1.124 *
(.089)
.170 *
(.054)
--
(3)
(4)
Robbery Aggravated
Assault
(5)
Simple
Assault
(6)
Burglary
(7)
MVT
(8)
Theft
.050 *
(.011)
.002 *
(.001)
-.0002 *
(.000)
.020 *
(.005)
--
.019 *
(.003)
--
.021 *
(.005)
--
--
--
-.010 *
(.005)
.0003
(.0003)
.00009 *
(.00004)
.001
(.003)
.0005 *
(.0002)
.00007 *
(.00002)
-.231 *
(.029)
-.254 *
(.024)
.056 *
(.028)
.035
(.037)
-.116 *
(.028)
--
-.220 *
(.104)
-.392 *
(.108)
.038
(.134)
-.499 *
(.008)
-.052
(.148)
--
-.174
(.139)
-.278 *
(.103)
-.151
(.134)
-.250
(.169)
-.358 *
(.115)
--
-.189 *
(.087)
-.211 *
(.075)
.155
(.083)
.096
(.107)
.052
(.080)
--
.032
(.062)
-.271 *
(.049)
.074
(.061)
.089
(.084)
.006
(.058)
--
-.281 *
(.100)
-.181
(.099)
-.045
(.132)
.364 *
(.194)
.012
(.145)
--
-.309 *
(.036)
-.260 *
(.029)
.070 *
(.033)
.015
(.043)
-.166 *
(.035)
--
--
--
--
--
--
--
--
-.970 *
(.021)
1.367 *
(.051)
--.338 *
(.037)
--
---
---
---
---
---
---
--
--
--
--
--
--
--.934 *
(.070)
.110
(.074)
1.138 *
(.096)
.367 *
(.079)
.308 *
(.095)
1.563 *
(.190)
.588 *
(.099)
--
--.225 *
(.085)
.400 *
(.071)
1.030 *
(.203)
.403 *
(.195)
.201
.104
1.361 *
(.189)
.273 *
(.123)
--
--.470 *
(.057)
.371 *
(.053)
--
--.523 *
(.055)
--
--2.670 *
(.116)
--
-.083 *
(.041)
--
--
--
--
--
--
--
--
.371 *
(.053)
--
--
--
--
--
--
--
--
--
--
--
--
.053 *
(.008)
.005 *
(.116)
.092 *
(.010)
--
-----.059 *
(.005)
--
27
Table 2. Continued
Intimate partner
Other family
Acquaintance
Victim present
Private location
Series crime
Victim characteristics
Female
Age
Black
Hispanic
Other race/ethnicity
Married
Income
Education
Home ownership
Offender characteristics
Female
Black
Other race
Under age 18
Intercept
Log likelihood
Model chi-square
N of incidents (unweighted)
(1)
Violent
Crimes
-.411 *
(.060)
-.507 *
(.083)
-.340 *
(.036)
-.660 *
(.035)
.121 *
(.057)
(2)
Property
Crimes
---.107 *
(.021)
--
(3)
(4)
Robbery Aggravated
Assault
-.255
-.602 *
(.178)
(.127)
-.715 *
-.449 *
(.263)
(.182)
-.222
-.246 *
(.117)
(.072)
---
(5)
Simple
Assault
-.356 *
(.074)
-.495 *
(.100)
-.384 *
(.045)
--
(6)
Burglary
(7)
MVT
(8)
Theft
--
--
--
--
--
--
--
--
--
.508 *
(.045)
--
-.014
(.128)
--
-.071 *
(.027)
--
-.743 *
(.059)
.608 *
(.084)
-.476 *
(.201)
.530 *
(.071)
.129
(.117)
.705 *
(.045)
.167 *
(.069)
-.597 *
(.108)
-.956 *
(.387)
-.796 *
(.074)
.396 *
(.032)
.070 *
(.008)
.083
(.046)
-.011
(.047)
-.115
(.084)
.365 *
(.035)
-.026 *
(.010)
.030 *
(.005)
.116 *
(.033)
.070 *
(.013)
.067 *
(.003)
.010
(.020)
-.098 *
(.023)
-.129 *
(.038)
.108 *
(.014)
.018 *
(.014)
.049 *
(.002)
-.019
(.015)
.672 *
(.075)
.112 *
(.017)
-.218 *
(.093)
-.440 *
(.102)
-.289
(.172)
.311 *
(.083)
.014
(.024)
.031 *
(.011)
.336 *
(.078)
.304 *
(.068)
.027
(.018)
.114
(.096)
.043
(.094)
-.223
(.172)
.518 *
(.071)
-.012
(.021)
.034 *
(.011)
.151 *
(.065)
.326 *
(.043)
.069 *
(.011)
.194 *
(.066)
.140 *
(.065)
-.017
(.117)
.342 *
(.046)
-.041 *
(.014)
.022 *
(.007)
.043
(.044)
.193 *
(.028)
.045 *
(.007)
.133 *
(.001)
-.077
(.049)
-.110
(.088)
.048
(.031)
.037 *
(.011)
.028 *
(.005)
.102 *
(.034)
.135 *
(.068)
.098 *
(.018)
.155
(.086)
-.112
(.094)
-.134
(.151)
.205
(.070)
.050 *
(.024)
-.003
(.013)
.127
(.074)
.054 *
(.016)
.064 *
(.004)
-.046
(.025)
-.127 *
(.028)
-.139 *
(.044)
.106 *
(.017)
.015 *
(.006)
.058 *
(.003)
-.040 *
(.018)
-.004
(.041)
.070 *
(.036)
-.046
(.050)
-.477 *
(.038)
-1.319 *
(.092)
-18,362
2,920 *
38,021
--
-.103
(.119)
.001
(.081)
.061
(.125)
-.097
(.083)
-1.514*
(.199)
-3,456
840 *
7,891
.130
(.087)
.115
(.074)
-.033
(.099)
-.527 *
(.078)
-1.462*
(.259)
-4,560
519 *
9,187
-.009
(.052)
.057
(.049)
-.124
(.068)
-.602 *
(.054)
-.991 *
(.123)
-10,179
1,319 *
20,943
--
--
--
--
--
--
--
--
--
--
--
--
-1.107 *
(.091)
-15,867
1,017 *
31,181
1.513 *
(.217)
-3,700
2,015 *
11,569
-2.365 *
(.046)
55,491
2,062 *
127,720
----2.216 *
(.037)
-76,336
8,308 *
170,470
Note: * p < .05, two-tailed test.
28
Table 3. Estimated Time Effects in Logistic Regression Models for Reporting, 5 largest MSAs (1979-2004)
(1)
Violent
Crimes
(2)
Property
Crimes
(3)
Robbery
(4)
Assault
(5)
Burglary
(6)
MVT
(7)
Theft
.054 *
(.020)
-.001
(.002)
-.0004 *
(.0002)
2,784
-.022*
(.011)
.002 *
(.001)
.0002 *
(.0001)
10,273
.080 *
(.033)
.001
(.003)
-.0005
(.0003)
1,178
.009
(.012)
--
.005
(.011)
--
.011
(.022)
--
--
--
--
1,606
2,003
1,146
-.031 *
(.012)
.002 *
(.001)
.0003 *
(.0001)
7,124
-.001
(.004)
.002 *
(.001)
--
.026
(.020)
.009 *
(.003)
--
.013
(.010)
---
-.008
(.010)
.003 *
(.001)
--
.002
(.018)
--
Year cubed
.014
(.009)
.002 *
(.001)
--
--
.002
(.005)
.002 *
(.001)
--
N of incidents (unweighted)
3,396
15,553
920
2,476
2,919
1,333
11,301
.031 *
(.010)
--
.005
(.004)
--
-.005
(.023)
--
.038 *
(.012)
--
.001
(.009)
--
.006
(.023)
--
.009
(.005)
--
--
--
--
--
--
--
--
2,157
9,407
556
1,601
1,852
653
6,902
.006
(.006)
--
.028
(.031)
--
.070 *
(.031)
--
.004
(.007)
--
--
--
.036 *
(.017)
.006 *
(.002)
--
-.0001
(.014)
--
Year cubed
.035 *
(.015)
.006 *
(.002)
--
--
--
--
N of incidents (unweighted)
1,387
6,049
339
1,048
1,003
423
4,623
.029 *
(.009)
--
.006
(.005)
--
-.010
(.025)
--
.034 *
(.010)
--
-.004
(.010)
--
-.016
(.021)
--
.012 *
(.005)
--
--
--
--
--
--
--
--
2,217
9,569
429
1,788
1,945
777
6,847
New York
Year
Year squared
Year cubed
N of incidents (unweighted)
Los Angeles
Year
Year squared
Chicago
Year
Year squared
Year cubed
N of incidents (unweighted)
Philadelphia
Year
Year squared
Detroit
Year
Year squared
Year cubed
N of incidents (unweighted)
Notes: The analyses controlled for characteristics of the incident, victim, and offender (see Appendix 2).
* p < .05, two-tailed test.
29
Table 4. Reasons for Reporting and Not Reporting an Incident to the Police, 1979-2004
(a) Reasons for reporting this incident to the police
New
York
17%
Los
Angeles
18%
Chicago
Philadelphia
Detroit
16%
12%
14%
Other
MSAs
15%
45%
46%
38%
33%
35%
39%
To punish offender
36%
35%
32%
27%
30%
34%
Duty to call police (to let
police know about crime)
Other reason
17%
18%
23%
15%
16%
19%
9%
8%
14%
11%
11%
10%
N of incidents (unweighted)
3,402
4,389
2,988
1,762
2,721
35,551
To stop this incident (to get
help)
To recover property
(b) Reasons for not reporting this incident to the police
Dealt with another way
(reported to another
official; handled
informally)
Not important enough to
respondent (minor crime)
Police couldn’t do anything
(e.g. can’t recover
property)
Police wouldn’t help (not
important to police)
Other reason
New
York
Los
Angeles
Chicago
Philadelphia
Detroit
Other
MSAs
15%
16%
20%
21%
21%
22%
37%
33%
37%
37%
38%
38%
24%
28%
24%
22%
22%
24%
21%
16%
13%
12%
14%
12%
20%
19%
22%
21%
19%
18%
N of incidents (unweighted)
8,037
12,398
6,957
4,605
7,529
90,959
Notes: For both tables, incidents include robbery, aggravated assault, simple assault, burglary, motor
vehicle theft, and theft. The incident weights were used to account for unequal probabilities of selection
and observation.
30
Table 5. Estimated Time Effects in Logistic Regression Models for “Police wouldn’t Help,” New York
(1979-2004)
Panel A: without controls
Year
Year squared
Year cubed
Panel B: with controls
Year
Year squared
Year cubed
N of incidents (unweighted)
(1)
Violent
Crimes
(2)
Property
Crimes
(3)
Robbery
(4)
Assault
(5)
Burglary
(6)
MVT
(7)
Theft
-.050 *
(.010)
--
-.018
(.011)
-.008 *
(.001)
-.0003 *
(.0001)
-.023
(.016)
--
-.046 *
(.012)
--
-.020
(.017)
--
--
--
-.117 *
(.029)
.0001
(.003)
.0006 *
(.0003)
-.007
(.012)
-.009 *
(.001)
-.0005 *
(.0001)
-.025 *
(.012)
-.008 *
(.001)
-.0004 *
(.0001)
10,233
-.014
(.022)
--
-.021
(.018)
--
-.056 *
(.016)
--
.023
(.027)
--
--
--
--
--
1,175
1,600
1,997
1,143
--
-.016
(.014)
--2,775
--
-.011
(.015)
-.010 *
(.001)
-.0006 *
(.0002)
7,093
Notes: The analyses in panel B controlled for characteristics of the incident, victim, and offender (see
Appendix 2). * p < .05, two-tailed test.
31
References
Bachman, Ronet. 1993. Predicting the reporting of rape victimizations: Have rape reforms made
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Notes: From 1979 to 2004, New York City accounted for 86% of the New York MSA population. The porportion of MSA residents
living in the central city was lower in other MSAs (39% in Los Angeles, 47% in Chicago, 33% in Philadelphia, and 25% in Detroit).
Appendix 1. Metropolitan Areas in the NCS-NCVS MSA Database (1979 – 2004)
34
Dependent Variable
Police notification
1=incident reported to the police; 0=no
Independent Variable
Year
The year in which the incident occurred (0=1979; 1=1980; so on)
MSA Location
New York
1=yes; 0=no
Los Angeles
1=yes; 0=no
Chicago
1=yes; 0=no
Philadelphia
1=yes; 0=no
Detroit
1=yes; 0=no
Other MSAs
1=yes; 0=no
Control Variables
Incident Characteristics
Robbery
1=yes; 0=no
Assault
1=yes; 0=no
Burglary
1=yes; 0=no
Motor vehicle theft
1=yes; 0=no
Theft
1=yes; 0=no
Attempted crime
1=yes; 0=no
Multiple offenders
1=yes; 0=no
Gun
1=offender had a gun; 0=no
Other weapon
1=offender had other weapon; 0=no
No weapon
1=offender had no weapon, or the victim was not certain whether the offender was armed; 0=no
Physical force
1=offender used physical force (hit or shot the victim with a gun, stabbed or attacked the victim
with a knife, hit the victim with another object, or slapped or knocked down the victim); 0=no
Serious injury
1=victim suffered serious injury (broken bones, loss of teeth, internal injuries, loss of
consciousness, or an undetermined injury requiring hospitalization; 0=no
Minor injury
1=victim suffered other minor injury; 0=no
Property loss
Dollar value of property loss (in hundreds; adjusted to 1999 dollars)
Appendix 2. Description and Summary Statistics for Study Variables
.49
-.27
.29
.23
.19
.22
.47
.40
.40
---.49
.44
.32
.40
.46
.47
.19
.42
--
.42
-.08
.09
.06
.04
.05
.68
.21
.79
---.57
.27
.10
.20
.69
.32
.04
.23
--
Violent Crimes
Mean
SD
-7.67
--
--.18
.07
.75
.11
------
.06
.09
.05
.03
.05
.70
--
.34
35
-33.74
--
--.38
.25
.43
.31
------
.25
.29
.23
.18
.22
.46
--
.47
Property Crimes
Mean
SD
1= offender was a current or former spouse, boyfriend, or girlfriend; 0=no
1=offender was another family member (parent, child, brother, sister, or other relatives); 0=no
1=offender was an unrelated acquaintance; 0=no
1= offender was someone never seen before or someone known by sight only; 0=no
1=victim/other household member present during incident; 0=no
1=incident occurred in or near victim’s home or the home of a friend, relative, or neighbor; 0=no
1=Three or more incidents (or 6 or more in the NCVS) similar in nature and the respondent is
unable to recall details of each incident; 0=no
Note: The summary statistics were calculated using incident weights and the NCVS-redesign weights.
Victim characteristics
Female
1=yes; 0=no
Age
1=12-17; 2=18-24; 3=25-29; 4=30-34; 5=35-39; 6=40-49; 7=50-59; 8=60 & older
White
1=non-Hispanic white; 0=no
Black
1=non-Hispanic black; 0=no
Hispanic
1=yes; 0=no
Other race/ethnicity 1=yes; 0=no
Married
1=yes; 0=no
Income
Level of household income (1 to 6)
Education
Level of victim education (0 to 18)
Home ownership
1=victim/family owned its home; 0=no
Offender characteristics
Female
1=yes; 0=no
White
1=yes; 0=no
Black
1=yes; 0=no
Other race
1=yes; 0=no
Under age 18
1= yes; 0=no
N of incidents (unweighted)
Intimate partner
Other family
Acquaintance
Stranger
Victim present
Private location
Series crime
Appendix 2. Continued
------
.16
.37
.52
.50
.37
.48
.10
.31
.27
.45
38,021
.50
2.15
.46
.37
.32
.18
.50
1.72
3.02
.50
36
-----170,470
.53
4.32
.69
.16
.11
.03
.44
3.82
12.99
.54
Property Crimes
Mean
SD
--------.11
.31
--.02
.14
.49
2.07
.48
.39
.33
.18
.44
1.78
3.15
.50
.38
3.36
.65
.19
.13
.03
.26
3.67
12.14
.47
Violent Crimes
Mean
SD
.09
.29
.03
.18
.28
.45
.60
.49
--.32
.47
.06
.24