Bridging Gaps in Police Crime Data

A Discussion Paper from the BJS Fellows Program

by Michael D. Maltz
Department of Criminal Justice
University of Illinois at Chicago
and 
Visiting Fellow
Bureau of Justice Statistics

September 1999, NCJ 176365

This paper is based on a Workshop
on Uniform Crime Reporting
Imputation, sponsored by the
Bureau of Justice Statistics
and the 
Federal Bureau of Investigation
Uniform Crime Reporting
Program

U.S. Department of Justice
Bureau of Justice Statistics

Jan M. Chaiken, Ph.D.
Director

The points of view or opinions 
expressed in this document are 
those of the author and do not 
necessarily represent the official 
position or policies of the U.S. 
Department of Justice. 

BJS Discussion Papers promote 
the exchange of information, 
analysis, and ideas on issues 
related to justice statistics 
and to the operations of the 
justice system. 

This document was prepared under
cooperative agreement 95-BJ-CX-
0001 for the BJS Visiting Fellowship
Program. 

The author may be reached at the following addresses:  

  Professor Michael D. Maltz  
  Department of Criminal Justice
  University of Illinois at Chicago
  1007 W. Harrison Street (M/C 141)
  Chicago, IL 60607-7140

  email: mikem@uic.edu


Acknowledgments

I began work on this project because, although I had been using 
the UCR for many years, I had never understood all of its 
intricacies -- and felt somewhat embarrassed to ask simple 
questions about why certain procedures were used, because 
obviously everyone else knew. It turned out, however, that most 
people seemed to be as much in the dark as I was, perhaps about 
different aspects of the UCR,and during this project we began to 
share our knowledge, each of us having an understanding of different 
aspects of the data collection and analysis process. This report 
is, then, more a collaboration than a single-authored effort, a 
kind of "open source" presentation of our collective knowledge. 
[Although the knowledge is collective, the interpretation of that 
knowledge is my own.]

The information contained herein is based on a 2-day meeting
held over 2 years ago; analyses of UCR data conducted since
then; and conversations, letters, faxes, and e-mails between me
and a number of colleagues. In particular, I wish to acknowledge
the comments and advice of the following:

*  From BJS -- Jan Chaiken, Larry 
Greenfeld, Tom Hester, Charles
Kindermann, Pat Langan, Sue 
Lindgren, Don Manson, Marilyn
Marbrook, Mona Rantala, Steve 
Smith, Paul White, and Marianne
Zawitz

*  From the FBI -- Yoshio Akiyama, 
Bennie Brewer, Kenneth Candell, 
Carlos Davis, Gil Gee, Antonio 
Hwang, Dawn Kording,Vicki Major, 
Jim Nolan, Sharon Propheter, and 
Maryvictoria Pyne

*  From the research community -- Dan 
Bibel, Becky Block, Roland Chilton, 
Chris Dunn, Bob Flewelling, Jamie 
Fox, John Jarvis, Jim Lynch, 
Mike Maxfield, and Howard Snyder.

In addition, I wish to thank the UCR program personnel from the
50 States with whom my research assistants (Leanne Brecklin,
University of Illinois at Chicago; Chris Kenaszchuk, University
of Maryland; and Todd Minton and Matt Durose, BJS) and I
corresponded and spoke. I received cooperation from officials in
every State in putting together this report; I hope that the end
result is of use to them and to others who deal with crime
statistics.

This report was written while I was a Visiting Fellow at the
Bureau of Justice Statistics and completed while I was on
sabbatical from the University of Illinois at Chicago. While I
greatly appreciate the help I received from BJS, FBI, and State
officials, they should not be held responsible for any errors in
this report, and the opinions, conclusions, and
recommendations expressed herein are my own and should not be
construed as the policy of any of these organizations.

Michael D. Maltz

Department of Criminal Justice
University of Illinois at Chicago

Visiting Fellow
Bureau of Justice Statistics


Summary 

Crime in the United States (CIUS), published annually by the
FBI, is a compilation of the Uniform Crime Reports (UCR)
provided by over 18,000 policing jurisdictions. It represents
one of the two primary sources of data about crime in the United
States, the National Crime Victimization Survey (NCVS) being the
other. While the NCVS is a very reliable indicator of national
trends in crime, it is based on a survey of under 50,000 house-
holds and thus cannot provide local information on crime,
which is provided by the UCR and CIUS.  [For a thorough
understanding of the differences between the two statistical
series, see Biderman and Lynch's (1991) Understanding Crime
Incidence Statistics: Why the UCR Diverges from the NCS.  The
NCS, or National Crime Survey, was the predecessor to the NCVS. 
A briefer explanation can be found in The Nation's Two Crime
Measures, found at http://www.ojp.usdoj.
gov/bjs/abstract/ntmc.htm and included annually in CIUS.]

Not only does CIUS provide local information about crime
incidence, it also compiles arrest data from these
jurisdictions; these data permit us to form a picture of who is
committing crime (or at least, who is arrested for committing
crime).

The quality of the data provided to the FBI, however, is uneven.
Reporting to the FBI remains for many jurisdictions a voluntary
activity; although many States now mandate that agencies report
crime and arrest data to them (which they then forward to the
FBI), even in those States local agencies do not always comply.
Moreover, despite the efforts of the FBI to maintain their
quality, there are many gaps in the data that make their use
questionable. While this has had limited impact in the past, the
fact that the UCR data have, for the first time, been used to
allocate Federal funds brings issues about data quality to
center stage. 

In addition, the FBI is moving to implement an improved crime
and arrest reporting system, the National Incident-Based
Reporting System (NIBRS), to augment the summary UCR data
published in CIUS. It is hoped that the study of deficiencies in
UCR data will be of use in planning for the full implementation
of NIBRS.

This report describes the history of the UCR system and the data
problems that it deals with in reporting crime, arrests, and
homicide. It describes the procedures used by the FBI to fill in
gaps in the data when they exist and makes suggestions about how
they might be improved.

Contents

I. Introduction

Why We Need to Look at the UCR     

     The Information-Gathering Process 
     Report Organization 

II. UCR History and Coverage

     State-Level Reporting 
     Comparing Crime Data 
     Coverage Gaps and Imputation 
     The UCR and Funding Decisions 
     The UCR and Electronic Access

    Use of Sub-National UCR Data 

     The UCR and NIBRS 



III. Incomplete Crime Data Error Checking 

     Reasons for Incomplete Reporting

IV. Incomplete Arrest Data 

V. Processing and Publishing the Crime Data

     Publishing the UCR 
     Archiving the UCR Data File   

VI. Procedures Used for Imputing Crime Data

     FBI Imputation Procedures for Crime
     NACJD Imputation Procedure    
     Imputation Procedures for Arrests
     Imputation and "Zero-Population" Agencies 
     Updating UCR Files

VII. Inaccuracies Produced by the Imputation Procedures

     Incomplete-Reporting Agencies
     Non-Reporting Agencies
     "Zero-Population" Agencies
     Summary

VIII. Suggested Imputation Philosophy 

     Suggested Imputation 
     Zero-Population Agencies
     Incomplete and Non-Reporting Agencies

IX. Supplementary Homicide Reports

     Uses of the SHR 
     Incomplete Provision of SHR Data by Police Departments 
            Updating SHR Files 
     Availability of SHR Data Sets 
     SHR Imputation
     Weighting the Victim File
     Weighting the Offender File
     Problems with This SHR Imputation Procedure

    Suggested Alternative SHR 

         Imputation Procedure 

X. Conclusions and Recommendations 

           Reporting Practices
           Publishing and Archiving
           Imputation
           NIBRS

Appendix A. Persons Attending the
Workshop on UCR Imputation Procedures

Appendix B. State On-Line Publication of Crime Data

Appendix C. Characteristics of State UCR Collection Programs

Appendix D. Extent of UCR Data Coverage, Alabama-Wyoming, 1958-97

Appendix E.  Missing Data in UCR Files Used for the 1996 LLEBG
Formula Calculations 

           Background

           Extent of Missing Data, by State
           Characteristics of Agencies with Less than 36 Months 
                of Violent Crime Data
           Agencies with 0 Months of Data 
           Jurisdictions with between 1 and 35 Months of Data 
           Impact of Incomplete Data
           Addendum on "Zeropop" Agencies    

References

Glossary


I.  Introduction

The Uniform Crime Reporting Program (UCR) of the Federal Bureau
of Investigation (FBI) has been collecting crime and arrest data
from police departments throughout the United States since 1930.
(Footnote: See Glossary for definitions of many abbreviated
names or terms.) The data are published in the annual report,
Crime in the United States (CIUS), and represent one of the more
widely used sources of longitudinal data in the social sciences.
The UCR is based on monthly summary reports of crimes known to
the police and arrests made by the police, that are provided to
the FBI by over 17,000 of the more than 18,000 police agencies
in the United States and its territories. (Footnote:  Police
agencies also report on other topics to the FBI, including hate
crimes, personnel statistics, and law enforcement officers
killed and assaulted. These topics are not covered in this
report.)

The FBI office that deals with the UCR is the Program Support
Section (PSS), a section of the Criminal Justice Information
Services (CJIS) Division. Five of the eight units within PSS are
concerned with various aspects of CIUS: 

*The Statistical Unit collects, checks, and manages the data
coming in from the police agencies.

*The Communications Unit is involved in publications and data
dissemination.

 

*The Education and Training Services Unit trains local agencies
in UCR data collection procedures.

 

*The Crime Analysis Research and Development Unit analyzes data
and develops specifications for new methods of presenting the
data.



*The CJIS Audit Unit performs quality assurance reviews to
maintain the quality of the UCR.



The UCR includes a Crime Index, a count of certain specific
crimes occurring over the past year in each jurisdiction. These
are called "Index crimes," and, listed in order of their
presumptive seriousness, are murder and nonnegligent
manslaughter, forcible rape, robbery, aggravated assault,
burglary, larceny-theft, and motor vehicle theft.  

Arson was added to the Crime Index in 1979 although it is not as
likely as the other Index crimes to be reported to the police,
because arsons are often categorized as "fires of suspicious
origin." Except for arson, these particular crimes were chosen
because they were frequent, generally serious in nature, and
most likely to be reported to the police; victims, their
relatives, and/or bystanders who witness the incident are likely
to know that incidents of those types are criminal in nature and
are likely to report them.

Although the UCR has some limitations (indeed, the aim of this
report is to address some of them), even these limitations
provide important information. For example, incomplete citizen
reporting to the police of certain types of crimes has been used
as an indicator of a number of police-related factors: how the
relationship between offender and victim affects citizen
reporting of crime; the extent to which citizens trust the
police; and the effect of police policies and problems on
reporting behavior. Yet the public is generally unaware that the
UCR system is essentially a voluntary system; there is no
federal legislation that requires states or local jurisdictions
to report their crime data to the FBI.



The voluntary nature of the UCR, of course, affects the accuracy
and completeness of the data. Although the FBI devotes a great
deal of attention to the quality of the data it publishes in
CIUS, it cannot mandate agencies to provide data on time (or at
all). As a consequence, the FBI must deal with problems of
missing or late data, and has developed a mechanism to account
for these gaps: it imputes (or estimates) data where gaps exist,
which limits the accuracy of the estimated crime statistics
published in CIUS.



Why We Need to Look at the UCR



Despite these problems with the data, adjustments for missing
data have not been of major consequence in the past, since the
primary purpose of the data was to present national and State
trends -- and estimates were adequate for this purpose.
Researchers, police administrators, and some journalists are
aware of the limitations of the UCR, but it mattered little to
others outside the field. However, in the recent past four
changes were made in the environment in which the UCR data are
being employed:



*UCR data are being used to allocate Federal funds.



*The data are now instantly accessible on the Internet.



*Because of the greater accessibility of the data, researchers
are increasingly analyzing UCR statistics at sub-national
levels, but the results of their analyses may be suspect because
of the way missing data are handled.



* A new reporting system (the National Incident-Based Reporting
System or NIBRS) now being implemented to augment  the summary
UCR data will increase the amount of data collected on each
crime and arrest.

Thus, the collection, analysis, and publication of crime data
are now occurring in a new environment, due to changes in
legislation, changes in the ease of access by citizens and
researchers to the data, and changes in crime reporting. This
means that the FBI's imputation procedures, which were adequate
for handling many of the weaknesses in the current data
collection system, may have to be revised.

Toward this end, a Workshop on UCR Imputation Procedures was
held in Washington, DC, April 24-25, 1997, and attended by key
personnel from the FBI and the Bureau of Justice Statistics
(BJS), as well as by researchers familiar with UCR data and
their problems. The list of attendees is given in Appendix A.
Just prior to the workshop the FBI had moved the Program Support
Section to Clarksburg, West Virginia. The move resulted in a
turnover of personnel and equipment. The workshop thus came at
an opportune time for the FBI, which recognizes the need to
update the procedures it has been using for over 40 years -- when
the UCR had its last major revision (FBI, 1958). 

The workshop provided an opportunity for statisticians and
researchers from both of these Federal agencies and from the
user community to discuss ways of improving UCR data collection
and estimation procedures. The goal of the workshop was to
recommend new ways to ensure that the American public is
provided with the best possible police- collected information
related to crime and criminality, and to move toward that end in
the most expeditious and feasible way possible. This report is
based on the findings and discussions from that workshop.

Issues relating to standard UCR data (i.e., crime counts,
arrests) were not the only topics addressed at the workshop.
Attention was also devoted to the Supplementary Homicide Reports
(SHR), forms filled out by police departments that provide a
more detailed description of each homicide than just the raw
statistics of number of homicides. The workshop explored how
these data could be made more useful, and this report discusses
those findings as well.

Issues related to Federal crime data are not included in this
report. Thus, the accuracy or completeness of the statistics of
crimes committed on Indian reservations, military installations,
and national parks are for the most part excluded.

This report also includes information gathered from State
criminal justice agency personnel and data analyses subsequent
to the workshop.  

The voluntary nature of the UCR system means that there is a
high degree of State-to-State variation in UCR reporting.
Specifically, some States mandate reporting and require reports
to be channeled through  (and checked by) State agencies before
being transmitted to the FBI, while in other States individual
jurisdictions report directly to the FBI. Although the FBI
institutes quality control checks on the data it receives, the
lack of uniform reporting standards and procedures results in a
lack of uniformity in the Uniform Crime Reports. (Footnote: The
lack of uniformity is due primarily to variation in completeness
of State reporting, not to variation in what is reported. The
PSS Education and Training Services Unit works with individual
police agencies to ensure uniformity in reporting practices.)

The Information-Gathering Process

Some of the material included herein is based on informal
conversations with FBI and BJS personnel and State officials who
use or collect the data, and some of their statements about the
UCR (or my interpretations of what they meant) may be in error.
Although I have tried to verify all statements, some errors may
have slipped through. Should a reader find mistakes in this
report, please notify me (mikem@uic.edu), and corrections will
be added to an errata sheet that will be posted on the BJS
website.

It seems that every decade or so I look into the intricacies of
crime data (Maltz, [1972] 1999; 1984: 141) and find the
following caution about official statistics from Josiah Stamp
(1929: 258) applicable:

"The individual source of the statistics may easily be the
weakest link. Harold Cox tells a story of his life as a young
man in India. He quoted some statistics to a Judge, an
Englishman, and a very good fellow. His friend said, "Cox, when
you are a bit older, you will not quote Indian statistics with
that assurance. The Government are very keen on amassing
statistics  --  they collect them, add them, raise them to the
nth power, take the cube root and prepare wonderful diagrams.
But what you must never forget is that every one of these
figures comes in the first place from the chowty dar [village
watchman], who just puts down what he damn pleases."



While strides have been made in improving the coverage and
accuracy of police-reported crime data (in India as well as in
this country), there is still need for a great deal of
improvement. My hope is that this report helps to realize this
goal.



Report Organization



The organization of this report is as follows: The next section
gives a brief summary of how the coverage of the UCR has
increased over the past few decades, both in terms of population
covered and State collection efforts. Section III describes the
reasons for incomplete crime data and Section IV problems with
arrest data. Section V documents the steps necessary to verify
and publish CIUS. The imputation procedures used by the FBI to
account for these gaps are described in Section VI, and the
problems with these imputation procedures in Section VII. Some
suggested changes in the imputation procedures are described in
Section VIII. Issues related to the SHR data are addressed in
Section IX. Conclusions and recommendations are found in Section
X. 

Five appendixes are included: Appendix A lists the attendees at
the BJS/FBI workshop. Appendix B is a compendium of
crime-related data available on the Internet from State
agencies. Appendix C lists some of the characteristics of State
UCR collection programs. The crime reporting history of each
State is charted in Appendix D. Appendix E, written by Sue
Lindgren of BJS, describes the procedures used to account for
missing data in calculating the Local Law Enforcement Block
Grant funding for each jurisdiction. 

II.  UCR History and Coverage 

The International Association of Chiefs of Police (IACP) created
the UCR in January 1930. It was created in large part to
forestall newspapers from manufacturing "crime waves" out of
thin air (IACP, 1929; Maltz, 1977). A national system of crime
reporting, it was felt, would put the inevitable (and
unpredictable) swings of crime incidence in a single
jurisdiction into a proper context, reducing the media pressure
put on any particular jurisdiction or police chief. This
pressure has led to police departments "cooking the books" and
reducing the amount of crime they recorded instead of the amount
of crime reported to them.

At the request of the IACP, the FBI assumed stewardship of the
UCR in 1930, soon after it started. Police departments that
provided crime (and other) data sent the data directly to the
FBI, which compiled the data and published periodic reports. 

[A note on terminology: the FBI identifies all police and other
agencies that report crime data with an ORI (for ORIginating
Agency Identifier) number. In this report I use the terms "ORI,"
"reporting agency," "police department," and "jurisdiction"
interchangeably.]

Initially there was not enough coverage of the entire United
States to permit estimation of the crime rate for the Nation as
a whole. From 1930 through 1957 the FBI published the data in
tables according to size of the reporting jurisdiction and did
not aggregate the data to the national level. In 1958, based on
a review of the UCR by a consultant committee (FBI, 1958), it
was felt that there was enough coverage to begin to estimate
annual crime rates for the Nation as a whole, which the FBI
began to do with the publication of the 1958 report. 

State-Level Reporting

In the late 1960's a few States that had been compiling their
own statistics arranged with the FBI to act as the data
collection point for all ORIs within the State, and began to
send the entire State's data directly to the FBI, in an effort
to make the process more manageable for the FBI. Other States
also began to compile their crime statistics; under the Law
Enforcement Assistance Administration (LEAA), funds were made
available to the States to establish statewide programs as part
of State criminal justice Statistical Analysis Centers (SACs).
The SACs were developed in an effort to build analytic
capability in States, so that they could deal with their crime
problems by themselves. The FBI then developed requirements for
State UCR collection programs; currently, 44 States have met
these requirements and send all of their agencies' data to the
FBI.( Footnote: The decrease in the early 1980's was due to the
loss of LEAA funding.)  See figure 1, which shows how the
program developed over the past four decades.

----------------------------------------------------------------
Figure 1.  Number of States Submitting 
UCR Data Directly to the FBI

         Number of States
    1969        2
    1970        9
    1971       11
    1972       13
    1973       22
    1974       32
    1975       36
    1976       41
    1977       43
    1978       45
    1979       47
    1980       44
    1981       40
    1982       39
    1983       41
    1984       41
    1985       41
    1986       41
    1987       41
    1988       41
    1989       42
    1990       42
    1991       42
    1992       42
    1993       44
    1994       44
    1995       44
    1996       44
    1997       44

Source: CIUS, 1969-97, and 
States responding by letter, 
e-mail, and telephone  
----------------------------------------------------------------

Although some of the SACs disappeared or were scaled down after
Federal funding declined, States have continued to compile their
own crime data. Most States and territories have set up
State-level UCR programs --some under SACs, some under the State
police, and some in other agencies -- and now publish annual
crime reports, and the FBI continues to compile and publish the
data for the Nation as a whole. Appendix B provides a (partial)
listing of the availability of State on-line publication of crime data.

Comparing Crime Data

The annual publication of CIUS is still an occasion for the
media to compare a jurisdiction's crime rates with those of
other jurisdictions and with its past experience, so media
pressure has not been entirely dispelled. But nowadays reporters
and the public are more sophisticated and recognize that the
police have only a limited ability to affect many types of
crime. This has eased the pressure on police administrators "to
keep the crime rate down" by reporting less crime than had
occurred, although the pressure to falsify crime data still
persists, from Philadelphia to New York City to Atlanta to Boca
Raton. (Footnote: See The Philadelphia Inquirer, November 1,
1998, "How To Cut City's Crime Rate: Don't Report It;"  The New
York Times, August 3, 1998, "As Crime Falls, Pressure Rises To
Alter Data;" The Atlanta Journal-Constitution, May 21, 1998,
"Manipulation of Crime Figures Alleged;" and The Miami Herald,
May 3, 1998, "Sugarcoating? Officer Faked Boca Crime Stats.")

Other problems with the data are due to inadequate systems and
procedures. But as local jurisdictions automate their crime
record systems we should be able to see improvements in crime
data accuracy. New software products for police records
management include provisions for automated reporting of UCR and
NIBRS data in the correct formats. And the accuracy of crime
data seems to have been improving.  As figure 2 shows, the UCR
estimates of violent crime recorded by the police have been
drawing closer to (and following the same general pattern of)
estimates of violent victimizations that victims say they
reported to the police (the two middle lines). 

----------------------------------------------------------------
Figure 2. Four measures of serious violent crime

            Serious Violent Crimes (including homicide)

         Total             Victimizations     Crimes
         violent           reported           recorded by
Year     crime             to the police      the police       Arrests

1973       3,590,500       1,861,000           715,900          392,700       
1974       3,800,000       2,030,000           791,000          462,900       
1975       3,594,700       1,976,100           844,100          441,100       
1976       3,613,300       2,039,600           822,800          414,600       
1977       3,662,500       1,966,800           845,700          438,500       
1978       3,626,100       1,879,800           894,100          469,900       
1979       3,834,800       2,020,500           998,700          467,700       
1980       3,794,400       2,037,500         1,108,300          482,900       
1981       4,101,700       2,217,900         1,125,000          496,600       
1982       3,926,200       2,232,700         1,108,300          547,400       
1983       3,455,500       1,877,400         1,064,900          516,600       
1984       3,683,300       2,003,500         1,081,800          501,600       
1985       3,358,400       1,926,500         1,126,700          506,800       
1986       3,284,700       1,900,200         1,268,800          565,000       
1987       3,424,900       1,987,800         1,265,300          568,100       
1988       3,563,000       1,942,000         1,336,700          600,000       
1989       3,533,700       1,847,800         1,405,000          666,100       
1990       3,500,600       1,949,200         1,556,800          722,400       
1991       3,712,000       2,133,000         1,632,700          738,200       
1992       3,987,000       2,161,000         1,657,300          722,700       
1993       4,191,000       2,218,500         1,648,100          716,100       
1994       4,116,000       2,110,800         1,605,600          778,800       
1995       3,493,500       1,848,600         1,549,900          796,200       
1996       3,260,000       1,740,400         1,444,600          729,900       
1997       3,039,000       1,741,800         1,405,200          717,800
1998       2,776,800       1,587,900         1,319,800          675,900

Note:  The serious violent crimes included are rape, robbery, aggravated assault,  
     and homicide.  Because of changes made to the victimization  survey, data 
     prior to 1992 are adjusted to make them comparable to data collected under 
     the redesigned methodology.  Estimates for 1993 to 1998 are based
     on collection year while earlier estimates are based on data year.    
        
The measures of violent crime come from two sources of data: 
  1)  The National Crime Victimization Survey (NCVS) a household survey 
      ongoing since 1972 that interviews about 80,000 persons age 12 and older 
      in 43,000 households twice each year about their victimizations from crime. 
  2)  The Uniform Crime Reports (UCR) that collects information on crimes and arrests 
      reported by law enforcement authorities to the FBI.
 
----------------------------------------------------------------

Measures of Serious Violent Crime

Source:  http://www.ojp.usdoj.gov/bjs/glance/cv2.htm) (Footnote:
The victimization data are from the National Crime Victimization
Survey (NCVS), which began in 1972.  In it a random sample of
U.S. households is chosen, and household members age 12 and over
are asked about their victimization experiences.  The crime data
are from CIUS, modified to be made comparable to the
victimization data; that is, for robbery it means that all
commercial robberies are excluded, as are rapes, robberies and
aggravated assaults whose victims were under 12 years old.  That
the two sources of data are not converging means that the crimes
that citizens say they have reported to the police are being
recorded more completely by the police in their statistics.  For
a more complete description of the characteristics of the two
crime measures, see The Nation's Two Crime Measures (BJS/FBI,
1995, http://www.ojp.usdoj.gov/bjs/pub/pdf/ntmc.pdf and in the
recent issues of CIUS).)

Coverage Gaps and Imputation 

However, in recent years more and more of the crime statistics
reported by the police have not been based on crime counts but
on imputed crime counts. Figure 3 shows how the percentage of
the population covered by the UCR has changed over time; as can
be seen, a long period of improvement in coverage has been
followed in recent years by a reduction in coverage. (Figure 3.
Percent of the U.S. Population Covered by the UCR  Source: 
CIUS, 1953-97.) This rather considerable decline in population
coverage in the 1990's is due in part to problems at the State
level, in converting their crime reporting systems to comply
with NIBRS requirements.  As problems are dealt with, the
coverage should return to above 95%.

The missing coverage is not uniform over space and time. Most
jurisdictions provide largely accurate crime reports every month
while others do not, for reasons described later in the report.
Since 1969 more and more States have passed legislation
mandating the submission of crime data by local jurisdictions to
state agencies, but very few incur penalties if they do not
comply with such requirements. See Appendix C for a listing of
the characteristics of State UCR reporting procedures.

Appendix D shows the extent to which the 50 States
have provided crime reports to the UCR since 1958, the year that
national and State crime rates were first published. As can be
seen from the patterns, some States have historically been able
to provide close to 100 percent UCR coverage (and therefore low
imputation rates). This response is true of about 20 States.

The infusion of LEAA funding in the 1970's apparently permitted
an additional 12 States to improve their UCR reporting systems. 
All experienced a reduction in the percent of crime imputed in
the 1970's and continue to have low percentages of imputed
crime. Some States, however, have experienced substantial
problems in UCR reporting:

*Complete data for Illinois, for example, have not been included
in the UCR since 1985, initially because the Illinois statutory
definition of sexual assault is inconsistent with the UCR
definition of rape, (Footnote:  "Until 1984, 'rape' was defined
as the carnal knowledge of a female, forcibly and against her
will.  On July 1, 1984, Illinois' sexual assault laws became
gender neutral and the old concept of rape was broadened to
include many types of sexual assault.  This index crime now
includes all sexual assaults, completed and attempted,
aggravated and non-aggravated."  (Illinois Criminal Justice
Information Authority, 1987, p. 5.)) and since 1992 because the
Illinois UCR submissions did not adhere to the UCR's "hierarchy
rule."  

*A number of States have had problems in implementing NIBRS,
reflected in recent major increases in the percentage of imputed
UCR data or in the complete absence of data. 

*In still other States, there has been a recent gradual growth
in the percent imputed, reflecting a gradual withdrawal of local
jurisdictions from the UCR reporting program.

These reporting omissions (i.e., data that are missing or are
reported too late to meet the publication deadline of CIUS) have
generally been considered to be of little consequence, because
most do not account for a significant percentage of overall
crime -- as can be seen in figure 3, despite the reporting gaps
depicted in Appendix D, the UCR still represents 87 percent of
the U.S. population. Moreover, the FBI has developed procedures
to accommodate such omissions. These procedures in essence "fill
in the gaps" by imputing data when the data are either missing
or not furnished to the FBI until after its publication
deadline. Such imputations permit the FBI to make national,
regional, and State estimates of crime data despite the missing
data, and thus keep the annual publication of CIUS on schedule
with relatively comparable data from year to year.

----------------------------------------------------------------
Figure 3.  Percent of the U.S. Population
Covered by the UCR

            Percent of U.S. population
       1953        70%
       1954        75
       1955        80
       1956        81
       1957        83
       1958        88
       1959        88
       1960        88
       1961        92
       1962        93
       1963        92
       1964        91
       1965        92
       1966        91
       1967        92
       1968        92
       1969        92
       1970        91
       1971        92
       1972        93
       1973        93
       1974        94
       1975        95
       1976        96
       1977        98
       1978        98
       1979        98
       1980        98
       1981        97
       1982        97
       1983        97
       1984        96
       1985        97
       1986        97
       1987        96
       1988        96
       1989        96
       1990        96
       1991        96
       1992        95
       1993        90
       1994        90
       1995        90
       1996        88
       1997        87

Source:  CIUS, 1953-97

----------------------------------------------------------------

But researchers have been using county-level data to study crime
characteristics, without realizing that some counties' crime
statistics are based on a substantial amount of imputed data.
(Footnote: Neither estimated city nor county data are
disseminated outside the FBI. They are used solely to arrive at
State and national estimates.)  The county-level data set is
compiled from the raw jurisdictional data provided by the FBI to
the National Archive of Criminal Justice Data (NACJD), which
uses its own county-level imputation procedures (described in
Section IV.). 

NACJD is maintained by the University of Michigan's
Inter-university Consortium for Political and Social Research
(ICPSR). Funded by BJS, NACJD obtains the FBI's archived raw
crime and arrest data sets to archive them on their own website
in a form suitable for research use. NACJD has agency- level
data files from 1966-96 and data files aggregated at the county
level for 1977 through 1996. Imputation procedures used by NACJD
in aggregating data to the county level are described in Section
IV.

As mentioned earlier, in some cases the data for a whole State
have been problematic. In particular, over the past decade some
or all of the data from Delaware (1995), Florida (1988, 1996),
Illinois (1985-97), Iowa (1991), Kansas (1993-97) Kentucky
(1988, 1996-97), Michigan (1993), Minnesota (1993), Montana
(1994-97), New Hampshire (1997), Pennsylvania (1995), and
Vermont (1997) have not been included by the FBI for tabulation
in CIUS, as seen in figure 4. In other words, to develop national 
estimates of crime, data for States have been imputed in whole 
or in part. Imputation to such an extent may no longer be 
appropriate or desirable, especially now that UCR crime data 
are legislatively required to be used in formulas for
allocating certain Federal funds.

------------------------------------------------------------------
Figure 4.  State-Level Reporting of UCR Data to the FBI

Indiana          no State-level data
Mississippi      no State-level data
Missouri         no State-level data
Utah             no State-level data 1970-93 reported UCR data to the FBI 1994-97
South Dakota     no State-level data 1970-92 reported UCR data to the FBI 1993-97
Dist. of Col.    no State-level data 1970-80 reported UCR data to the FBI 1981-97
Washington       no State-level data 1970-80 reported UCR data to the FBI 1981-97
North Dakota     no State-level data 1970-78 reported UCR data to the FBI 1979-97
Connecticut      no State-level data 1970-77 reported UCR data to the FBI 1978-97
Massachusetts    no State-level data 1970-77 reported UCR data to the FBI 1978-97
New Hampshire    no State-level data 1970-77 reported UCR data to the FBI 1978-96  had some problems with data 1997
Wyoming          no State-level data 1970-77 reported UCR data to the FBI 1978-97
Alabama          no State-level data 1970-76 reported UCR data to the FBI 1976-97
Colorado         no State-level data 1970-76 reported UCR data to the FBI 1976-97
Ohio*            no State-level data 1970-76, 1985-97 reported UCR data 1976-84
Texas            no State-level data 1970-76 reported UCR data to the FBI 1976-97
Arizona          no State-level data 1970-74 reported UCR data to the FBI 1976-97
Georgia          no State-level data 1970-74 reported UCR data to the FBI 1975-97    
Hawaii           no State-level data 1970-74 reported UCR data to the FBI 1975-97
Iowa             no State-level data 1970-74 reported UCR data to the FBI 1975-97 had some problems with data 1991
Kansas           no State-level data 1970-74 reported UCR data to the FBI 1975-97 had some problems with data 1993-97
Louisiana        no State-level data 1970-74 reported UCR data to the FBI 1975-97
Maryland         no State-level data 1970-74 reported UCR data to the FBI 1975-97
New York         no State-level data 1970-74 reported UCR data to the FBI 1975-97
Tennessee*       no State-level data 1970-74, 1981-97 reported UCR data to the FBI 1975-80
Virginia         no State-level data 1970-74 reported UCR data to the FBI 1975-97
Arkansas         no State-level data 1970-73 reported UCR data to the FBI 1974-97
Idaho            no State-level data 1970-73 reported UCR data to the FBI 1974-97    
Illinois         no State-level data 1970-73 reported UCR data to the FBI 1974-97 had some problems with data 1985-97
Maine            no State-level data 1970-73 reported UCR data to the FBI 1974-97
Nevada           no State-level data 1970-73 reported UCR data to the FBI 1974-97
New Mexico*      no State-level data 1970-73, 1984-97 reported UCR data to the FBI 1974-83
Oklahoma         no State-level data 1970-73 reported UCR data to the FBI 1974-97
Oregon           no State-level data 1970-73 reported UCR data to the FBI 1974-97
South Carolina   no State-level data 1970-73 reported UCR data to the FBI 1974-97
Michigan         no State-level data 1970-72 reported UCR data to the FBI 1973-97 had some problems with data 1993
North Carolina   no State-level data 1970-72 reported UCR data to the FBI 1973-97
Delaware         no State-level data 1970-71 reported UCR data to the FBI 1972-97 had some problems with data 1995
West Virginia    no State-level data 1970-71 reported UCR data to the FBI 1972-97
Florida          no State-level data 1970 reported UCR data to the FBI 1971-97 had some problems with data 1988, 1996
Kentucky         no State-level data 1970 reported UCR data to the FBI 1971-97 had some problems with data 1989, 1993-97
Minnesota        no State-level data 1970 reported UCR data to the FBI 1971-97 had some problems with data 1993
Alaska           reported UCR data to the FBI 1970-97
California       reported UCR data to the FBI 1970-97
Montana          reported UCR data to the FBI 1970-97 had some problems with data 1994-97
Nebraska         reported UCR data to the FBI 1970-97
New Jersey       reported UCR data to the FBI 1970-97
Pennsylvania     reported UCR data to the FBI 1970-97 had some problems with data 1995
Rhode Island     reported UCR data to the FBI 1970-97
Vermont          reported UCR data to the FBI 1970-97 had some problems with data 1997
Wisconsin        reported UCR data to the FBI 1970-97 

Sources:  CIUS, 1969-96, and responses from State officials by letter, e-mail, and telephone

------------------------------------------------------------------

The UCR and Funding Decisions

In 1994, in reauthorizing the Omnibus Crime Control and Safe
Streets Act of 1968, the U.S. Congress appropriated additional
anticrime funding for jurisdictions under the Local Law
Enforcement Block Grant Program. The amount of funds received by
a jurisdiction was to be based on the number of violent crimes
they had experienced in the 3 most recent years (1992-94).
According to the statute, the UCR was to be the source of the
crime data. 

This marked the first time that funding decisions were to be
made on the basis of the data in the  UCR, and caused a number
of other agencies within the US Department of Justice (DOJ) to
deal directly with the short- comings of this data set. The
Bureau of Justice Assistance (BJA) was charged with the task of
allocating the funds; BJA called on BJS, with its statistical
expertise and know- ledge of the UCR's characteristics, to
develop the allocation formula according to the law's
provisions. [Appendix E gives the background for the development
of this formula.]   

BJS used the actual raw crime data as reported by each police
agency to the FBI, rather than the imputed data, in the
allocation formula. But in reviewing the raw UCR data, BJS
immediately recognized their limitations: Of the 18,413 police
agencies that reported to the FBI in 1992-94, 3,516 (19%) did
not provide crime data for any month during the 36-month period
used in the formula and another 3,197 (17%) reported between 1
and 35 months (table 1). 

----------------------------------------------------------------
Table 1.  Reporting Behavior of 18,413 Police Agencies, 1992-94


                                    Police agencies
Reporting frequency                 Number      Percent
Total                               18,413       100%

Full reporting (36 months)          11,700        64%
Partial reporting (1-35 months)      3,197        17
No reports                           3,516        19
   Special agency                    2,650        14
   Regular agency                      866         5

----------------------------------------------------------------

Although most gaps in the data were found to be relatively 
inconsequential, this was not true across the board. Of the 
3,516 non-reporting agencies, all but 866 either were within 
jurisdictions that had other agencies report for them or were 
State agencies or special police agencies (such as transit 
police, fish-and-game police, or park police) that probably 
were not eligible for a formula award. (Footnote:  But
by not reporting crimes that occurred within the jurisdiction,
they may have affected the statistics of agencies that were
eligible for an award, and thus the crime figures reported to
the FBI for that jurisdiction may be lower than had actually
occurred. In some States an agency reporting as few as five
violent crimes in the 3-year period qualified for a grant of
over $10,000.)

However, the remaining 866 agencies that provided no crime data
for the 36-month period included some major jurisdictions:  the
primary police agencies in 3 cities and counties with
populations over 100,000; 17 cities with populations between
50,000 and 100,000; and almost 200 cities with populations over
10,000. Note that fully 5 percent of the regular police agencies
provided no reports for 3 full years. A subsequent analysis
found that 15 percent of the regular agencies did not provide
any data for 1992, and reporting behavior worsened in succeeding
years (reanalysis by S. Lindgren, May 27, 1999).

In less populous States, even cities with populations of 10,000
received awards. Thus, the lack of complete reporting had
financial consequences for a significant number of
jurisdictions. The legislation does make provision for
determining funding if UCR data are not available, but it may
also serve as a spur for ORIs to improve their reporting
practices.

The UCR and Electronic Access

Another recent change in the crime data environment is the
greater degree of public access to crime data. They have always
been available to the public on paper, in the annual CIUS
publications. For the most part, analyzing the data in the past
usually meant entering data from the paper version of CIUS into
one's own computer. (Footnote:  When I started as a Visiting
Fellow at BJS in early 1995, BJS statisticians were still doing
this on a regular basis.) As discussed earlier, for many years
they have been made available (primarily to researchers) in
electronic form (e.g., magnetic tape), but they are now also
accessible to the general public from various websites. 

The FBI, BJS, and NACJD now have regularly updated websites that
provide access to UCR data, so it can be anticipated that more
people will be encountering the inconsistencies in the data.
Each site contains crime data, but in different forms and
formats:

*The FBI site is http://www.fbi.gov;
it contains the UCR data as published in CIUS, beginning in 1995.

*The BJS site is http://www.ojp. usdoj/bjs In the section Crime
and Justice Electronic Data Abstracts (CJEDA), it provides UCR
crime data by State from 1960 to 1997, UCR crime and arrest data
for the 90 largest counties for 1990-96, and 1985-97 homicide
data for cities with populations over 100,000.

*The NACJD site is  http://www.icpsr.
umich.edu/nacjd/ucr.html; it contains downloadable arrest and
offense data at the agency level from 1966 to 1996 and at the
county level from 1977 to 1996.

The State estimates provided by the FBI (and found on the BJS
website) are based on the FBI's imputation and estimation
procedures and are not directly comparable to NACJD's
county-level data. Now that any person in the world with a
computer and modem can download the data and do comparisons, it
would be helpful to resolve the data inconsistencies as much as
possible and to provide explanations for the inconsistencies
when resolution is not possible. (Footnote:  Many of the
downloadable data sets currently contain explanations for some
inconsistencies (see, for example, the data set at
http://www.ojp.usdoj.gov/bjs/dtdata.htm#crime), but the
explanations are not complete.)

Use of Sub-National UCR Data

The ready availability of UCR data at the subnational level has
resulted in researchers using these data to answer policy
questions. Unfortunately, the data may not be up to the task. To
understand why this is so, a brief account of the history of
their collection, aggregation, and initial uses would be
beneficial.

Prior to 1958, UCR data were collected from individual
jurisdictions and aggregated to give, for each crime type in the
crime index: urban and rural crime rates and year-to-year
changes; crime counts by size of city (for reporting cities) and
year-to-year changes; crime counts by State for reporting cities
in each State, and year-to-year changes; and crime counts in
cities by size of city, and year-to-year changes. State-level
data were based on only those cities that reported to the FBI, and
State-level crime rates were calculated by dividing the crime
counts for these cities by their aggregate population.

State-level data. Despite the known deficiencies in the data,
the UCR State-level homicide data for the years 1930, 1940,
1950, 1960, and 1970 were used by Ehrlich (1975) to estimate
that every execution deters eight homicides, a finding that the
U.S. Supreme Court cited (Maltz, 1996, p. 36). Critics pointed
out some of the analytic problems, but it was assumed that
State-level homicide data would be more accurate than data
concerning other crimes. 

However, the State-level homicide rates for 1930, 1940, and 1950
were doubtless based on jurisdictions covering less than 70
percent of the Nation.  (See figure 3.) The variation in
coverage from State to State was probably considerable. Although
I have not examined the data from this era, it seems likely that
much of the reporting was from urban agencies. Thus, a State
that was 75 percent rural and 25 percent urban, but in which the
urban agencies were much more diligent than rural agencies in
reporting UCR data, would have its homicide rate based primarily
on the homicide experience of its urban areas rather than on the
experience of the State as a whole.

County-level data. In 1983 BJS published Report to the Nation on
Crime and Justice (Zawitz, 1983), a snapshot of the state of
crime and justice at that time. It featured a choropleth map of
the county-by-county violent crime rate in the United States in
1980 (Figure  5. Map: County-Level UCR Violent Crime Rates, 1980 
Source:  Zawitz, 1983). (Footnote: A choropleth map of crime
displays levels of crime with different shadings or colors.) To
produce this map, BJS tasked NACJD with estimating the crime
rate of each county. This was done by aggregating the crime
count for all the jurisdictions in the county and dividing by
the aggregated population for those jurisdictions. 

Some counties reported no data; they are represented in white in
the figure. ORIs that did not report at least 6 months of data
to the FBI were also excluded; those that did report 6 months or
more, but provided less than 12 months, had their data imputed.
The imputation procedure simply multiplied the violent crime
rate by 12/N, where N was the number of months reported. This
implicitly assumes that the crime rate for non-reporting months
is the same as for the reporting months. 

Moreover, if some agencies in a county did not report, or
reported less than 6 months of data, their data and their
population were excluded from the crime rate calculations. This
implicitly assumes that the crime rate for nonreporting ORIs is
the same as for the reporting ORIs in the county, which is
probably a stretch.

It should be noted that the imputation procedure was developed
as an ad hoc procedure to make the 1980 data reasonably
comparable from county to county so as to provide a snapshot,
and not as a final means of dealing with missing data.

However, this report was received so favorably that BJS decided
to update it.  In 1988 it released the second edition of Report
to the Nation on Crime and Justice (Zawitz, 1988a), based on UCR
data from 1984.  (See Figure  6. Map: County-Level UCR Violent Crime
Rates, 1984  Source: Zawitz, 1988a.) NACJD used the same
imputation procedure to fill in the missing data. (Footnote: The
procedure is described in Zawitz (1988b), p. 8.)

Because of favorable reception of the reports and the data on
which they were based, BJS decided to make county-level data
sets routinely available through NACJD. The deficiencies or
consequences of using the ad hoc imputation procedure were not
considered, because up to that time the county-level data had
only been used for cross-sectional comparisons and not for more
rigorous analytic purposes.

Since then, however, these data have been used for other
purposes. For example, a recent study used the data to conclude
that right-to-carry laws reduce crime (Lott, 1998). This finding
was contested on methodological grounds (Black and Nagin, 1998),
but not from the standpoint of the data quality. It turns out
that smaller counties are more likely than the larger counties
to have a significant fraction of their data imputed (C. Dunn,
at the 1997 workshop); the fact that smaller counties are more
rural may have a decided effect on this analysis.

One data documentation feature that NACJD now uses (until an
improved imputation procedure is implemented) is a "coverage
factor" in the county-level data set. This feature (described in
Section VI) at least warns the analyst that the data are limited
in coverage.

The UCR and NIBRS

A fourth change in the crime data picture concerns the way crime
data are to be reported to the FBI. Over 10 years ago a study
commissioned by the FBI and BJS provided a "blueprint" for
changing the way crime data were to be reported to the FBI
(Poggio et al., 1985). The recommended changes have been adapted
and incorporated in a set of new procedures that comprise NIBRS;
NIBRS has already been implemented in a number of states and is
expanding to cover the entire United States.

The change in data collection is considerable. Under the UCR
program an agency provides a monthly summary report of crime,
called Return A (Figure 7.  Replica of the FBI's UCR Return A);
each line of the report refers to a single type of crime; it
contains a count of the number of crimes of that type that had
occurred in that month. Under NIBRS each incident is to be
reported in detail, with a number of records devoted to
describing the characteristics of each crime. For a single 
incident, information is recorded for each included offense
(type, weapons, location, motivation method of entry, etc.);
victim, offender, and arrestee; type of property; and so on. 
See figure 8 (Figure 8. Diagram: The Structure of NIBRS Data Elements
Source:  Akiyama and Nolan, 1999a). NIBRS will provide a great
deal of detail about the nature of criminal activity: for
example, one will be able to determine to what extent aggravated
assaults were committed by family members or strangers, or what
fraction of burglaries occurred in apartments or in private
homes, by time of day, and in other ways. This will give both
the police and the public with detailed information on the risk
of crime to enable them to develop more useful policies and
tactics.

One of the problems with current UCR reporting that should be
ameliorated by NIBRS is caused by a characteristic of the UCR
system known as the hierarchy rule. The hierarchy rule for
reporting crimes was instituted by the FBI in the 1930's, to
ensure that there would be no double-counting of crimes. A
criminal event that includes two different crime categories is
thus counted only once, and only in the most serious crime
category. For example, if a convenience store robbery results in
the death of the store clerk, this would be classified as a
homicide rather than a robbery -- because homicide is a more
serious crime than robbery. Yet this expedient, important in the
pre-computer age, masks the nature of what happened. It would
certainly be better to recognize both characteristics of the
incident, if only to be able to provide an estimate of risk, in
the form of the fraction of incidents that start out as
robberies but result in homicide (see, e.g., Maltz, 1976b).

Even with NIBRS implemented, summary data will doubtless be
aggregated and compiled for each agency, and data for some
agencies may continue to be missing, delinquent, or in error. In
other words, there will still be a need for imputation
procedures after NIBRS is implemented nationwide. In fact,
missing data may become a greater problem under NIBRS, because
of the huge increase in categories and the complexity of
definitions. This may make it more difficult to assume that the
counting rules and definitions are being applied uniformly.

III. Incomplete Crime Data

Two separate streams of crime data are sent to the FBI's Uniform
Crime Reporting Section: one from individual police agencies,
the other from State UCR collection programs. Although 36 States
now have statutes mandating the reporting of crime and other
criminal justice information, not all police departments submit
this information to their State agency designated to collect the
data, or they may submit it too late for entry in CIUS.

Occasionally some of the data may appear to be in error -- too
high or too low, based on the jurisdiction's past crime
experience. This section describes the procedures used by the
FBI to correct errors in reporting and, when reporting gaps
occur, to impute data as necessary. It also discusses when and
how UCR data files are updated.

Error Checking

Potential errors in the data are checked in different ways,
depending on how the UCR reports were sent to the FBI and
depending on the size of the jurisdiction. If the data are first
collected by the State agency, that agency itself may undertake
follow-up procedures to verify the data. When the errors are
glaring, they can be found by simply inspecting the data or by
using simple graphical techniques. One State agency refers to
such errors as "tent poles" and "craters" -- excessively high or
low figures compared to the surrounding data (R. Christ,
personal communication). Such errors often come from transposing
numbers in returns submitted manually. Small errors, however,
will probably not be caught in this manner.

If the State does not have auditing procedures, or if the data
are sent directly to the FBI, staff members in the FBI's UCR
Section may note the omission or anomaly and request the State
agency to follow up. In cases in which the data are sent
directly to the FBI, the FBI may follow up with the police
agency by mail. If, however, the agency has a population of over
100,000, personnel from the UCR Section call the agency directly
to verify the data (D. Kording, at the 1997 workshop).

When errors are found in the data, they are corrected, and the
corrected counts are included in the statistics. Depending on
when the errors were discovered and corrected, they may not be
incorporated in CIUS (if the corrections occur after the FBI's
publication deadline), but they may be included in the
public-use data set archived at NACJD (J. Lynch, at the 1997
workshop). This means that someone trying to determine the
extent of crime in a jurisdiction will encounter unexplained
differences between CIUS statistics and the data archived by
NACJD.

Reasons for Incomplete Reporting

Aside from these errors in reporting, police agencies may not
provide complete (or any) reports to the FBI. The agencies may
be delinquent or incomplete in their reporting of crime for a
number of reasons:

*Some agencies experienced natural disasters that prevented them
from getting their data in on time (or in some instances, at
all).

*As has been the case with other public agencies, budgetary
restrictions on the police have meant that some agencies have
had to cut back on services. Although crime reporting is
considered an essential function because it provides information
about community safety to the public, some agencies that are
especially strapped may forgo these routine clerical activities
so as to ensure that sufficient resources exist for patrolling
the streets.

*Retirements, promotions, and other personnel changes may mean
that the person experienced in the preparation of UCR crime and
arrest data is replaced by someone -- 

-- who has little experience in its preparation (and
consequently makes numerous errors)  

-- who is not given sufficient training 

-- who gives the task a low priority 

-- or who doesn't prepare the data in a timely manner.



*With respect to training, some jurisdictions may rely
completely on handbooks on UCR reporting produced by the Program
Support Section, and there may be ambiguities in the reports
that require more complete descriptions than are included in the
handbooks.

*Phasing in a new reporting system or computerization of the old
system may cause delays or gaps in the crime reporting process. 
This may be especially true as agencies convert to NIBRS. (See
Appendix D.)

*Small agencies with little crime to report may feel it
unnecessary to fill out reports that are filled almost entirely
with zeros. [In fact, in some cases small agencies file reports
for only 1 month; they want to ensure that their agencies'
employee statistics are included in CIUS, and reporting their
data for 1 month will accomplish this.]

*A State may have offense definitions that are incompatible with
UCR definitions, leading to data being submitted but not
accepted.

Thus, there are a number of reasons that crime reports may be
incomplete, late, or in error. The extent to which this is a
problem in an individual State can be seen in Appendix D (page
47), which shows the UCR reporting behavior of each State over
the past 40 years. Note that the impact of LEAA funding of State
statistical systems in the 1970's is apparent in these graphs,
as is (in some States) the impact of its termination.  

Note also that while some States have a history of consistently
good reporting, other States have a history of consistently poor
reporting, and yet others have exhibited highly erratic
reporting behavior. In particular:

*The data for six States were excluded from the 1997 UCR, with
the data from one of those States not having been included since
1993.

*Six States have consistently poor reporting, missing reports on
the crime experienced by more than 20% of their population.

Some of the recent erratic reporting by States is attributable
to their conversion to NIBRS. In particular, some States and
agencies that have begun the NIBRS conversion process are
working with software that currently does not have the ability
to produce UCR reports. Over the long term we can expect that
many of these reporting problems will disappear or at least
diminish.  Many smaller agencies that are currently automating
are purchasing computer software that provides near-automatic
reporting (including audit checks) of these data. However, in
the near term we can expect these problems to continue, for
standards for such software do not currently exist.  (See
Appendix C.)

Incomplete Arrest Data

As with offense data, there are major gaps in arrest data. To
some extent the problems with arrest data are greater because of
three factors:

*The percent of arrests reported by police is substantially
lower than the percentage of crimes reported.



*By publishing the characteristics of arrestees, there is an
implicit assumption that they also characterize those who commit
similar crimes but are not arrested.

*Whereas crimes reported to the police are generally considered
to be (and have been shown to be) similar to crimes not reported
to the police, arrests reported by the police are as much a
reflection of police priorities as they are of criminal activity.

Agencies are less diligent in reporting arrest data than crime
data. The FBI attempts to ensure completeness of arrest data by
rejecting an agency's adult arrest data if it does not also send
in juvenile arrest data, nor is arrestee race information
accepted without age and sex information (V. Major, at the 1997
workshop). This means, however, that information on arrests is
considerably less complete than information on crimes.

Moreover, the arrest data published in CIUS are biased even in
comparison to the arrest data eventually reported to the FBI.
(Footnote: The FBI accepts data that it receives after its
publication deadline date and includes them in data files sent
to NACJD, so the data files include reports not included in
CIUS.) For example, Snyder compared the 1980 CIUS arrest data
with the final counts of arrests, after all of the
late-reporting ORIs submitted their data (H. Snyder, personal
communication, 1999). He found that juvenile arrests (as a
percentage of all arrests) were overrepresented in the published
statistics. He attributed this to the fact that large urban
agencies, with higher percentages of juvenile arrestees,
generally reported early (in time for publication) and the late
reporters tended to be less urban agencies, with lower percent-
ages of juvenile arrestees. So, not only are the arrest data
published in CIUS not a representative sample of all arrests,
they are not a representative sample of arrest data eventually
reported to the FBI.

Figure 9 shows how 1997 arrest reporting varies by State. As can
be seen, 4 States and Washington, DC, did not provide acceptable
arrest data, and more than half of the population was not
represented in an additional 12 States. 

---------------------------------------------------------------
Figure 9.  Percent of Population Covered in Arrest Data 
Reported to the FBI, 1997

                       Percent of population
State                  covered by reported arrest data

District of Columbia                 0%
Florida                              0
Kansas                               0
New Hampshire                        0
Vermont                              0
Kentucky                            19
Illinois                            23
Georgia                             33
Mississippi                         37
Montana                             39
Delaware                            41
Tennessee                           42
Alaska                              44
South Dakota                        45
Nevada                              46
New York                            46
Pennsylvania                        47
Ohio                                55
Indiana                             57
Missouri                            60
Washington                          61
New Mexico                          68
Colorado                            70
Utah                                75
Wisconsin                           76
Louisiana                           79
Michigan                            81
Iowa                                82
Massachusetts                       83
Arizona                             85
Connecticut                         85
Oregon                              87
Arkansas                            89
North Dakota                        90
Alabama                             94
Maine                               94
Nebraska                            94
West Virginia                       96
New Jersey                          96
Hawaii                              97
Texas                               97
Idaho                               98
Wyoming                             98
Virginia                            98
California                          99
North Carolina                      99
South Carolina                      99
Minnesota                          100
Maryland                           100
Oklahoma                           100
Rhode Island                       100

Source:  CIUS, 1997

---------------------------------------------------------------

Figure 10 shows the extent nationally to which arrest data have
been reported to the FBI for the past four decades. The percent-
ages are consistently lower than those for crime data (cover
figure), but their time trend shows the same declining pattern.
As with the decline in crime reporting, it is probably
attributable for the most part to the difficulties in shifting
to NIBRS. (Figure 10.  Percent of  U.S. Population Represented
by Agencies that Provide Arrest Data  Source:  CIUS, 1960-97)

---------------------------------------------------------------
Figure 10.  Percent of U.S. Population Represented
by Agencies that Provide Arrest Data

            Percent of U.S. population
       1960           61%
       1961           63%
       1962           67%
       1963           68%
       1964           70%
       1965           69%
       1966           70%
       1967           73%
       1968           73%
       1969           72%
       1970           75%
       1971           76%
       1972           77%
       1973           74%
       1974           64%
       1975           84%
       1976           82%
       1977           88%
       1978           95%
       1979           93%
       1980           92%
       1981           94%
       1982           81%
       1983           86%
       1984           76%
       1985           85%
       1986           82%
       1987           83%
       1988           77%
       1989           81%
       1990           78%
       1991           75%
       1992           84%
       1993           83%
       1994           80%
       1995           74%
       1996           72%
       1997           68%

Source:  CIUS, 1960-97


---------------------------------------------------------------

Note that compared to the reporting of crime data, there is a
greater degree of annual variation in the reporting of arrest
data.  Year-to-year differences of close to ten percent in the
reporting population are not uncommon. This variation is due in
part to the changes in reporting standards for arrests.

For example the large "notch" in arrest reporting in 1974 was
probably due to the changeover from annual to monthly arrest
reporting, which took some time for agencies to systematize (V.
Major, personal communication, May 20 and August 23, 1999). In
prior years only agencies that reported arrests every month
(i.e., were "12-months complete") were included in the arrest
tallies; starting in 1974, when monthly arrest data began to be
collected, arrest data were aggregated for all agencies with 6
months or more of arrest data. This changed again after 1981;
from 1982 on, the FBI reverted to reporting aggregate arrests
for only those agencies that provided 12 months of arrest data.
The effect of this change can be seen in the 1981-82 drop in the
population represented in arrest reports.

This lack of completeness and consistency (and, more
importantly, lack of representativeness) of the reporting of
arrest data can have major consequences because of implicit
assumptions made by some individuals in "analyzing" the arrest
data. For example, Snyder shows how these arrest data have been
used improperly to infer offense rates of juveniles (Snyder,
1999).

V.  Processing and Publishing the Crime Data

After the data are received by the FBI (specifically, by the
Program Support Section of the Criminal Justice Information
Services Division), they are stored in a data file that contains
(among other data) offense data for each ORI taken from Return A
(figure 7): month-by-month counts of each of the offenses
listed. A computer program processes this file, in which the 12
months of data for each offense are summarized by two numbers:
total for that offense and number of months reported.  A page of
output from one of the many programs used by the FBI to process
the data is shown in Figure 11. (Figure 11. Part of the Printout
of the FBI's Crime-by-County File )  This output file, "Crime by
County," is one of then more widely distributed files. Note that --

*The ORIs are grouped by State and by county within each State. 

*An ORI's population and its Standard Metropolitan Area (SMA --
not the same codes as used by the U.S. Bureau of the Census) and
population group indicators are given. (See table 2.)

----------------------------------------------------------------
Table 2.  FBI Classification of Population Groups

Population  group   Political  label   Population  range
I                   City               250,000 and over
II                  City               100,000 to 249,999
III                 City               50,000 to 99,999
IV                  City               25,000 to 49,999
V                   City               10,000 to 24,999
VI                  City/a             Less than 10,000
VIII (Rural County) County/b           . . .
IX (Suburban County County/b           . . .

Note:  Group VII, missing from this table, consists of cities with population 
under 2,500 and universities and colleges to which no population is attributed
For compilation of CIUS, Group VII is included in Group VI.
a/Includes universities and colleges to which no population is attributed.
b/Includes State police to which no population is attributed.

----------------------------------------------------------------

*Both the total number of Index crimes and modified Index crimes
(the first seven Index crimes plus arson) are given. (Footnote:
The arson totals provided in figure 11 are summaries of all
arson on file for the respective agencies and may not be
representative of the number of months reported in the MO
column.)

*Although not apparent from the printout, only the reporting
ORIs are included in this compilation.  If an ORI does not
submit any UCR data for a year, it is omitted from the data
file, and its population is not included in the county total.

Publishing the UCR

The deadline for submitting UCR data to the FBI is late March of
the following year. Data submitted beyond this date are accepted
by the FBI and incorporated in the data file until the FBI
closes out the data file for that year. The date that this file
is closed out is not fixed; for example, the 1997 file was not 
closed out until early April 1999.

The paper version of CIUS for a given year is published in the
fall of the following year, usually in October or November.
Between the March cutoff date and the publication date the staff
of the Program Support Section perform the error checks and
prepare the data for publication.



January and February are devoted to writing to the larger ORIs
to verify data and/or to request missing months; listings of
missing months are routinely prepared during this period. Data
from agencies that do not provide 12 months of data are analyzed
to identify any month(s) deviating from agency norms due to
special circumstances affecting those agencies (e.g., floods,
tornadoes, and fire). By early March all agencies with
delinquent data have been contacted. By mid-February population
estimates are calculated, based on Census Bureau data, and
included in the raw data file.

By mid-April the data processing unit prepares a preliminary set
of tables; this permits the Crime Analysis Research and
Development Unit to begin to look for patterns in the data and
draft the text and analysis for the report. In addition, the
tables are sent to the outside contractor to format the tables 
for printing.

The material for CIUS is sent to an contractor in three
installments. The first installment is delivered to the
contractor in May. It consists chiefly of the appendixes, which
have few tables and do not change much from year to year, the
methodology section, and tables of law enforcement personnel,
which had been collected from the ORIs in October. Over the next
month the FBI  corrects the proofs and returns them to the
contractor twice in succession.

Installment 2, consisting chiefly of Crime Index Offenses
Cleared, is sent to the contractor in early June and, as with
the first installment, is proofread and returned to the
contractor twice for revision over the next month.

The third installment consists primarily of tables and text on
offenses and arrests, as well as the program summary (Section I)
and the inclusion of some data that were omitted from sections
that had been prepared earlier. For example, the schedule for
the 1998 CIUS projects completion of these three installments by
the end of July, and a final check of the entire report by early
August.

Archiving the UCR Data File

At the request of BJS, the raw data file is provided to NACJD
for archiving. The file is then restructured by NACJD and
additional fields are included to make it more accessible for
research purposes. In the past this restructuring has resulted
in errors such as mismatched fields; consequently,  the FBI
cannot respond to queries about the data archived by NACJD.

The file that NACJD archives is not the one used to produce
CIUS; rather, it is the updated file that contains the
additional data received by the FBI after the March publication
deadline. This has meant that analyses using the raw data cannot
be compared to the tables in CIUS, because they are based on
different data sets.

NACJD also produces county-level files from the raw data file
(see Section II) which are available for downloading from NACJD
(http://www.icpsr.umich.edu/nacjd/ucr.html) . Notes accompanying
those files state that "UCR county-level files are not official
FBI UCR releases and are being provided for research purposes
only. Users with questions regarding these UCR county-level data
files can contact the National Archive of Criminal Justice Data
at ICPSR."



Thus, two sets of UCR data are made available to the public.
One, published in CIUS and available through the FBI website,
contains the data sent to the FBI before its publication cutoff
date. The other, available through the NACJD website, contains
data sent to the FBI before its data file cutoff date, which may
be considerably later than the publication cutoff date. The
difference between the two is usually not great, but has led to
some misunderstandings.

VI.  Procedures Used for Imputing Crime Data

It should be noted at the outset that the FBI does not publish
or release data that include imputations below the State level.
Its imputation procedures are used solely for estimating crime
rates at the State and national levels.  NACJD, however, does
publish offense data that have been imputed at the county level.
In this section we describe the imputation procedures used by
both the FBI and NACJD.

FBI Imputation Procedures for Crime

Whether an ORI reports through the Summary UCR Program or
through NIBRS, the FBI will still need to use imputation
techniques that allow it to make reasonable estimates of crime
and arrests. The imputation procedures used by the FBI for
estimating crime rates are described below. 

Since 1958 the FBI has used two different means of imputing
crime data for a police agency. (Footnote: Crime data are not
imputed for all agencies; see "Imputation and Zero-Population
Agencies.") If the agency reports 3 or more months, one
procedure is used, while another is used when less than 3 months
is reported.

Partial Use of Data. If an agency has provided reports of crime
data for 3 or more months, the imputation procedure is based on
those reports. The total annual crime for that jurisdiction is
estimated by multiplying the reported number of crimes by 12/N,
where N is the number of months for which reports exist. Thus,
an agency that reports 4 months of crime data (a third of the
year) would be estimated to have 12/4, or 3 times the number of
crimes that it reports for that period.

Data Not Used. If an agency reports for 2 or fewer months, the
number of crimes is estimated from scratch. These agencies are
considered to be nonreporting agencies, and the FBI bases the
imputed data for such agencies on the crime rates for the same
year for similar agencies. "Similar agencies" are considered to
be those in the same Population Group in the same State, but
only those that provided 12 months of data. Table 2 (page 21)
shows how the FBI categorizes these groups. Thus, if an agency
in Alabama with a population of 150,000 reports 2 months or less
of crime data in 1997, and the 1997 aggravated assault rate for
Group II agencies (population between 100,000 and 249,999) in
Alabama is 620.2 per 100,000, then the agency is estimated to
have had 930.3 (620.2 x 150,000 / 100,000) aggravated assaults
for 1997. (Footnote:  If there are no comparable ORIs in the
State, the estimate is based on the rates of occurrence in the
region: New England, Middle Atlantic, East North Central, etc.)

NACJD Imputation Procedure

Every year NACJD obtains a data set from the FBI containing the
raw UCR figures from the FBI, archives it, and uses it to
develop a file containing crime and arrest data for each county
in the US. NACJD also has to contend with missing data, in
aggregating to the county level. It has used two different
imputation procedures: one for the 1980-93 data sets and the
other for datasets from 1994 onward.

As stated earlier, the original county-level imputation
procedure was developed to be used to plot crime by county for a
single year, 1980. When BJS decided to continue providing
county-level data through NACJD, they continued to use the same
imputation procedure, similar to the FBI procedure but with a
different cut point: agencies that provided reports for 6 or
more months were estimated to have 12/N crimes, where N is the
number of months reported.

Those reporting less than 6 months were estimated to have the
same offense rates as the rest of the county (not State and
population group). If an agency with 10 percent of a county's
population provided reports for 5 months or less, then the
county's rates were used for that agency, and the estimated
number of crimes for the county became C/.9, where C is the
number of crimes reported by the rest of the county. However,
from the 1994 data file onward NACJD has used essentially the
same imputation procedure as the FBI.

Coverage Factor.  More recently, in consultation with BJS, NACJD
began to include a new element, "coverage factor," in its
county-level data files. This factor represents the extent to
which the crime and arrest figures for a county are based on
real data and the extent to which they are based on imputed
figures. For example, if a county with a population of 500,000
includes an agency with a population of 100,000 that reported
for only 9 months (all other agencies reporting fully), then the
coverage factor for that county would be 1.00 - (3/12) x
(100,000/500,000) = .95, or 95 percent, because a fourth of the
data are missing for 20 percent of the county population. If
that agency reported for 2 months or less, then the coverage
factor would be 1.00 - (12/12) x (100,000/500,000) = .80, or 80
percent, since all 12 months are considered missing. This does
not correct the problems of imputation so much as it puts the
users of the data on notice that the data they are using have
been estimated to some degree.

Imputation Procedures for Arrests

Arrest data are missing to a much greater extent than crime
data. Imputing the missing arrest figures thus becomes much more
difficult. The imputation procedure used by the FBI to account
for missing or late arrest data is applied to all ORIs that
report fewer than 12 months of data, i.e., are not "12-months
complete."  It is similar to that used for crime data, with two
exceptions: first, arrests are estimated only at the national
level; and second, instead of basing the imputation on the
arrest rates in the same population group and State, it is based
on the arrest rates in the same population group throughout the
country.

NACJD has used a similar arrest imputation procedure since the
1994 data year, except that data for the nonreporting agencies
(2 or fewer months) are imputed based on the arrests rates in
the same population group and the same State.

Imputation and "Zero-Population" Agencies



In compiling its crime and arrest statistics, the FBI tries to
ensure that both the numerators (number of crimes and arrests)
and denominators (number of people) are based on accurate data
and estimates. This means that populations that are policed by
more than one agency should be counted only once. Some
jurisdictions are policed by State or county police departments,
or even by other cities. For example, Chicago is in Cook County,
Illinois. But the number of crimes reported by the Cook County
Sheriff's Police Department (CCSPD) is for the areas policed by
the CCSPD, and only for those areas (both unincorporated areas
and municipalities that contract with CCSPD); it does not
include the crimes in Chicago or any other Cook County
jurisdiction not policed by the CCSPD. Since the crime rate is
the number of crimes divided by the population, the population
in question should live only in the areas policed by the CCSPD.

As noted earlier (and in the footnotes to table 2), not all
police agencies have populations associated with them in the UCR
program. These "zero-population" agencies (transit police, park
police, university police, and similar agencies) may be entirely
within "primary" police jurisdictions (Groups I-VI in table 2)
that already report crime to the FBI. Were these special police
agencies to be associated in the UCR with the populations they
serve, it would be tantamount to double-counting those
populations.

When these agencies report crime data, the data are attributed
to that ORI and are included in the respective counties of the
agencies. When they are not reported (or are delayed in
reporting, or report only partially), no imputation is made of
the missing figures.

State police agencies are usually considered "zero-population"
agencies because, for the most part, they police State highways
and rural areas not covered by municipal agencies. When these
agencies report crime or arrest data, the data are also
attributed to their ORI (for example, each State police barracks
may have its own ORI) and are included in the respective
counties of the agencies. And similarly, when these agencies'
data are not reported or are delayed in reporting or are
reported only partially, no imputation is made of the missing
figures.

Updating UCR Files

As previously noted, UCR files are updated when additional or
corrected information is made available . For example, suppose a
jurisdiction was missing December's Return A, but then sent it
in after the publication cutoff date and prior to the date that
the data file is archived. In such a case the data file (but not
the published material) is updated to include the additional
information. The updated file is then sent to NACJD for
archiving, and contains different data than the printed version.

VII.  Inaccuracies Produced by the Imputation Procedures

As is the case with all estimates, those produced by the FBI's
imputation procedures are not entirely accurate. It may actually
be that the inaccuracies due to imputation do not amount to
much, at least for some uses of the crime data. But currently we
have no way of knowing whether they produce major or minor
discrepancies in the crime data, because the extent to which
this imprecision affects our estimate of the crime rate has not
been determined. Most observers believe that the effect on the
estimate of the overall crime rate in the United States would be
minimal, but that it could be quite problematic when
investigating the crime rate for a smaller unit such as a State
or county, or when looking at rural crime rates.

The potential effects of the imputation procedures are described
in this section. They include those used for primary police
agencies reporting between 3 and 11 months (called
"incomplete-reporting agencies"), for agencies that report less
than 3 months (called "nonreporting agencies"), and for police
agencies with no associated population (called "zero-population"
agencies).

Incomplete-Reporting Agencies

Included in this category are agencies that report between 3 and
11 months of data, for which full-year estimates are made by
inflating the data to cover the entire year. Even when this
imputation procedure is used, biases may exist. This would be
especially true for crimes that vary seasonally. If the months
that were not reported are historically lower in crime, then
basing the jurisdiction's annual crime rate on the remaining
months would result in an overestimate. For example, an agency
in a resort area may report its crime only during the tourist
season (when there are more officers and more crime), so the
imputed crime rate might be considerably higher than the actual
crime rate.

Nonreporting Agencies

Included in this category are agencies that report data for 2 or
fewer months, since their data are not incorporated in the
agency's estimated crime rates. As described earlier, imputation
of the crime rates of these agencies is based on other agencies
in the same population group and State. However, it may be that
the nonreporting agencies do not report specifically because
they have little crime to report. If this is the case, then this
procedure may overestimate the amount of crime. For the most
part, the nonreporting agencies are in jurisdictions with quite
small populations; however, in some counties the effect of such
imputation may increase the estimated crime rate considerably.

"Zero-Population" Agencies

Included in this category are those agencies that have statewide
jurisdiction (e.g., State police, fish and game police), or that
are entirely within jurisdictions policed by agencies that
already provide crime reports (e.g., university police), or are
cities with populations under 2,500. When these
"zero-population" agencies do not report crimes or arrests, no
imputation is made of their estimated crime. For example, crimes
and arrests made by the University of Michigan Police are
reported to the FBI for UCR purposes. However, if the University
of Michigan Police neglect to report the data or are late in
getting the data to the State, no imputation is made of the
missing data. This omission may distort the picture of crime and
arrest volume, depending on the amount of crime that is not
reported.

For another example, suppose a State police agency's crime
statistics are not produced in time for the FBI's publication
cutoff date for CIUS. Suppose further that in one (rural) county
the State police barracks is responsible for half of the crime
reports. Since no provision is made for their imputation, the
crime figures for that county would be underestimated by about
50 percent. In other words, the effect of not imputing
zero-population agencies may not have much effect in terms of
national or State-level statistics, but it can have a measurable
effect on city- or county-level statistics.

Summary

In 1958, when the FBI first began to provide national estimates
-- based on the recommendations of its Consultant Committee on
Uniform Crime Reporting (FBI, 1958) -- about half of the States
had over 90 percent coverage, and now about three-quarters have
over 90 percent coverage. Yet, despite the increases in
coverage, the missing data can distort the crime picture in
subnational estimates. When a primary police agency (i.e., in
Groups I-V in table 2) does not report, or provides partial
reports, an attempt is made to rectify the omissions by
imputation. The imputation procedures that are used, however,
may serve to overestimate the amount of crime that actually
occurred.

When reports from a zero-population agency are missing, no
matter how large the agency or how important it is in terms of
crime reporting, no estimates are made to compensate for the
missing reports. Thus, the effect of this policy may be to
underestimate crime and arrest rates for counties and rural
areas.

The FBI initiated these procedures over four decades ago, well
before the time when computing was widespread, and when
compilation of crime and arrest data from each individual agency
was a difficult and time-consuming process. Although it is still
difficult and time consuming, the current state of computer
hardware and software makes it possible to make adjustments to
the data with much greater ease. 

That this imputation procedure has continued to the present is
probably attributable to --
(a) the desire on the part of the FBI to maintain consistency in
its data series

(b) the assumption that it made little 
difference at the national level (at most 1 or 2 percent) and
would not greatly affect the general trends in the data

(c) the fact that there had been no need in the past to make any
changes -- "If it ain't broke (or if no one cares if it's
broke), don't fix it." 

But now that UCR data are being used at the jurisdictional level
to determine funding, it is clear that the crime reporting
system needs to be improved. (Footnote: Data for individual
jurisdictions are not imputed in the calculation of funds;
"however, the State-level estimates we use to determine the
amount to be distributed across the States are based on
imputations. To the extent that these imputations cause errors
that are not consistent across all States, it does affect the
dollar amounts to be distributed across the States" (S.
Lindgren, memorandum to the author, March 20, 1999). Therefore,
the agencies within States that have higher imputed estimates of
crime than they actually experienced will get larger awards than
they should, and those in States with too low estimates will get
lower awards.)  According to NACJD personnel, even before the
new Federal legislation brought the problems to the forefront,
it was quite evident that the crime rates in some counties had
substantial discrepancies due to missing data (C. Dunn, at the
1997 workshop). Even though the FBI does not impute at the
county level (and does not condone such uses), it is a fact that
the data are imputed at this level -- and that policies are
proposed based on the imputed data. It is for this reason that
the FBI should consider revising its imputation procedures. A
small percentage difference in overall crime is one thing, but 
when looking at a level like rural crime in a single State (or
more specifically, like a single jurisdiction's crime rate), the
difference might be more substantial.

VIII.  Suggested Imputation Philosophy

[This section is based on conversations with and memoranda
written by Yoshio Akiyama and James Nolan, III. While I am
deeply indebted to them for their advice and consultation, as
with the rest of this report the opinions and recommendations
are mine and should not be taken as policy of the FBI or BJS.]

The UCR imputation procedures used by the FBI have been
generally effective. The goal of the FBI has been to estimate
national and State rates, and the methods perform that task
effectively. The extension of imputation to smaller units of
analysis (where problems arise) was first done by NACJD at the
behest of BJS, to provide a more fine-grained national picture
of crime, and has since been done on a yearly basis by NACJD.
This may have led researchers to (incorrectly) assume that the
imputed data were accurate at this level. 

However, because the UCR data are the only source of crime and
arrest data at the jurisdictional level and because they have
been used to allocate Federal funds, it may well be worthwhile
to update the imputation procedures to an extent. This will
permit the FBI to provide as accurate an estimate of
jurisdictional data as possible; however, it should be
accomplished without unduly burdening the FBI with complicated
procedures.

No specific imputation procedures are recommended. Only those
who have a detailed knowledge of the data collection and
analysis processes can specify what makes sense in practice.
However, some general principles can be considered.

Suggested Imputation Philosophy

At the jurisdiction level, a longitudinal estimation procedure
(i.e., over time within the same jurisdiction) appears to be
preferable to a cross-sectional one (i.e., for the same year
across jurisdictions). Longitudinal estimation assumes that the
best indicator of a jurisdiction's current and future crime and
arrest activity is its own crime and arrest history, not the
history of "similar" jurisdictions. This means that an estimate
based on the jurisdiction's crime experience in the previous
year is better than an estimate based on other jurisdictions'
crime experience during the current year, and an estimate for a
missing month that is based on the same month last year is
better than one based on the reported months for this year. 

Current research in criminology strongly supports this approach.
Recent studies have clearly shown that "all crime is local"
(Sherman et al, 1997; Lattimore et al, 1997). That is, different
jurisdictions have different crime patterns (and different
neighborhoods in a jurisdiction have different crime patterns).
This should be recognized in terms of how gaps in criminal
justice data are filled in by imputation. 

For global estimates of missing agencies, however,
cross-sectional methods should be seriously considered. They
assume that the best indicator of a group of agencies is the
average of similar agencies for the same year, as sampling
theory indicates.

Among the points that must be considered in developing an
imputation scheme are (a) whether an agency reported data in the
previous year (to permit longitudinal estimation), (b) the
historical reporting behavior of a delinquent agency, (c)
whether non-reporting agencies constitute a random sample of all
jurisdictions, (d) whether the agencies in question have changed
their borders during the time period in question.

Different procedures need to be considered for zero-population
agencies and for regular agencies that are incomplete- and
non-reporting agencies. Some of the issues that might affect the
imputation procedures are described below.

Zero-Population Agencies

Currently no imputation is made of missing data from
zero-population agencies. This policy should be reconsidered.
Although the crime counts from these agencies may constitute a
small fraction of the whole, it may be advisable to investigate
the extent to which ignoring these agencies' data affects the
estimates of crime rates for specific sub-populations -- rural
areas, certain types of crimes, etc. 

In particular, before any specific imputation procedure can be
recommended, a study should be undertaken to determine (a) if
different types of zero-population agencies should be handled
differently and (b) whether they provide reports to other
governmental entities (cities, States) from which an estimate of
their statistics can be made. The examples given earlier, of
university or State police agencies not providing data
consistently, indicate that they may have some effect on biasing
the crime picture; it is worth determining the extent to which
this is the case.

Different strategies might be tried for different situations.
Imputing data for a non- reporting State police department might
be based on the crime rate for the State as a whole; if it has
decreased by 5 percent, then a reasonable estimate for the State
police might also be a decrease of 5 percent. Imputing data for
a university police department, on the other hand, might be
based more properly on the change in crime rate for the city in
which the university is located. [Although it might be more
accurate to base the imputation on the change in crime rate for
the neighborhood of the university, such detail is beyond the
capability or needs of the UCR program.

Incomplete and Non-Reporting Agencies

As Akiyama and Nolan (1999b) point out in their memorandum, "UCR
Data Imputation: Longitudinal vs Cross-Sectional Approaches,"
the bulk of the delinquent agencies report fewer than 4 months
(i.e., there are relatively few incomplete-reporting agencies as
compared to non-reporting agencies). Imputing the missing data
for these agencies might best be accomplished by imputing each
missing month separately. This has the effect of simplifying the
imputation process by putting all agencies with missing data
into the same category (and eliminating the arbitrary 3-month
cut-point), but it also would permit seasonality to be taken
into account by providing monthly estimates.

There are two different cases to consider for primary police
agencies that submit no reports. First, there are agencies that
virtually never provide reports of crimes. The voluntary nature
of the UCR program precludes the FBI from taking any measures to
require reporting from non-reporting agencies. The fact that
they do not report to the FBI, however, does not mean that they
provide no reports of their activity at all; most agencies must
do so, to some governmental body. A sample of these reports
could be analyzed to determine the extent to which the current
imputation procedure (i.e., using the same crime rate as in
"similar" jurisdictions) reflects their true crime rates.

For example, if a particular municipal police department does
not provide crime data to the FBI or State criminal justice
agency, it still may prepare an annual report to the
municipality: a police department must normally justify its
annual budget with an accounting of its activity. To determine
the extent to which crime is underreported, it might be
worthwhile to get in contact with a random sample of
non-reporting agencies and obtain their annual reports. In this
way an estimate could be made of the extent of crime and arrest
activity in these and similar non-reporting jurisdictions.

Second, there are agencies that normally do prepare reports but
are unable to do so for a particular year. In such a case, an
imputation procedure that is partly longitudinal and partly
cross-sectional may be useful. That is, one can use the
year-to-year change in crime rate for like jurisdictions (in the
same state and same population group), and apply this
year-to-year change to the agency's prior year's data. For
example, if Agency X did not produce reports for 1997, and the
year-to-year increase in crime for "like" agencies (i.e., in the
same population group and State) was 3%, this increase could be
imputed to last year's data for Agency X.

IX.  Supplementary Homicide Reports

The FBI initiated the Supplementary Homicide Reports in the
1960s (Riedel, 1990), and NACJD has archived the data from 1976
to the present. All police departments that report homicides to
the FBI generally also submit SHR forms. While the monthly
reports to the UCR program consist for the most part of summary
data (e.g., the number of homicides occurring in January 1998),
the data from the SHR are considerably more detailed (Figure 12.
Replica of Supplementary Homicide Report Form, pages 1 and 2).

In some ways, the SHR can be considered a precursor to NIBRS,
since it provides details about the incident's occurrence (the
jurisdiction, month, and year of occurrence); the apparent
circumstances under which it occurred (number of victims and
offenders, whether it resulted from a robbery, domestic
violence, argument, or other circumstance); age, race, and sex
information about the victims  and offenders, if known; and the
relationship between victims and offenders, if known.

As with any new data series, SHR data for the first few years
had some limitations, but the series appears to be fairly
accurate in terms of the number of homicides reported. The basis
for this assessment is the fact that another data series on
homicide exists, mortality data based on death certificates and
collected by health departments since around the turn of the
century. Data on Vital Statistics are collected and maintained
by the National Center for Health Statistics (NCHS), US
Department of Health and Human Services. Although the SHR was 
apparently undercounting homicides by about 14 percent during
the 1960's and 1970's (Riedel, 1990), more recently the two
series have been within a few percentage points of each other
(Riedel, 1999).

Uses of the SHR

The SHR has been useful in developing policy recommendations
related to homicide. Its nationwide collection, and the fact
that not just the number of homicides but the characteristics of
the victims and offenders are included, permits researchers to
uncover patterns of significant importance: for example, that
the decreasing homicide rates for some groups tended to mask the
increase in homicide rates for 14- to 17-year-old males (Fox,
1997) and that infanticide is a significant problem in the
United States (Maltz, 1998). In addition, many of the articles
in Homicide Studies, a journal published by the Homicide
Research Working Group, are based on the SHR data, and many have
policy consequences. 

These studies may not lead to solving particular homicides,
which has long been the primary focus of police attention to
homicide; however, insofar as they point out patterns and risk
factors, they contribute greatly to the public safety, the
primary mission of the police. The accessibility of the SHR data
is increased by the inclusion of files that contain SPSS and SAS
data definition and programming statements, so that the SHR can
be analyzed using these two statistical analysis packages or
others that can read these formats.

The SHR has been used not only to study homicide patterns but to
study patterns of violent crime in general. The rationale for
using homicide rates as a proxy for violent crime rates is
because they are highly correlated (e.g., Blumstein, 1974);
however, since there is very little unreported homicide in
comparison to other crimes, using the homicide rate all but
eliminates the problem of unreported crime. But this is
misleading, and the SHR has been misinterpreted by researchers
and journalists in their search for patterns in homicide data.
Homicide is not so much a crime in itself as it is the fatal
outcome of different crimes or "homicide syndromes," and
analysis of homicide as a single entity can produce misleading
results. The easy accessibility of the SHR data, then, has
unfavorable as well as beneficial consequences. 

A better way to look for patterns in homicide data is to
consider the various circumstances under which homicides occur,
that is, to disaggregate infanticides from felony homicides from
spousal murders, and to consider the homicide rate from within
the context of the underlying crime. From this type of analysis
one can investigate the risk of death due to child abuse, armed
robbery, or domestic violence (Maltz, 1976, 1998; Maxfield,
1989; Block and Block, 1992). 

This, then is one of the great benefits of the SHR: because it
provides detailed information about each homicide, it can be
used to great advantage in exploring offense patterns and public
policies. For example, if the risk of death due to child abuse
is much higher in one jurisdiction than another, it may be that
the true rates are the same but that the lower-rate jurisdiction
has better child abuse reporting practices.

Incomplete Provision of SHR Data by Police Departments

The SHR has a number of shortcomings, in particular with respect
to incomplete data. There are three ways in which SHR data may
be incomplete. First, not all homicides reported on the UCR are
reported on the SHR form. Second, some agencies do not include
all the information about offender characteristics or
motivations that is available to them. Third, even when the
information is complete it may be wrong, because offender-victim
relationship is given instead of victim-offender relationship or
because the same relationship is given for all victims and/or
offenders in an incident with more than one victim and/or
offender.

The FBI tries to ensure that all homicides reported on the UCR
are reported in the SHR as well, by specifically requesting this
information from jurisdictions for each UCR-reported homicide.
The FBI doesn't always obtain it, but the SHR/UCR ratio has run
between 86 percent and 96 percent between 1980 and 1994 (Snyder,
1996: 10-11).

Incomplete reporting of SHR data is a greater problem. For
example, the data element "Circumstance" reflects the nature of
the homicide as far as it can be determined. See table 3. Yet,
the number of homicides with unknown circumstances varies
considerably from agency to agency, indicating that departmental
policy more than knowledge of the circumstances governs the
information collected by the SHR.

----------------------------------------------------------------

Table 3.  Circumstances under Which Homicide Occurred

Circumstances coded in SHR records
    2 Rape                                        32 Abortion                              60 Other
    3 Robbery                                     40 Lover's triangle                      50 Victim shot in hunting accident
    5 Burglary                                    41 Child killed by babysitter            51 Gun-cleaning death not self-inflicted 
    6 Larceny                                     42 Brawl due to influence of alcohol   
    7 Motor vehicle theft                         43 Brawl due to influence of narcotics   52 Children playing with guns
    9 Arson                                       44 Argument over money or property       53 Other negligent handling of gun
   10 Prostitution and  commercialized vice       45 Other arguments                       59 All other negligent manslaughter except traffic death
   17 Other sex offense                           46 Gangland killing                      70 Suspected felony-type
   18 Narcotics and drug laws                     47 Juvenile gang killing                 80 Felon killed by police
   19 Gambling                                    48 Institutional killing                 81 Felon killed by private citizen
   26 Other felony-type not specified             49 Sniper attack

 Source:  Fox, 1997

----------------------------------------------------------------

There may be a number of reasons for agencies not providing
complete information. First, the information may not be readily
available initially, when the officer first completes the
agency's homicide report -- and it may be this initial form that
is used to complete the SHR form. There are strong indications
that the Washington, DC, Metropolitan Police Department may fill
the form out based on this preliminary report -- for example, in
1994 the offenders in 96 percent of homicides were listed as of
unknown age (this was used as a proxy for "offender unknown").
Although this is the most extreme example of inadequate data
collection efforts, figure 13 (based on data from Snyder, 1996)
shows that Washington is far from alone. (Figure 13.  Percent 
of Offenders with Known Ages (as Proxy for Known Offenders)
in SHR Data in Counties with More than 100 Homicides in 1994)

Second, some departments may downplay the utility of such
information and give it low priority, since it is a voluntary
collection system. Thus, the goal of obtaining complete
information for crime prevention purposes too often takes a back
seat to reducing the paperwork burden for a police department.

Third, one city (Boston) does not provide information about the
offender or his/her possible motivation "in order to prevent
creating documentation that would be discoverable and of
potential use to the defense at trial" (Braga, Piehl and
Kennedy, 1997). (Footnote:  I suspect that this policy was
instituted after the information was used successfully in
acquitting a defendant; I also suspect that a less drastic step
could have been taken. In any event, this problem may be mooted
by NIBRS, which allows a window of 2 years in which to update an
incident with additional information.)

Fourth, there is a great deal of variability from city to city
in the diligence with which the SHR information is provided.
During the 1997 workshop it was mentioned that one city 
(Washington, DC) rarely records drug involvement in homicides,
while in another city (Detroit) almost every homicide is
recorded as drug-involved -- when the actual truth for both
cities is somewhere in between.

Moreover, incompleteness in SHR reporting also reflects the
coding procedures established by the FBI to collect the data.
The codebook (Fox, 1996) for the SHR data gives an example:
"[T]he structure of the data collection forms prescribes that
the relationship of the offender to the first victim (often
chosen arbitrarily) be coded for this offender. Thus, for
example, in 1977 a Redondo Beach, California, woman killed her
husband and three step-children by burning down the family home.
Appropriately in this case, the weapon was coded a 'fire' for
all four victims, but the relationship of victim to offender was
coded as 'step-daughter' for all victims -- two 8-year-old white
females, a 7-year-old white male, and a 40-year-old white male."
That is, the FBI strips the relationship data that may be
provided to the FBI and uses only one relationship to
characterize the entire incident.

This problem does not affect most homicides, however, since the
great majority of homicides consist of one victim and one
offender.

Updating SHR Files

SHR files are updated when additional information is provided by
police departments. It should be understood that the SHR file is
updated, but individual records are not updated. For example, an
accidental death may be reclassified as a homicide, and
consequently is sent in to the FBI for inclusion in the SHR
file; it is in this way that the SHR file is updated.

However, if police submit an SHR record to the FBI with a
homicide whose offender was classified as unknown, and
subsequently learn of the identity of the offender (the
Unabomber or Theodore Kasczinski case is exemplary), the records
of those homicides are not changed. The FBI cannot revisit old
records because no unique index code is included in the SHR file
that would permit them to identify specific homicides. 

This problem will diminish when NIBRS is implemented, since each
incident will have a unique identifier, permitting true updating
for 2 years after the incident. Insofar as police departments
adopt NIBRS and adhere to its requirements, it will be possible
to truly update the incident files as more information about
incidents develops.

Availability of SHR Data Sets

The FBI makes the data files available to BJS, and the files are
then restructured, reformatted, cleaned, and given wider
accessibility through the NACJD website (at
http://icpsr.umich.edu/nacjd/ucr.html). Each record can contain
information on up to 11 victims and offenders; since most
homicides are one-victim/one-offender homicides, this makes each
year's file much larger than it need be and consequently more
difficult to analyze. In addition, there is a separate file for
each year, so these datasets need to be combined to perform any
multi-year analyses.

For this reason, BJS and the National Institute of Justice
funded an effort to make the SHR data more accessible, resulting
in a multi-year SHR data set, 1976-94 (Fox, 1996). Included in
the restructured file are weights that take into account missing
offender data according to their age, race, and sex, at both the
State and Federal levels. Manslaughters by negligence and
justifiable homicides are not included in the data set.

There are problems in dealing with multiple victims and
offenders in a single data set, in a way that keeps the
victim-offender relationships intact without having to carry
along (mostly empty) space for 11 victims and offenders. The way
it is handled in the 1976-94 dataset is to create a different
record for each victim-offender pair. That is, an incident with
four victims and two offenders would have eight records, each
record corresponding to a different victim-offender pair.

Multi-year SHR data sets from 1976-97 will soon be available at
NACJD, which handle multiple victims and offenders in a
different manner. Two separate data sets are generated from the
FBI data files, a victim data set and an offender data set. The
victim data set contains a separate record for each victim; if a
single homicide incident includes four victims and two
offenders, four records are created -- one for each victim --
and the offender data included on those four records are the
characteristics of the first offender. To describe the same
incident, the offender data set would include two records, one
for each offender, and the victim data included on those two
records are the characteristics of the first victim.

SHR Imputation

The first point to be made about imputation of the SHR is that
the FBI does not impute SHR data. However, the victim and
offender data sets for the combined 1976-97 SHR data, to be
provided at NACJD, do include imputation procedures. The
imputation procedures incorporated in these data sets are not
the only ones that have been used for SHR data, but because they
are used on the most complete SHR data sets (1976-97) -- and the
ones most likely to be used in the future -- their
characteristics are described below.

Two different kinds of imputation are used in the (to-be)
archived multi-year SHR data set. The first one is used to
reconcile the count of SHR homicide victims with the count in
CIUS. The second imputation procedure is used to estimate the
characteristics of offenders in incidents in which there is no
information about the offender. In both types of imputation
weights are assigned to each case. The best way to explain these
imputation procedures and their use is to discuss each type of
weight given in the SHR file.

Weighting the Victim File

The number of records in the victim file is the count of SHR
homicides. As noted earlier, this number is often not the same
as the count of UCR homicides, both nationally and at the State
level. Two of the weights included in the victim file are used
to reconcile these two numbers. 

Weight wtus. This weight is the same for all cases for a given
year. The weight represents the ratio of the number of homicides
reported in CIUS to the number reported in the SHR. Thus, since
the UCR reported 18,780 homicides and the SHR reported 16,605
homicides for 1976, the weighting factor wtus is 1.13
(18,870/16,605) for 1976. It is used in the following way:
suppose one wants to estimate the number of homicide victims
under 6 years of age. The UCR does not detail this information,
but we can estimate the number by extrapolating from the known
SHR cases to the UCR cases. In 1976 the SHR recorded 519 such
cases, so the 1976 estimate would be (1.13 x 519 =) 587 victims
under age 6. This would permit us to compare 1976 data with data
from another year, in which a different weighting factor is
used. For example, in 1977, 548 such cases were recorded in the
SHR, representing a 6% increase over the 519 in 1976. However,
wtus for 1977 was 1.06 (19,120 UCR homicides versus 18,032 SHR
homicides), so we estimate that there actually were 581 such
victims; this represents a 1% decrease from the 1976 estimate of
586. (Footnote:  These examples are given for illustrative
purposes only. A better way of estimating year-to-year change is
to obtain the rate, by dividing the estimates by the estimated
population under 6 in each year.)

-------------------------------------------------------------

Table 4.  Victim Imputation in the SHR
                                                          Estimated
         UCR          SHR        UCR/        SHR age      UCR age
Year     total        total      SHR ratio   5 or under   5 or under 

1976      18780        16605        1.13          519        587
1977      19120        18032        1.06          548        581

Victims under age 5 --
SHR percent increase                    5.6%
Estimated UCR percent increase         -1.0%


-------------------------------------------------------------

Weight wtst. This weight is the same for all cases in a State.
The weight represents the ratio of the number of homicides
reported by the State in CIUS to the number reported In the SHR.
Thus, the weighting factor wtst of 1.17 for Alabama's 453
SHR-recorded homicides for  1976 indicates that Alabama
experienced (1.17 x 453 = ) 530 homicides in 1976. For Alaska,
however, wtst was 1.0 for all of the 43 homicides, indicating
that the UCR and SHR reported the same number of homicides. In
1977 the values of wtst for Alabama and Alaska were 1.06 and
0.96, respectively, so the SHR counts of 487 and 46 indicate
that these States had (1.06 x 487 =) 516 and (0.96 x 46 =) 44
UCR-reported homicides, respectively. (Footnote: There may be
more SHR homicides than UCR classifications due to crime
reclassification across years. For example, perhaps two shooting
victims in Alaska in incidents classified as aggravated assaults
in 1976 died in 1977.)

Weighting the Offender File

There are three weights in the offender file. Just as the
weights in the victim file are meant to provide a better
estimate of the number of homicide victims, the weights in the
offender file are meant to provide a better estimate of the
number of homicide offenders. Whereas the victim file weights
are used to fill in for missing records, the offender file
weights are used to fill in for missing data within records,
that is, for cases where the identity and characteristics of the
offender are unknown.

Weight wtimp. This weight imputes, for the Nation at large, the
number of offenders by age/race/sex category. Suppose that there
are 500 victims in a specific age/sex/race category, and 400 of
them are killed by known offenders. Then wtimp would equal 1.25
because the unknown offenders are presumed to have the same
age/sex/race characteristics as the known offenders. For
example, suppose that 25 percent of them (or 100) are killed by
white males ages 15-24. Then we would estimate that 25 of the
victims of unknown offenders are also killed by white males ages
15-24.

Weights wtimpus and wtimpst. In order to take into account the
cases that are reported to the UCR but not included in the SHR,
we can estimate the total number of white male offenders ages
15-24 in the United States, by multiplying wtimp by the
aforementioned wtus. If the latter is 1.13, as in the earlier
example, the value of the weight wtimpus would be (1.13 * 1.25
=) 1.41. Similarly, if we wanted to estimate the number of such
offenders in Alabama, we would weight each homicide committed by
a 15- to 24-year-old white male by (1.17*1.25 =) 1.46, the value
of wtimpst for Alabama.

Problems with this SHR Imputation Procedure

Since there are two different weighting schemes, for the victim
and offender data sets respectively, the inaccuracies that arise
from their application need to be considered separately.


Victim File. The weights wtus and wtst are applied to reconcile
the number of homicides recorded in the UCR and SHR, at the
national or State levels, respectively. When there is a
discrepancy between the two, the UCR is usually (but not always)
the larger of the two, so these weights are usually larger than
1. There may be a number of reasons for the discrepancy. 

*There may be clerical errors in an agency's submission of
either Return A or the SHR form. For example, some homicides
that were reported to the UCR may have "slipped through the
cracks" when an agency filled out the SHR form, or perhaps
because the agency did not fill the form out at all. If the
former is true, then the agency is likely to be a larger police
department with many cases to report; if the latter is true,
then the agency is likely to be a rural department that does not
report homicides very often.

*Part of the discrepancy may be due to date slippage, as when a
person is injured in one calendar year and succumbs to these
injuries in another year. In such cases, the homicide may not be
reported on the SHR form, especially if agency practice is to
fill it out at the time of the offense. In any case, assuming
that the missing homicides generally have the same
characteristics as the reported ones (which this weighting
scheme implies) may be in error. The error attributable to this,
however, is likely to be small, since the concordance between
the two data sets is usually fairly high (Chilton and Jarvis,
1999). 

Offender File. The weights wtimp, wtimpus, and wtimpst estimate
the number of offenders in cases where the offenders are
unknown. However, there is a problem in assuming that the best
means of estimating offender characteristics is to predicate the
estimation on the age, race, and sex of the victims. For
example, suppose that most 45-year-old women are killed by their
mates, i.e., by 40- to 49-year-old white males. However, if a
45-year-old female clerk at a convenience store is killed by an
unknown assailant, not by her husband, there should be some way
of including this in the imputation equation. In other words,
the circumstance of the killing (which is included in the SHR
record) is probably a better indicator of offender
characteristics than a rule that does not include this
information. Williams and Flewelling (1987) used this imputation
method; however, Langford, Isaac, and Kabat (1998) describe some
limitations to using circumstance for imputation purposes.

An indication of the problem with this type of imputation is
made clear when investigating the distribution of unknown
homicides within a State. Between 1976 and 1996 Richmond
accounted for 18 percent of Virginia's homicides but 41 percent
of its homicides by unknown assailants; Atlanta accounted for 29
percent of Georgia's homicides but 49 percent of its unknowns;
and Indianapolis accounted for 21 percent of Indiana's homicides
and 35 percent of its unknowns. (Footnote:  These figures are
based on an analysis by the author.) So making the assumption
that the unknown assailants have the same characteristics as the
known offenders is questionable.

More to the point, the very fact that the assailant is unknown
often means that the type of homicide is quite different than
those in which the assailant is known. Studies in Chicago and
Boston confirm this assertion.   Block (1998) compared 1993-94
Chicago homicides found in the SHR data with those in the
Chicago Homicide Dataset (CHD), perhaps the most complete
homicide dataset in the country. Her data show that about 10
percent of the CHD incidents had unknown offenders in the SHR
data, but that it varied from around 30 percent (for sexual
assault) to under 4 percent (when firearms were used).

Braga et al. (1997) compared Boston SHR data for 1990-94, for
victims 21 or under (N = 155), with data they had collected for
this population in conjunction with the Boston Gun Project (BGP
-- see Kennedy, Piehl, and Braga, 1996), a program to reduce
youth gun violence. Since the BGP data were collected well after
the events, they were much more detailed than the SHR data,
especially considering the concerns of the Boston homicide
detectives (see page 34 above). Thus, it was not surprising to
find that, while the SHR data listed 65 percent as with unknown
circumstances (and 79 percent with unknown victim-offender
relationship), the BGP data had only 30 percent -- less than
half as many -- with unknown circumstances and 40 percent --
again about half as many -- with unknown victim-offender
relationship.

Although neither city is representative of the Nation as a
whole, what these studies point out is that the unknown
offenders are not necessarily representative of the knowns. This
may be due to the fact that homicide is not a specific crime
like robbery. Rather, as mentioned earlier, it is the fatal
outcome of a lot of different crimes: intimate partner violence,
armed robbery, child abuse, etc. The imputation rule used in the
SHR offender file implicitly assumes that they are all of the
same general nature, which is not the case; moreover, the
information included in the SHR file is specifically included so
that different types of homicides can be distinguished from each
other, and an imputation rule for the SHR should take this
information into account.

Suggested Alternative SHR Imputation Procedure

A homicide's circumstance (see table 3), rather than the
victim's characteristics, would seem to be a more appropriate
indicator of the characteristics of the offender. No matter what
the age, race, and sex of the victim, if the homicide arose
during the course of a robbery the characteristics of the
offender would probably resemble other robbery- homicide
offenders more than they would any other class of offender. To
some extent, this suggestion takes the SHR reporting practices
of some agencies into account: even though Washington, DC,
reported that 94 percent of the 473 homicides had unknown
offenders, the circumstances were listed as unknown in only 50
percent of the cases.

However, this suggestion should be considered carefully.
Maxfield (1989) noted the lack of consistency among agencies in
coding homicide circumstance. Because of its importance,
imputation of homicide offenders is a topic that should not be
undertaken lightly. In the aggregate it may not amount to much,
but as we begin to go beyond looking only at aggregate rates and
investigating small subgroups it takes on more importance.

Newspapers report not just on State-by-State homicide rates, but
on offense rates, by State, by age group, by weapon, and the
rates inferred by imputation may be seriously in error, as
suggested by the Boston and Chicago studies. Granted, the
"unknown" offenders in Boston and Chicago do not represent
unknown offenders in the rest of the United States; these cities
were chosen because of the availability of good data; in both
cities homicide detectives and researchers have formed a strong
working relationship. Rather, as noted earlier, despite their
lack of representativeness, these studies do provide a benchmark
against which to test different imputation procedures.  Only
through research like this can we find the extent to which our
view of crime is distorted by incomplete and inaccurate data.

X.  Conclusions and Recommendations

Reporting Practices

The primary focus of this report has been to understand, and
make suggestions for improving, how the FBI collects and
analyzes crime and arrest data that are subsequently published
in CIUS. Along the way, however, one could not help but notice
the recent decline in the quality and quantity of the data 
submitted by State and local agencies to the FBI. This trend 
compromises the quality of information we have about the nature 
and extent of crime in the United States. I hope that this report 
serves as an impetus to improve the completeness and accuracy 
of crime and arrest data.

One State, Illinois, sends data to the UCR program that do not
adhere to UCR reporting standards with regard to the hierarchy
rule. This occurred for the best of reasons:  in 1992 Illinois
embarked on an ambitious effort to implement NIBRS statewide,
but the complexities were too great, and the effort was
suspended in 1994. Beginning with 1993 data and continuing to
the present, Illinois has submitted only summary UCR data, but
without applying the hierarchy rule, so that incidents with
multiple offenses may be counted more than once. 

In addition, since 1984 Illinois statutes have defined forcible
sexual assault without reference to gender. This is not
compatible with UCR standards. Because of this incompatibility,
the UCR does not include Illinois data on rape. Illinois, one of
the most populous States (and my home State), should be
encouraged to change the reporting practices that have kept it 
from contributing to the UCR in the recent past.

Although this issue was not addressed in the report, and is
somewhat beyond its scope, the absence of crime and arrest data
from Federal agencies distorts the picture we have of crime in
the United States. The recent report detailing the high rate of
victimization of American Indians (Greenfeld and Smith, 1999)
serves to underscore this deficiency in our crime statistics:
insofar as UCR data are used to allocate resources, this would
affect the extent to which enforcement resources for American
Indians are provided.

Publishing and Archiving

It is a tribute to the diligence and dedication of the FBI's
Program Support Section staff that there are so few errors in a
report so filled with numerical data as CIUS; one cannot just
run a spell-checking program to see if errors have been made in
the manual transcription of numbers from one medium to another.
However, it is a waste of personnel time to use a process that
requires so much staff time to proofread numerical data. The
tables were initially produced by a computer that could, with
relatively little additional funding and effort (and that only
for the first year) be set up to produce camera-ready output. 

This added technology would not only remove an unneeded burden
from the PSS staff, but it has the promise of reducing the time
taken to produce CIUS by a matter of some weeks, perhaps even
months. This extra time could be put to use at either end of the
report production process: ORIs and States could be given more
time to transmit their data to the FBI, which may become
increasingly important as more agencies convert to NIBRS; and/or
the report could be published earlier in the year. It should be
noted that two other annual reports, Hate Crime and Law
Enforcement Officers Killed and Assaulted, are published
directly by the PSS staff; the FBI should consider direct
publication for CIUS as well.

If it is possible for the FBI to do so, BJS should request the
FBI to "freeze" the version of the raw data set that is used to
produce CIUS, and send it to NACJD for archiving, as well as the
final version of that data file. This will permit researchers to
perform data analyses that are consistent with CIUS and/or use
the most current data to provide more complete analyses.

Imputation

As this report details, imputation of crime and arrest data has
been based on ad hoc procedures that were appropriate at the
time they were made and for the uses to which they were
originally put. Now that UCR data are being used for different
purposes, new methods of imputing data need to be considered.
This report describes some reasonable candidates, but these
should not be applied without setting up an evaluation program
to determine whether they actually provide improved estimates.
This is true for all data sets in which imputation is being
used: crime, arrest, and SHR data. One area where some
experimentation might be helpful is to determine where the
cutoff threshold should be before an ORI's data are sufficient
to be included without full imputation, the 3 months used by FBI
or the 6 months previously used by NACJD. Among the possible
ways of performing these experiments are:

*Analyze nonreporting agencies' submissions to their municipal
governments to see to what extent the imputation procedure is
valid.

*Develop different categories of nonreporting ORIs and randomly
select a number of full reporters from each category; apply an
imputation method and see how closely the imputed data comes to
the actual data.

*Do the same with incomplete-reporting ORIs.

The data from so-called "zero-population" agencies should also
be imputed. The exact means of doing so should probably depend
in part on the nature of the jurisdiction: the procedure for
imputing a State police agency's data should not be the same as
for a university police department.

NIBRS

NIBRS represents a major increase in the amount of data to be
collected by local agencies and forwarded to the FBI. A number
of States have implemented NIBRS successfully in essentially all
their reporting agencies; however, others have had software and
other problems that have not only prevented NIBRS from being
fully implemented, but they have been unable to send even the
summary UCR data to the FBI.

Some police administrators have complained about NIBRS and the
level of detail it entails. However, its implementation is in
part a recognition that if the focus of policing is to be more
than just "catching the bad guys," and to deal with public
safety more generally, then the police need to be concerned with
analyzing crime for patterns that go beyond the modus operandi
of individual offenders. Such a pattern may suggest, for
example, that a new domestic violence or after-school program
might reduce certain types of offenses. Implementing NIBRS will
give us the ability to compare the effectiveness of such
programs in different jurisdictions with different populations.
A recent FBI report, The Structure of Family Violence (found on
the FBI's website at www.fbi.gov/ucr.htm/famvio21.pdf), gives
some indication of the way NIBRS data can be used for this
purpose.

As I mentioned in the beginning of this report, my goal (and the
goal of those who attended the workshop) has been to suggest
some ideas for consideration in revising the FBI's Uniform Crime
Reporting Program. While not all of the suggestions may be
feasible to implement at this time, I hope that this report lays
the groundwork for improving our knowledge of the nature and
extent of crime in the United States, one of the more pressing
social problems confronting the Nation.

Appendix A:  
Persons Attending the Workshop on UCR Imputation Procedures 

US Department of Justice Participants:

Bureau of Justice Assistance
Richard Ward

Bureau of Justice Statistics
Jan Chaiken
Lawrence Greenfeld
Charles Kindermann
Patrick Langan
Sue Lindgren
Michael Rand
Bruce Taylor
Marianne Zawitz

Criminal Division
Julie Samuels
Steven Shandy

Federal Bureau of Investigation
Yoshio Akiyama
Ben Brewer
Gilford Gee
Antonio Hwang
John Jarvis
Dawn Kording
Victoria Major
James Nolan
Sharon Propheter

National Institute of Justice
Jordan Leiter

Office of Juvenile Justice and Delinquency Prevention
Barbara Allen-Hagen

Other Participants:

American University
James Lynch

Inter-University Consortium for Political and Social Research
Nora Arato
Christopher Dunn
Christopher Lysholm
Kaye Marz

Massachusetts State Police
Daniel Bibel

National Center for Juvenile Justice
Howard Snyder

Northeastern University
James Fox

Research Triangle Institute
Robert Flewelling

Rutgers University

Michael Maxfield

Project SEARCH
David Roberts

University of Illinois at Chicago
Michael Maltz

University of Massachusetts, Amherst
Roland Chilton


Appendix B:  State On-Line Publication of Crime Data
                                     Data reported on website
                                                             Law enforcement
                                     Crime                   Officers killed/
State           UCR      Crime trends Hate/bias Arrests      assaulted       Personnel  Website URL

Alabama               97       93-97                x               x            x      http://agencies.state.al.us/ acjis/pages/alacrime.htm
Arkansas           95-96       97-98                x               x            x      http://www.acic.org/ statistics.htm
California         96-97                  x         x               x            x      http://caag.state.ca.us/cjsc/ pubsol.htm
Colorado           80-96                                                                http://www.state.co.us/gov/ dir/cdps/dci/ors/stats.htm
Connecticut        96-97                                                                http://www.state.ct.us/dps/ CT-UCR.htm
Florida               97       96-97                x                                   http://www.fdle.state.fl.us/ Crime_Statistics
Georgia               98   85, 95-96                x                                   http://www.ganet.org/gbi/ stcrime.html
Hawaii             83-97       92-97                x               x            x      http://www.cpja.ag.state.hi.us/rs/
Illinois           94-97       93-97      x         x               x            x      http://www.state.il.us/isp/ cii00001.htm
Iowa                  96                            x               x            x      http://www.state.ia.us/ government/dps/crime/stats/
Kentucky           95-96                            x               x            x      http://www.state.ky.us/ agencies/ksp/crime.htm
Massachusetts         95                                                                http://www.magnet.state.ma.us/ msp/crimedat.htm#Taunton
Michigan              97       88-97      x         x               x            x      http://www.state.mi.us/msp/ crd/ucr/contents.htm
Minnesota          95-96                                                                http://www.dps.state.mn.us/bca
Nebraska           93-95                                                                http://www.info.ded.state.ne.us/ stathand/contents.htm
New Hampshire         97                                                                http://www.state.nh.us/nhsp/
New Jersey         93-97       88-97      x         x               x                   http://www.state.nj.us/lps/ njsp/stats.html
New York                       90-96                x                                   http://criminaljustice.state. ny.us/crimnet/pubs.htm
North Carolina     93-98       78-97      x         x                            x      http://sbi.jus.state.nc.us/crimstat/ nccrime.htm
Ohio               80-96       80-96                                                    http://www.ocjs.state.oh.us/
Pennsylvania       76-96                            x                                   http://www.state.pa.us/PA_Exec/PCCD/stats/factsheets/statspag
Utah                  96                  x         x               x                   http://www.ps.ex.state.ut.us/
Vermont               97                            x                            x      http://www.dps.state.vt.us/ cjs/crimestats.htm
Virginia              97       96-97                x               x            x      http://www.state.va.us/vsp/ zucr1.html


Appendix D: Extent of UCR Data Coverage, Alabama-Wyoming, 1958-97
(Data for these 50 graphs are available in a spreadsheet.)


Appendix E:  
Missing Data in UCR Files Used for the 1996 LLEBG Formula
Calculations 

Sue Lindgren
Bureau of Justice Statistics 

January 23, 1997: 
Modified for the April 24-25, 1997
Workshop on UCR Imputation Procedures

Background

The Local Law Enforcement Block Grant (LLEBG) program allocates
Federal funds to State areas and local governments based on a
3-year average of Part I violent crimes as reported to the FBI.
The Bureau of Justice Assistance (BJA) administers the program,
using award amounts computed by the Bureau of Justice Statistics
(BJS). The legislation recognizes that some jurisdictions do not
report to the FBI and prescribes that we estimate data for such
"nonreporting agencies." Because of the press of time and our
unfamiliarity with the Uniform Crime Reporting (UCR) data base
to be used in the formula, it was not possible to attempt any
such estimates for the 1996 awards (computed late spring, early
summer 1996 after the formula was enacted in April 1996).
Rather, we assumed that there would not be a significant number
of such "nonreporting" agencies eligible for an award and
decided that we would handle such situations on a case-by-case
basis. A few such cases were brought to our attention and we
researched and resolved each on an individual basis.

Once the formula was run and the grants awarded, and State and
local government inquiries about award amounts researched and
resolved, we began the following analysis to inform BJS and BJA
of the extent and nature of missing data in the UCR data set.

Extent of Missing Data, by State

As the first part of this analysis, we compared the violent
crime State-wide totals we computed by summing all agency
records within each State on the UCR files to the estimated
violent crime totals published by the FBI in table 5 of Crime in
the United States for the 1992-94 period used in the 1996
formula awards and for the 1995 data. (Footnote: Earlier years
data were used in the fiscal 1996 formula data set for Kansas,
Illinois, and Montana because of their conversion to NIBRS.  For
those States, the fiscal 1996 analysis examined data
availability for those earlier years.) As seen in the attached
tables 1-3, there is considerable variability across States. For
the 1992-94 period, three States had less than 70% complete
data, and 11 had less than 90%. By 1995, 28 States had even
lower coverage than in the 1992-94 period, although the 1995
data for at least 4 States are low because of conversion to
NIBRS. (We used data prior to 1994 for three of these States in
the 1996 awards). 

Characteristics of Agencies with Less Than 36 Months of Violent
Crime Data

The second part of this analysis examined what kinds of agencies
are included in the UCR files, but, according to the FBI,
reported zero months for the period used for the formula. The
FBI files contain a variable giving the number of months
reporting for each year; using this variable for the three
years, we were able to construct a "Number of months reporting"
variable with a range of 0 to 36 months. Of 18,413 agencies in
the FBI's UCR files for 1992-94, 3,516 (19.1%) did not report
for any month during the 36-month period used in the formula and
another 3,197 (17.4%) reported between 1 and 35 months. (See
attached table 4.) Agencies reporting 0 months and agencies
reporting 1 to 35 months are examined separately below.

Agencies with 0 Months of Data

We looked first at the 3,516 agencies reporting for no months
during the 36 month period used in the formula. Based on our
analysis, we discovered that many of the agencies on the files
are now "retired" or "inoperative." This is less important for
imputation purposes than for formula purposes, where we need to
be sure we are not estimating data for agencies no longer in
existence. Despite the research described below, we are not in a
position to say with certainty which are inactive and which are
nonreporting, although the research gives some idea of the
relative sizes of each group. 

As seen in table 5, almost half, 1,652, of those reporting no
months were covered by another reporting agency by the end of
1994, making them ineligible for an LLEBG grant by our
interpretation of the legislation, and a third, or 1,221, are
"zero population" agencies, meaning they are almost certainly
State agencies or special police agencies (such as transit
police) that probably are not eligible for a formula award. (194
of those were covered by another reporting agency as well as
being zero population.) 

Excluding all of these presumably ineligible agencies, we are
left with 866 agencies coded as reporting no months, most of
which are small jurisdictions. As seen in table 6, nearly 70%
are cities with less than 10,000 population. However, when
looking at the actual jurisdictions, there are 2 counties and 1
city with over 100,000 population, 3 cities with between 50,000
and 100,000, and 199 cities and counties with over 10,000
population. For formula allocation purposes, it is important to
keep in mind that in less populous States, cities with 10,000
population receive awards; thus, there is more of a potential
impact here than we originally thought.

Jurisdictions with between 1 
and 35 Months of Data

Table 4 shows that 17.4% of the agencies in the UCR files
reported for at least 1 month, but less than 36. This represents
3,197 agencies. As seen in table 7, 17% of these, or 554, are
zero population agencies, again meaning they are probably not
eligible jurisdictions, including 5 that were covered by another
reporting agency by the end of 1994. An additional 50 with
populations are covered by another reporting agency, again
indicating ineligibility. Of the remaining 2,593 that might be
eligible for an LLEBG grant, 10%, or 262 jurisdictions, already
exceeded their State threshold for an award; their awards would
increase if they reported for more of the period. (Footnote:
This includes three State police barracks that were not
"zeropop;" that is, unlike most State-level agencies they had an
entry other than zero in the "population covered" variable. See
"Addendum on zeropop agencies." )

Most of the 2,331 that did not get an award would not reach the
threshold if they reported fully; 525 of them reported no
violent crime, so it is unlikely that they would have enough
violent crime in the months they did not report to get an award.
(Footnote: Zero violent crime  is a possibility; the agency  may
have reported property crime (which is not on the files supplied
to us), or may have no crime at all but continues to participate
in the UCR program. Interestingly, 792 fully-reporting agencies
report no violent crime; 484 are zero population, and 308 have
populations.)  Of those reporting some violent crime, the
overwhelming majority are so far below the threshold for an
award in their States that it seems unlikely that they would
receive an award even if they had complete data. However, 28 of
the 2,331 had over 90% of the threshold amount, and another 26
had between 80% and 90%, and more complete data may have
resulted in an award.

Impact of Incomplete Data

It is clear that a considerable number of agencies are adversely
affected in the formula by missing data in the UCR data sets;
data that are missing for a variety of reasons beyond not
participating in the UCR program. In the course of responding to
State and local inquires, we heard some anecdotal evidence from
individual police departments who claim they submitted data to
the State program that are not showing up on the UCR files. For
formula purposes, the reason for missing data is a concern, but
not necessarily for imputation purposes. 

While estimating the total impact of missing data on the formula
allocations is not attempted, a few facts are clear:

* 262 jurisdictions that received an award would get a larger
award with more complete data coverage.

* It is possible to estimate that, if all cities reported at the
same rate as those that did for 36 months, an additional 84
cities would receive an award.

* Because of the configuration of the award files, it is not
possible to replicate that computation for counties, but
presumably some of the 2,212 counties that did not get an award
would with more complete data coverage. 

Although the impact of incomplete data seems greatest for larger
jurisdictions which may have hundreds of violent crimes a year,
each of which brings $216.62 in most States, small jurisdictions
in those States with very low thresholds such as Vermont (4
violent crimes) and North Dakota (7 violent crimes) might
quickly become eligible for an award with more complete data.
(Footnote:The LLEBG legislation establishes $10,000 as the
minimum local award.  Thus, a jurisdiction needs a certain
amount of violent crime to qualify for an award.  In some States
this amount is lower than the national average because the
State-level formula establishes a minimum State award regardless
of violent crime levels.)  In addition, the dollars per crime
figures for those States are considerably higher: $2,321.17 per
crime in Vermont and $1,509.17 in North Dakota.

In reviewing these results, it is necessary to keep in mind that
because the formula is relative and interdependent, an increase
in reporting (or in estimating missing data) would result in an
increase in the thresholds and a decrease in the dollar value of
a single crime.

Addendum on "Zeropop" Agencies

Agencies without an entry in the "population covered" variable
on the UCR tapes were coded to be "zeropop" in the variable by
that name. This was used as a surrogate indicator to identify
State police, special police, and similar agencies that were not
a city or county police or sheriff's office that might be
eligible for a local law enforcement formula award. This was
necessary because the UCR coding scheme does not allow the
separation of State police and university police from county and
city police. In the process of examining the extent of missing
data in the UCR files, it was discovered that some of these
agencies actually had a population entry and thus were not coded
"zeropop." A search of the file for "State Police" in the agency
name uncovered 136 of these with a population figure, about 15%
of agencies with "State Police" in the name. These are primarily
State police barracks in various counties reporting data 
for the county area. 

This had no effect on the distribution of fiscal 1996 awards
because any agency not coded as a city was treated as reporting
for a county. It will not affect the fiscal 1997 awards because
they will rely on the Census Bureau government identification
number that is being added to the files to allow accurate 
identification of city, county, and State agencies and special 
police forces. 

This should have no impact on the imputation of county-level
data because we will be able to provide a crosswalk between the
FBI's agency codes (ORI codes) and the Census Bureau government
identification, level-of- government, and type-of-agency codes
so that those doing the imputation can treat those agencies any
way they want.

Table 1. 1992-94 UCR Total Violent Crime Data for Input to 1996 LLEB

(Numbers on data file as percent of estimated amounts presented 
in Crime in the U.S.)

State           Percent               State               Percent
South Carolina        101.57 %        Minnesota               97.74
Idaho                 100.49          Colorado                97.56
Wyoming               100.15          Arizona                  97.5
Connecticut           100.01          Nebraska                97.48
Rhode Island          100.01          Alaska                  96.39
Hawaii                100             Georgia                 95.52
New Jersey             99.99          Missouri                95.41
Maryland               99.98          Illinois                95.32
Texas                  99.97          Pennsylvania            94.99
West Virginia          99.89          Nevada                  94.49
California             99.87          Michigan                94.13
Oklahoma               99.86          Tennessee               90.36
Virginia               99.81          Alabama                 90.24
New York               99.73          Ohio                    90.07
Wisconsin              99.70          South Dakota            89.65
Arkansas               99.66          Louisiana               89.27
Oregon                 99.43          New Hampshire           87.37
Maine                  99.31          Massachusetts           86.33
North Dakota           99.30          Indiana                 82.29
Washington             99.13          New Mexico              75.81
Kansas                 98.98          Iowa                    74.56
Kentucky               98.45          Vermont                 74.35
Florida                98.37          Mississippi             68.04
Utah                   98.33          Delaware                65.35
North Carolina         98.24          Montana                 63.00


Table 2. 1992-94 Summary of Actual Counts of Violent Crime on LLEBG Data Files 
as a Percent of the Amount Estimated by the FBI

Part I violent   
crimes on data                     Percent of estimated     Cumulative 
files as a percent     Number of   number of Part I         percent
of published estimate  States      violent crimes           of estimated crime

100% or more              6             12%                    12%
99% to 99.99%            14             28                     40
98% to 98.99%             5             10                     50
96% to 97.99%             5             10                     60
90% to 95.99%             9             18                     78
80% to 89.99%             5             10                     88
Less than 80%             6             12                    100
                         50            100


Table 3. Comparisons of UCR Violent Crime Data for Input to 1996 and 1997 LLEBG F
(Numbers on data file as percent of estimated amounts present in Crime in the U.S.

                   Part I violent crimes
                   as percent of published                   Difference in the percentage
                   estimates, 1992-94                        from 1992-94 to 1995
                                                 1995 
Alabama                       90.24%             95.09%              4.85%
Alaska                        96.39              94.18              -2.21
Arizona                       97.50              96.87              -0.63
Arkansas                      99.66              99.78               0.12
California                    99.87              97.19              -2.68
Colorado                      97.56              97.12              -0.44
Connecticut                  100.01              99.92              -0.09
Delaware                      65.35              35.51             -29.84
Florida                       98.37              98.75               0.38
Georgia                       95.52              96.39               0.87
Hawaii                       100                100.00                  0
Idaho                        100.49              99.17              -1.32
Illinois*                     95.32              63.03             -32.29
Indiana                       82.29              75.05              -7.24
Iowa                          74.56              79.03               4.47
Kansas*                       98.98              34.21             -64.77
Kentucky                      98.45              91.51              -6.94
Louisiana                     89.27              94.50               5.23
Maine                         99.31              99.63               0.32
Maryland                      99.98              99.99               0.01
Massachusetts                 86.33              91.49               5.16
Michigan                      94.13              94.96               0.83
Minnesota                     97.74              99.47               1.73
Mississippi                   68.04              61.41              -6.63
Missouri                      95.41              94.74              -0.67
Montana                       63.00              17.92             -45.08
Nebraska                      97.48              99.07               1.59
Nevada                        94.49              99.32               4.83
New Hampshire                 87.37              75.19             -12.18
New Jersey                    99.99             100.09               0.10
New Mexico                    75.81              73.24              -2.57
New York                      99.73              98.70              -1.03
North Carolina                98.24              98.83               0.59
North Dakota                   99.3              95.32              -3.98
Ohio                          90.07              83.98              -6.09
Oklahoma                      99.86              99.84              -0.02
Oregon                        99.43              97.90              -1.53
Pennsylvania                  94.99              83.95             -11.04
Rhode Island                 100.01             100.11               0.10
South Carolina               101.57             102.38               0.81
South Dakota                  89.65              82.35              -7.30
Tennessee                     90.36              87.66              -2.70
Texas                         99.97              99.78              -0.19
Utah                          98.33              95.84              -2.49
Vermont                       74.35              90.61              16.26
Virginia                      99.81              99.88               0.07
Washington                    99.13              95.90              -3.23
West Virginia                 99.89              99.90               0.01
Wisconsin                      99.7              99.83               0.13
Wyoming                      100.15              99.34              -0.81

Maximum                      1.0157               1.0238
Minimum                        0.63               0.1792



Table 4.  Number of Months/Years Reporting, 
All Agencies in UCR Files,

                  Number of                   Percent of 
                  reporting agencies          all agencies
No months                3516                     19.1%
1 - 11 months             404                      2.2
1 full year               508                      2.8
13 - 23 months            464                      2.5
2 full years              530                      2.9
25 - 35 months           1291                      7.0
3 full years            11700                     63.5
Total                   18413                    100



Table 5.  Agencies Reporting Zero Months, by Whether Covered
by Another Reporting Agency or "Zeropop" 

                         Covered by another agency
                             No             Yes            Total
Zeropop                     1027            194             1221
Non-zeropop                  836           1458             2294
Total                       1863           1652             3515



Table 6.  Population Distribution of Agencies Reporting
and not Covered by Another Reporting  Agency or "Zeropop"

Government type and size   Frequency   Percent
City 100,000-250,000           1         0.1
City 50,000-100,000            3         0.3
City 25,000-50,000            12         1.4
City 10,000-25,000            38         4.4
City 2,500-10,000            197        22.7
City under 2,500             398        46.0
County 100,000 or more         2         0.2
County 25,000-100,000         46         5.3
County 10,000-25,000          97        11.2
County under 10,000           72         8.3
Total                        866         100

Table 7.  Agencies Reporting 1 to 35 Months, by Whether
Covered by Another Reporting Agency by 1994 or "Zeropop"

                      Covered by another agency
                       No         Yes       Total
Zeropop               549           5         554
Non-zeropop          2593          50        2643
Total                3142          55        3197

---------------------------------------------------------------

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Glossary 

BGP: Boston Gun Project, a multi-organization effort
that served to reduce the number of youth homicides in Boston.

BJA: Bureau of Justice Assistance, one of the agencies of the
Office of Justice Programs that provides programmatic and
financial assistance to State and local criminal justice
agencies.

BJS: Bureau of Justice Statistics, the statistical arm of the
U.S. Department of Justice.

CCSPD: Cook County (Illinois) Sheriff's Police Department.

CHD: Chicago Homicide Dataset, a computer file of homicides
maintained by the Illinois Criminal Justice Information
Authority and available for downloading from NACJD. 

CIUS: Crime in the United States, the annual report on crime
published by the FBI.

CJIS: Criminal Justice Information Services Division of the FBI,
responsible for the collection, analysis, and publication of
crime, arrest, and other police-related data.

DOJ: U.S. Department of Justice.

FBI: Federal Bureau of Investigation, the investigative arm of
the U.S. Department of Justice.

IACP: International Association of Chiefs of Police, the
organization that initiated the Uniform Crime Reporting Program.

ICPSR: Inter-university Consortium of Political and Social
Research, an organization established by colleges and
universities to store political and social data files so they
may be accessed by researchers for analysis.

LEAA: the Law Enforcement Assistance Administration, an agency
established by the Omnibus Crime Control and Safe Streets Act of 
1968, in which was housed many of the agencies and functions 
now part of the Office of Justice Programs.

NACJD: National Archive of Criminal Justice Data, a unit within
the Inter-university Consortium of Political and Social Research
that serves as a repository of criminal justice data funded by
BJS and NIJ and housing data files produced by these agencies 
and their grantees/contractors, from which the files can be obtained.

NCHS: National Center for Health Statistics, 
a statistical arm of the U.S. Department of Health and Human
Services.

NIBRS: National Incident-Based Reporting System, the crime data
collection system that will replace the UCR system.

NIJ: National Institute of Justice, the research arm of the U.S.
Department of Justice.

OJP: Office of Justice Programs, an office within DOJ that
houses the Bureau of Justice Statistics, Bureau of Justice
Assistance, National Institute of Justice, and other agencies
that deal with State and local criminal justice agencies, 
issues and programs.

ORI: Originating Agency Identifier, the identification number
used by the FBI to identify police agencies

PSS: Program Support Section, the section of the FBI's Criminal 
Justice Information Services Division that deals with the 
collection, analysis, and publication of crime data.

SAC: Statistical Analysis Center, an agency established in many
States during the 1970's, often with funding from BJS.



SAS: Statistical Analysis System, computer software for data
analysis.

SHR: Supplementary Homicide Reports, a supplement to the Uniform
Crime Reporting Program that collects information about each
homicide incident, including victim and offender
characteristics, the relation between victim(s) and offender(s),
and incident circumstances.

SPSS: Statistical Program for the Social Sciences, computer
software for data analysis.

UCR: Uniform Crime Reports or the Uniform Crime Reporting
Program, the FBI program that collects, analyzes, and publishes
crime, arrest and police personnel data from police agencies
throughout the United States.