FUNDING COMMUNITY POLICING TO REDUCE CRIME:

 HAVE COPS GRANTS MADE A DIFFERENCE FROM 1994 to 2000?*

 Submitted to the Office of Community Oriented Policing Services,

 U.S. Department of Justice

by

Jihong “Solomon” Zhao, Ph.D.

Department of Criminal Justice

University of Nebraska at Omaha

Omaha, NE 68182

(402) 554-3928

Quint Thurman, Ph.D.

Department of Criminal Justice

Texas State University - San Marcos

601 University Drive

San Marcos, TX 78666-4616

(512) 245-3341

July, 2004

*This project was supported by cooperative agreements #2001-CK-WX-K002, #2001-CK-WXK053 and #2002-CK-WX-K010 awarded by the Office of Community Oriented Policing

Services, U.S. Department of Justice. Points of view or opinions contained in this document are

those of the authors and do not necessarily represent the official positions or policies of the U.S.

Department of Justice. We would like to take this opportunity to thank the anonymous peer

reviewers and the GAO review team for their helpful comments on earlier versions.
1

FUNDING COMMUNITY POLICING TO REDUCE CRIME:

 HAVE COPS GRANTS MADE A DIFFERENCE FROM 1994-2000?

ABSTRACT

This research examines how funding from the U.S. Department of Justice, Office of

Community Oriented Policing Services (COPS) from 1994-1999 has affected violent and

property crime rates in the United States from 1995 to 2000. Drawing upon seven years of panel

data, we examine the effects of three types of awards made by COPS to 5,659 law enforcement

agencies serving more than 133 million citizens and 1,938 non-COPS funded agencies serving

around 21 million citizens in order to assess the impact on crime reduction over time in

jurisdictions receiving funding and controlling for baseline levels of crime, socioeconomic

characteristics, city size, population diversity and mobility, and other unobserved control

variables (through the use of dichotomous city level variables). Our analyses suggest that COPS

hiring and innovative grant programs are related to significant reductions in local crime rates in

cities with populations greater than 10,000 for both violent and non-violent offenses. 

Multivariate analysis shows that in cities with populations greater than 10,000 an increase in one

dollar of hiring grants per resident contributed to a corresponding decline of 10.95 violent crimes

and 27.88 property crimes per 100,000 residents. Similarly, an increase in one dollar of

innovative grant funding per resident was found to contribute to a decline of 4.30 violent crimes

and 10.07 property crimes per 100,000 persons. Furthermore, COPS MORE grants had a

significant effect on property crime between 1995 and 2000. An increase in one dollar in MORE

grant funding corresponded to a decline of 17.12 property crimes per 100,000 residents. In
2

addition, the findings suggest that COPS grants have had no significant negative effect on violent

and property crime rates in cities with populations less than 10,000.
3

METHDOLOGICAL ABSTRACT

This study draws upon four existing data sources, the Uniform Crime Reports, the Bureau

of Labor Statistics, the U.S. Census, and COPS Office data to create seven years of panel data

(1994-2000) including all city level law enforcement agencies for which data could be obtained

(both those who did and did not receive COPS funding). A two-factor fixed effect model was

used to analyze the effects of COPS funding on crime reduction controlling for eight social

disorganization variables. The “two-factors” model allows for the analysis to also attempt to

control for unobserved systematic (non-random) variation. The “two-factors” approach

accommodates a geographic component represented by the cities in which agencies reside, and a

time-specific component represented by the six years of data. By including the “first-factor” (the

geographic component) through the inclusion of a cross-sectional dummy variable for each city

in which the agencies reside, the difference in crime rates caused by unobserved variance

occurring in each city is estimated. This means that an attempt can be made to control for the

bias caused by omitted variables. Similarly, the “second-factor” (the time-specific component)

involves the inclusion of year dummy variables that attempts to control for those unknown

factors impacting crime in the U.S. nationally that are not accounted for by the other independent

and socioeconomic variables. This type of dummy variables in panel model designs is common

and their benefits to model specification are well known (Wooldridge 2001). This panel data

model attempts to provide a test for causality by: 1) establishing correlation between levels of

COPS funding and crime rates the following year across the study period, 2) demonstrating time

order of the variables through the longitudinal design, and 3) controlling for spuriousness (the
4

possible effect of third variables on the relationship) through the inclusion of control variables

that account for both unobserved and observed variation. In doing so, this study attempts to

provide evidence, given the available data, concerning the relationship between COPS funding

and crime rates.
5

FUNDING COMMUNITY POLICING TO REDUCE CRIME:

 HAVE COPS GRANTS MADE A DIFFERENCE FROM 1994-2000?

Crime rates have dropped significantly across the United States in most large cities since

the mid-1990s and continued into the early 21st century. While there are studies examining the

relationship between demographic changes and this decline in crime at the national level (e.g.,

Blumstein 2001; Spelman 2001), little is known about the contribution of the recent

implementation of community oriented policing funded principally through the U.S Department

of Justice, Office of Community Oriented Policing Services (COPS). The extent of assistance

provided by the COPS Office to local law enforcement is unprecedented in American history. 

Therefore, the activities of this Office may have significantly contributed to the drop in crime in

jurisdictions receiving this Federal support. However, determining the extent to which COPS

Office awards have made a difference in reducing crime has been problematic to date. On a

national level, the impact of COPS funding on crime has been difficult to estimate due to the fact

that much of the research designed to assess the effects of COPS programs is either limited to

individual programs or cities (Rosenbaum 1994; Kartcoski and Dukes 1995; Silverman 1999;

Sadd and Grinc 1994).

The purpose of this research is to extend a previous research study (Zhao and Thurman

2001) using a similar model to examine the overall impact of COPS funding nationally over a

substantial period of time and controlling for likely confounding factors. Accordingly, we

assembled a multi-wave panel data set featuring 7,597 COPS funded and non-funded cities

serving more than 154 million people living in the U.S.1

 After familiarizing the reader with the

 

1

 The number of cities varied by year (from a high of 5,099 cities in 1995 to a low of 4,827 in 1997) due to the fact
6

COPS Office and its activities, we describe the data that was assembled for this analyses.

THE COPS OFFICE

Community policing has become a dominant force behind contemporary police

innovations designed to reduce crime throughout the U.S. since the mid-1990s (for a review see

Cordner 1997; Eck and Maguire 2001; Zhao at al. 1999). Perhaps the strongest driving force

behind the implementation of community policing in recent years has been the steadfast

endorsement of this concept by the federal government, and specifically, the passage of The

Violent Crime Control and Law Enforcement Act (the Crime Act) in September, 1994. Title I of

the Crime Act, known as the “Public Safety Partnership and Community Policing Act of 1994”

authorized the use of $8.8 billion to fund local law enforcement agencies in the fight against

crime through the enhancement of their community policing capabilities. To carry out this task,

the U.S. Department of Justice created a new agency--the Office of Community Oriented

Policing Services (the COPS Office)--to administer and supervise new grant programs resulting

from the act (Roth and Ryan 2000).

Over the past eight years, the COPS Office has awarded grants to law enforcement

agencies amounting to more than 8 billion dollars. These awards have included funds for the

direct hiring of additional community policing officers, awards to support innovations, and those

 

that 12 months of crime information reporting to the FBI was a selection criterion while the number of non-funded

cities varied from a high of 1,671 in 1995 to a low of 1,553 in 1997. There were 4,885 cities that received COPS

grants during the entire time of study (1994 to 1999), the others only received grants at different times during the

study period. Accordingly, the statistical modeling uses an unbalanced panel data design. The previous study results

were re-run using this same unbalanced panel data design (Zhao and Thurman 2001). None of the substantive results

changed based on this more rigorous model. 1994 census data was used to group cities into more than 10,000

population or less than 10,000 population groups in order to avoid double estimating caused by shifting of

population above and below the 10,000 threshold by a small number of cities between 1994 and 1999. 
7

for new technology (through the COPS MORE Program).2

 Through these award programs the

COPS Office has provided funding for more than 118,000 additional community policing

officers.

Hiring grants are designed to directly assist local law enforcement in the deployment of

additional community police offices. While the Universal Hiring Program (UHP) has become

the primary and best known initiative, smaller precursor hiring programs included PHASE 1, the

Accelerated Hiring, Education, and Deployment Program (AHEAD), the Funding Accelerated

for Smaller Towns Program (FAST), and the Police Hiring Supplement Program (PHS). Awards

made in each of these programs stipulated that the COPS Office would contribute a maximum of

75 percent of the cost of hiring a law enforcement officer, up to $75,000 for a period of three

years.3

 The purpose of hiring grants is to increase the number of law enforcement officers

engaged in community policing activities in their communities. To date, the COPS Office has

awarded approximately $5.5 billion dollars in hiring grants to law enforcement agencies.

The Making Officer Redeployment Effective (MORE) grant program represents a second

award category. MORE grants provide funding for law enforcement agencies to acquire new

technology and to add civilian personnel in order to increase officer effectiveness and efficiency.4

 

2

The COPS Office also provides funding for a number of training and technical assistance grant programs designed

to enhance the community policing abilities of local law enforcement. These grant programs include the funding of

Regional Community Policing Institutes and the production of technical assistance guidebooks and evaluations of

community policing programs. These programs are not examined here and account for a relatively small portion of

the overall COPS budget.

3 In certain rare circumstances, the COPS Office has provided agencies with waivers. In these cases, COPS covers all

or a portion of the 25% local match. This does not influence the results because the total dollar amount of COPS

funding is still the independent variable. Based on data collected by the COPS Office, 90% of grant funded officers

have been hired, completed training and are on the street within 12 months. Therefore, we decided to use a one-year

lag in the panel analysis.

4 In 1995, the COPS Office provided a small portion of funds from the MORE program to fund officer overtime pay.
8

 Civilians are hired to perform administrative and support tasks previously performed by officers.

Both the procurement of technology and hiring of civilians are expected to save officer time so

that they might be redeployed to the street to engage in community policing. These two facets of

MORE grants (civilians and technology) differ significantly from one another in terms of the

time it takes to implement and produce officer time-savings and, therefore, are treated somewhat

differently in this analysis, as is discussed later. For the MORE 95, MORE 96 and MORE 98

programs, the COPS Office requires grantees to track “time-savings” resulting directly from the

technology purchased or civilians hired through this grant program. The Office estimates that for

every 1,824 hours per year that the grantee saves, one officer is redeployed into community

policing efforts.5

 To date, COPS has awarded approximately 1.3 billion dollars in MORE

grants.6

Innovative grants comprise the third and final group of awards offered by the COPS

Office.7

 Overall, innovative grants make up a smaller portion of the COPS budget than either

hiring or MORE grants. Innovative grants fund specialized programs targeted at specific

jurisdictions and/or categories of crime and social disorder. The Distressed Neighborhoods

Grant Program is one example of an innovative grant program targeted at specific jurisdictions.

This program provided funds for eighteen jurisdictions that were identified by the COPS Office

as having some of the most significant public order and economic challenges in the nation. 

 

 These funds are also included under MORE grants in this analysis.

5 The COPS Office refers to this as an officer Full Time Equivalent (FTE).

6 Note that MORE grants have not been awarded by the COPS Office since the year 2000.

7

In this analysis, the following COPS programs are categorized as innovative grant programs 311, Advancing

Community Policing Program, Organizational Change Demonstration Centers, Methamphetamine Initiative,

Distressed Neighborhoods Program, Community Policing to Combat Domestic Violence, Anti-Gang Initiative,

Problem Solving Partnerships, Youth Firearms Violence Initiative, Integrity Initiative, and the School Based
9

Cities were directed to analyze various sources of neighborhood level data in order to concentrate

community police officers into a relatively small number of high problem areas within their city.

The Community Policing to Combat Domestic Violence Program is an example of an innovative

program that was designed to target a specific type of problem. Grantees submitted proposals

regarding how they would employ community policing strategies to combat the problem of

domestic violence. Innovative grants typically represent the most competitively determined

awards, although some awards result from targeted solicitations by the COPS Office. To date,

COPS has awarded approximately 661 million dollars for these specific innovative grants.

As of April 2004, the COPS Office estimates that approximately 101,962 of the more

than 118,000 funded officers were on the street. This includes 69,124 officers hired from hiring

and innovative grant programs, and 32,838 “redeployed officers” resulting from the MORE

program. These figures are derived through a yearly “COPS Count” survey conducted by the

COPS Office of its grantees. All active hiring grants and a stratified random sample of MORE

grants are surveyed. In all, for the most recent count, COPS collected fax, telephone and webbased surveys from over 11,000 agencies who provided information regarding over 17,000

grants. The fact that officers are actually being hired and redeployed as a result of COPS funding

is also supported by an analysis by the Urban Institute of hiring and redeployment (Koper et al.

2001). It is important to note that the Crime Act specifically dictates that grants are to be used

only to hire additional law enforcement officers and the COPS Office attempts to be vigilant in

this regard. The fact that COPS Office grants do not simply result in the funding of previously

existing officers is supported through an independent evaluation (Koper et al. 2001).

 

Partnerships Program.
10

THE RELATIONSHIP BETWEEN POLICING AND CRIME

After steady increase during 1970s and 80s, national crime data reported by law

enforcement agencies indicate that crime rates, particularly those involving crimes of violence,

have decreased significantly since 1994 (Uniform Crime Report, 1994-2000). Violent crime

rates are at their lowest level in the past thirty years, particularly in large metropolitan areas. The

timing of this crime decrease also coincides with increases in support from the COPS Office and

increases in the number of community police officers and community policing programs across

the country. The implementation of community policing activities, which has increased

dramatically during the same period, may have played a role in this crime reduction. Evidence

for an increase in the amount of community policing can be found in a comparison of the 1997

and 1999 LEMAS Surveys, a national survey of law enforcement agencies conducted by the

Bureau of Justice Statistics (Hickman and Reaves 2001). These data indicate that the percentage

of departments employing personnel designated as community police has risen from 34% to 64%

and the absolute number of “community policing” officers rose from 21,000 to 113,000 from

1997 to 1999. In addition, numerous projects under the umbrella of community policing have

been implemented in police agencies across the nation (for a review, please see Greene 2000). 

Scholars have suggested that community policing in general may be partly responsible for

this nationwide decrease in crime (Marvell and Moody 1996). The use of innovative strategies

for crime prevention, problem solving, and community partnerships have been found to reduce

local crime and social disorder problems in many jurisdictions where federal support has been

received (for documented evaluations of the effect of community policing on social disorder,
11

crime, and fear of crime in American cities please see Rosenbaum 1994; Kartcoski and Dukes

1995; Skogan and Harnett 1997). Storefront stations and foot patrol in heavily populated

residential neighborhoods or business districts have been shown to increase police knowledge of

crime problems in addition to providing a greater police presence. Through the mobilization of

local community residents and through problem solving projects, community policing has been

observed to help reduce social disorder and crime incidents through information and resource

sharing between law enforcement and the community (for a review of community policing

strategies and innovative programs, please see Cordner 1997; Eck and Maguire 2001). The

community policing philosophy has enjoyed widespread support among both academicians and

practitioners, as well as a considerable amount of anecdotal evidence that suggests popularity

among those who have tried it at differing levels. There have also been numerous scientific

studies and evaluations on the effectiveness of community policing programs across the country,

including longitudinal studies, cross-sectional studies, and multiple site evaluations (Sadd and

Grinc 1994; Silverman 1999; Katz et al. 2001; Weisburd and Green 1995).

At the national level, Zhao and Thurman (2001) examined the effect of COPS funding on

crime reduction in more than 6,000 cities and found COPS hiring and innovative grants were

significantly related to declining violent and property crime rates between 1994 and 1999.8

 

8 There is another national statistical study examining the effect of COPS grants on crime reduction (Muhlhausen

2001). However, this study had a serious problem concerning the dependent variable, county-level crime rate. First,

in the data obtained from the Heritage Foundation, on average 40% of police agencies (usually small departments)

included in their sample did not receive COPS grants during the period of study. However, their crime rates were

included in the analysis as an aggregate dependent variable to assess the effect of COPS grants on crime decline at

the county level. For example, this research assumes that the grants that COPS made to 7 law enforcement agencies

in Galveston County, Texas over the study period will affect the crime rate for the entire 29 law enforcement

agencies located in Galveston County. This introduces severe bias against finding an effect of COPS grants. Second,

it is inaccurate to aggregate crime incidents of all police agencies located in the same county because not all of them
12

Similar research on the relationship between police strength and crime incidents also indicates

that the number of police officers may have a significant impact. For example, in pooled time

series-cross-sectional study on the relationship between police and crime in 56 cities, findings

indicated that each additional officer at the city level results in approximately 24 fewer crimes,

including 0.2 homicides, 0.7 robberies, and 3.2 burglaries (Marvell and Moody, 1996). The

research on the relationship between police strength and crime rate is not conclusive. There have

been studies that found a negative relationship (e.g., Ehrlich 1972) and there have been equal

number of studies that have found an insignificant effect (for a review, see: Eck and Maguire

2000).

RESEARCH METHOD

DATA

The data used in this analysis were derived from four sources. The first source is the

Uniform Crime Reports (UCR) published annually by the Federal Bureau of Investigation. UCR

data reflect a nation-wide effort to collect data from approximately 17,000 state, county, and city

law enforcement agencies voluntarily reporting crimes that have been brought to their attention. 

These data on “crimes known to police” contained in the UCR from 1994 to 2000 were

downloaded from the ICPSR web site at the University of Michigan, a data collection warehouse

which stores them. The Part I index crimes reported by the UCR includes eight categories of

crime reported from 90 percent of law enforcement jurisdictions regularly providing such local

crime data to the FBI and are typically thought to contain the most serious forms of criminal

 

may have reported their respective crime incidents for 12 consecutive months. This may lead to missing data in one

or more agencies in a county, and can be more of a problem in panel data, which requires reporting of all agencies
13

activity. Part I crimes include four categories of violent crime (murder, rape, robbery, and

aggravated assault) and four categories of property crime (burglary, larceny, auto theft and

arson). All of these crimes with the exception of arson are included in this analysis. Arson was

excluded because it was not available for all of the study years. Although it is well understood

that the UCR underestimates the amount of crimes that actually occur, UCR Part I crimes are

thought to provide generally reliable (consistent) underestimates of these rates and therefore

provide an accurate measurement of changes in crime rates (the dependent variable of interest

here). Use of UCR crime rates is common in criminological research and its benefits and

limitations are well understood. 

A second source of data was the dollar amount of grants awarded to individual law

enforcement agencies from the COPS Office from 1994 to 1999. As stated earlier, COPS grants

are broken down into three general categories--hiring grants, innovative grants, and MORE

grants. In the original data provided by the COPS Office, there were 12,070 law enforcement

agencies that received funding from 1994 to 1999. Because the other sources of data (UCR,

Census, and Labor Statistics) are collected at the local level, and to avoid overlap between

agency jurisdictions, only local police departments are included in the analysis; the following

types of agencies were excluded: state police agencies, county police agencies and sheriff’s

offices, university/college police departments, and special purpose law enforcement agencies like

court police, forest police, park police etc. This left 7,179 local city police agencies available

(not yet the final sample for analysis). 

There is no meaningful way to include these types of agencies in the model because of

 

for 12 months over the entire period of study.
14

overlapping jurisdictions. If they were to be included this would introduce a substantial amount

of error into the analytical models. Thus, the analysis was limited to the defined boundaries of

cities and both the COPS and crime variables were measured at this level. Any nested effect of

other law enforcement agencies located in the city (e.g., school district police, university police,

park police, sheriff office, etc.) theoretically may contribute to the crime decline in a city (usually

big cities), however, these agencies report their own separate annual crime incidents to the FBI. 

Thus, it is reasonable to assume that any crossover due to reporting is minimal. Moreover, there

is no clear census information on school district police, park police, etc. Lack of socioeconomic

variables makes the estimate of the effect of these agencies extremely difficult, if not impossible

at the national level. In the hundreds of studies on city crime rates in the past four decades we

are unaware of a single study that attempts to measure this crossover by including school district

police, court police, park police etc. in an analysis.

Demographic information at the city level was also included from the 1990 and the 2000

U.S. Census. 1990 Census data were obtained from the CD disk and 2000 from DVD disk

published by the U.S. Bureau of Census. Finally, unemployment information (1994 to 1999) was

obtained from the Department of Labor Statistics. The Labor Department collects annual

employment data in cities with populations greater than 25,000. County level unemployment

data from the same source was used for cities with populations less than 25,000, because for

these small cities, unemployment data is not available at the city level.

VARIABLES IN THE ANALYSIS

Dependent Variables. The two dependent variables employed in the analysis are derived
15

from Uniform Crime Report data for violent crimes and property crimes per 100,000 population.

Consistent with the UCR format, the violent crime rate reflects the sum of the incidences of four

crimes (murder, rape, robbery, and aggravated assault) divided by each city’s population and

multiplied by 100,000. Similarly, the property crime rate reflects the sum of the incidences per

100,000 for three crimes--burglary, larceny, and auto theft.

Independent Variables. Three independent variables were used in the analysis to represent

the type of award made (hiring grant, innovative grant, or MORE grant). The total amount of

each type of grant funding received by a city in each calendar year (1994-1999) was divided by

the city’s population so that the total for each type of grant program was standardized to indicate

the dollar amount received per year, per resident for each city. In addition, the total dollar amount

of COPS funding was adjusted to 1994 dollars using the Consumer Price Index. There were a

total of 7,179 local city police departments that were funded by the COPS Office from 1994 to

1999. Of this number, 535 cities with populations less than 1,000 were excluded from the

analysis since the data for these smallest of cities were often inaccurate and tended to fluctuate

widely to the extent that these variations made the results difficult to assess with much

confidence over time.

Additionally, we excluded another 985 COPS funded cities from the data due to the lack

of complete UCR crime data over the six-year period or/and lack of census data, leaving 5,659

COPS funded cases for analysis (including 2,306 cases with population over 10k and 3,353 cases

with population less than 10k). At the same time, there were 1,938 local city law enforcement

departments included in the study that did not receive COPS grants during the period of study
16

and had the complete UCR and census data. These 1,938 non-funded cities are used as control

group in the analysis (including 354 cases with population over 10k and 1,584 cases with

population less than 10k). All the police agencies in the analysis reported 12 months of UCR

data in a given year. In addition, cities in four states (Delaware, Illinois, Kansas, and Montana)

were not included in the analysis due to missing UCR data, except for the following cities:

Dover, Delaware; Havre, Montana, and Wichita, Kansas. We are well aware of the limitations of

using UCR data due to the presence of these missing cases, however, the use of UCR data is

widely accepted in criminological work. Moreover, in the analyses presented here there is no

specific theoretical reason to suspect that the effect of COPS funding on crime would be any

greater or any less in the cities that are omitted from the analyses due to this missing data, thus

making it unlikely that their inclusion would alter the results.9

 

There are three additional specifications for the independent variables. First, the three

COPS grant independent variables (hiring grants, MORE grants, and innovative grants) were

lagged by one year when their impact on crime was analyzed. This one-year lag provides

implementation time to hire officers, procure technology, and initiate innovative programs. This

one year lag was based on internal COPS Office tracking systems that indicate on average it takes

12 months to hire, train and deploy an officer from the award start date.10 Accordingly, data

concerning COPS Office funding coincides with grants awarded from 1994-1999 and UCR crime

 

9 There is another issue related to the missing UCR data: whether estimates should be used to replace the missing

UCR values at the city level. In UCR data, these missing values are systematic and usually concentrated in several

states (e.g., Illinois, Florida, etc), thus, the use of estimates may lead to more bias than not using such estimates. 

Consequently, missing values of UCR data are excluded from the current analysis.

10 This is based on an analysis conducted by the COPS Office of its yearly “COPS Count” data, an annual survey of

all of its hiring grants.
17

data were included for 1995-2000. Second, because hiring grants are intended to hire officers

over a three-year period, hiring grants awarded to police departments are allocated over that

period in a declining rate according to the following factors: 38 percent for the first year, 34

percent for the second year, and 28 percent for the third year.11

Finally, although MORE grants are designed as one-year grants, agencies typically take a

longer period of time in order to procure the technology, make it operational, and train officers in

its use. Therefore, the technology portion of the MORE grants was spread out over a period of

three years to compensate for this fact according to the following allocation: 36 percent for the

first year, 36 percent for the second year, and 28 percent for the third year.12 In addition, analysis

of COPS Office tracking systems (COPS Count) indicated that civilians are typically hired within

one year of the grant. Thus, the MORE funding variable used in this analysis was calculated for

the first year as the sum of the civilian portion of the grant plus 36 percent of the technology

portion of the grant. In the second year, 36 percent of the technology portion of the grant was

used, with the remaining 28 percent of the technology portion allocated to the final year.

Control Variables. The 1994 crime rate was included as a control variable in this analysis.

 Inclusion of the 1994 crime rate allows for the analysis to be standardized to examine the change

in crime rates since 1994. Also, 1994 was the point at which communities began receiving

federal funds from the COPS Office and controlling for the crime rate at this time enables the

 

11These numbers are based on COPS Office recommendations regarding how agencies spend money over the threeyear period. The adjusted allocation of COPS hiring grant over a three-year period is used for panel data analysis. 12 This allocation method was based on an analysis of the average actual spending patterns of police departments who

have received COPS MORE grants. The adjusted allocation of COPS MORE grant over a three-year period is used

for panel data analysis.
18

analysis to focus on the period after the creation of the Office. The inclusion of the 1994 crime

rate reflects the ability of police agencies to control the level of crime incidents given “typical”

resources including budget and personnel levels without COPS funding. It should be noted that

the inclusion of city dummy variables also attempts to compensate for these factors (also see

Appendix A for a discussion of unbiased estimates concerning independent variables). 

Eight other control variables designed to account for the socioeconomic health of

communities also were included. Social disorganization theory developed by Shaw and McKay

provides a theoretical framework for the inclusion of these variables in this analysis (for a

discussion and testing of social disorganization theory see Shaw and McKay, 1972; Bursik, 1988;

Sampson, 1985; Sampson and Groves, 1989; Smith and Jarjoura, 1989). Scholars of social

disorganization theory argue that unique socioeconomic characteristics of communities are

closely associated with local crime problems. Specifically, there are three primary

socioeconomic dimensions that merit empirical scrutiny (Osgood and Chambers 2000). The first

dimension is community heterogeneity. In this study, heterogeneity is represented by the

percentage of minority residents and percent male in a community. The second dimension is a

community’s socioeconomic status, measured here by five variables: the percentage of

unemployment, percentage of single parent households, percentage of young people between the

ages of 15 to 24, percentage of home ownership and per capita income. The final dimension we

control for is community mobility as indicated by the percentage of people having lived at the

same address since 1985. Since the census data are available for every decade we created a

measure, estimating the change in socioeconomic characteristics from 1994 to 1999. That is, the
19

time-varying socioeconomic variables are measured via linear trend (Kovandzic et al. 2002).13

Ideally a researcher would like to include all of the variables of interest in the analytic

equation to say with absolute confidence that every potential control variable is included. In

reality, however, for all social science research, there will always be variables that are

unavailable for inclusion or cannot be included due to issues with multicollinearity and lack of

available data. However, a special statistical advantage of panel data analysis (as compared to

more common cross-sectional data analysis) is that it is able to capture unobserved variance

through the use of dummy variables. By using dichotomous variables (in this case city and time

dummy variables and in other models county dummy variables) we were able to control for

unobserved systematic variance across the time period studied. Thus, these variables attempt to

control for other factors such as police expenditures, changes in the national economy, and access

to local and federal grant funding. The effect of these variables accounts for the most significant

effects in the entire equations and results in an exceptionally large amount of explained variance.

In addition, the use of this type of dummy variables in panel model designs is common and their

benefits to model specification are well known (Wooldridge 2001).

One specific control variable to mention is police expenditures; that is the amount of

funding local departments use to address crime issues. We were unable to include this specific

 

13 Time-varying variables can be defined as variables that have annual observations during the period of study (e.g.,

the annual change in single mother household between 1994 and 2000 based on 1990 and 2000 Census data). The

fixed effect panel model means it uses time and city level dummy to control for unobserved variation in the panel

data. There is also possibility that the fluctuation of drug market may correlated with the crime rate in cities. 

However, a careful search on the available drug related data reveals that there has been no annual collection of data

concerning the nature of local drug market at city level except a few dozens of large cities included in the drug

forecasting project nationwide. 
20

variable due to the fact that it does not exist in a properly specified form at the city level.14 

However, we addressed this issue in multiple ways. First, as mentioned above, we included the

1994 crime rate to standardize for these effects. Second, the city level dummy variables used in

the model attempt to take such a variable into account. Third, it is unlikely that the inclusion of

this variable, if it were possible, would result in much additional explained variance in the

statistical model. This is due to the fact that the R2 for the models that we report are already

exceptionally high. Finally, we completed a separate follow-up study in which we collected

original police expenditure data (minus COPS funding) from 55 of the largest police departments

in the United States across the study period. We included these data in statistical models very

similar to those reported here. The findings showed that the inclusion of police expenditures had

virtually no effect on COPS funding. A brief description of this study can be found in Appendix

E. Because of all of these factors we find it unlikely that the inclusion of this control variable

(even if it were possible to include in the full sample analysis) would alter the substantive

findings of this report.15 

 

14 There is some limited data on police expenditures, however, it is not for all of the cities included in this analysis,

and perhaps more importantly, it does not explicitly exclude COPS Grant funding from its estimates. If COPS grant

funding is not explicitly excluded from these estimates, researchers have no way of knowing which agencies included

and excluded COPS funding in their data. In addition, some may argue that the drop in crime rate might have

happened or begun earlier than 1994 in some cities. In this analysis, the crime drop is examined in two different

ways. First, the annual crime rate in each city is investigated in the panel data analysis. Second, a comparison of

crime rates is made between the COPS funded cities and the non-funded cities between 1994 and 2000. Therefore,

the focus of this project is not on the initiation of the crime drop (prior to 1994) but on the rate of the crime drop

(change in rate over time) within the group (funded) and between groups (funded vs. non-funded) from 1994 to

2000.

15 One possible way to control for police budget is the use of the measure, number of sworn officers as reported by

the FBI annually. However, it is difficult to distinguish if the annual increase in the number of police officers is due

to COPS Office grants, due to an increase in departmental budget, or due to both. Consequently, we decided to use

two variables to control for that effect, the 1994 crime rate as a baseline and the fixed effect (dummy variables). 

Similarly, the Law Enforcement Management and Administrative Statistics (LEMAS) offers survey data of large

police agencies with more than 100 sworn officers concerning operation, organization structure, calls for service, etc.
21

DATA ANALYSIS

A two-factor fixed effect model was used to analyze the effects of COPS funding on

crime reduction. The “two-factors” model allows for the analysis to attempt to control for

unobserved systematic (non-random) variation. The “two-factors” approach accommodates a

geographic component represented by the cities in which agencies reside, and a time-specific

component represented by the six years of data. By including the “first-factor” (the geographic

component) through the inclusion of a cross-sectional dummy variable for each city in which the

agencies reside, the difference in crime rates caused by unobserved variance occurring in each

city is estimated. This means that the bias caused by such omitted variables can then be

estimated and an attempt made to control for in the panel data we analyze. Similarly, the

“second-factor” (the time-specific component) involves the inclusion of year dummy variables

that attempts to control for those unknown factors impacting crime in the U.S. that are not

accounted for by the other independent and socioeconomic variables. The current study covers

almost the entire population of large cities in the United States.16 Please see Appendix A for a

discussion of statistical model specification, Appendix B for a discussion on the differencing

issue, Appendix C for an analysis using city dummies removing percent male and per capita

income, Appendix D for an analysis using county dummy variables and removing percent male

 

 However, the LEMAS surveys have been conducted irregularly (1990, 1993, 1997, 1999, and 2000). There were no

surveys administered between 1994 and 1996, which is crucial for assessing the initial effect of COPS Office grants.

 Therefore, we decided not to use LEMAS data in the analysis.

16 All cities greater than 150,000 population received COPS grants between 1994 and 1999 except for Jacksonville,

Florida which received COPS funding in 2002. Therefore, Jacksonville, Florida, was used as the only non-funded

agency in cities greater than 150,000 population. In addition, about 87% of cities greater than 10,000 population

were funded by the COPS Office during the same period.
22

and per capita income, as was reported in a the previous version of this study. 17

The total number of variables on the right side of the equation in each analysis includes 3

independent variables (hiring grant funding, innovative grant funding, and MORE grant funding),

the 1994 crime rate, 8 time varying socioeconomic (demographic) variables modeled after social

disorganization theory, 6 year dummy variables to control for time variance, and 7,596 city

dummies to control for geographically located unobserved systematic variation in the panel

model.18

FINDINGS

DESCRIPTIVE STATISTICS

Descriptive statistics for the full sample appear in Table 1. As might be expected, the

means for both violent and property crime rates show that the property crime rate per 100,000

population is much higher than the violent crime rate in the cities studied. Similarly, hiring grant

programs were the largest programs funded by the COPS Office with a mean of $2.65 per person

during the six-year period, followed by MORE grants ($0.76) and innovative grants ($0.49). The

 

17 Appendices C and D were run so that the current study model could be compared to those reported in the pervious

report (Zhao and Thurman, 2001). Note that in both of these models, percent male and per capita income were both

removed (as was the case in the previous version of the report). The inclusion of these variables came at the

recommendation of outside reviewers. Also, the analyses presented in Appendices C and D are for COPS Funded

agencies only and do not include non-funded agencies as was the case in the previous version of this report. The

results showed that the coefficients of the three COPS grant categories remain very stable regarding the directions of

sign and magnitudes of coefficients.

18 The explanatory and control variables are chosen based on a review of literature (social disorganization theory)

and recommendations from the GAO review team (percentage of male and per capital income). Multicollinearity is a

potential problem in panel data analysis. Accordingly, the variance inflation factor (VIF) often is used to detect

whether high collinearity exists between any variables. Some researchers use a VIF score of 4 or greater as an

indication of severe mulicollinearity (Fisher and Mason, 1981). Our initial collinearity statistics showed that none of

the VIF values exceeded 4. Initially, the variable, percentage of people below poverty, was included in the analysis.

 However, the correlation between variable, percentage of unemployment, and percentage of people below poverty

line was very high. Therefore, only percentage of unemployment is included in the current analysis.
23

demographic variables are based on Census data, using the mean from 1994-1999. The

demographic variables show that the six-year average of unemployment in the sample was 4.75

percent. About one-third of the residents living in these cities identified themselves as minority

(37.56%), and single parent households comprised 11.89 percent of the population. The

percentage of young people was 14.94. In addition, 57.24 percent of the residents were

homeowners, about one-half of the residents (51.13%) lived at the same address during the five

years prior to the survey, 48.21% of residents were male, and the average per capita income was

$18,947.

----- Table 1 About Here -----

ANALYSIS BY POPULATION SIZE

We also examined crime reduction attributable to COPS grants according to city size.

There has been considerable discussion suggesting that the recent decline in crime rates across

the U.S. primarily is a large city phenomenon, perhaps even a trend heavily influenced by

enormous drops in crime in America’s largest metropolitan areas (for reviews see Eck and

Maguire, 2000; Silverman, 1999). Conversely, few studies specifically have examined crime

rates in very small cities and rural areas where the literature suggests that styles of policing and

patterns of crime problems differ significantly from those found in more urban locales (for

research on crime and policing in rural and small towns, see Weisheit and Wells 1999; Thurman

and McGarrell 1997; Weisheit et al. 1995).

Previous research has not enjoyed the luxury of having such a large sample of police

agencies in their analysis as is the case here. Consequently, the effect of changes in policing on
24

crime in small towns has largely been overlooked. Langworthy and Travis (1999) argue that

policing research has traditionally focused on large cities. In the present study, the same criteria

used by the UCR were followed such that cities were grouped into two categories: cities with

populations greater than 10,000 and those with populations less than 10,000 inhabitants. The

crime rate patterns for both of these groups are found in Table 2.

----- Table 2 About Here -----

It is evident that the drop in crime in the U.S. between 1994 and 2000 varies greatly by

city size. The pattern for violent crime rates among cities with populations greater than 10,000 

dropped substantially (-32.7%). In contrast, small cities (with populations less than 10,000) had

a much lower drop in their violent crime rate (-9.1%). A closer look at the crime rate in these

small cities reveals that the decline in violent crime primarily took place between 1994 and 1996.

These small cities actually experienced an increase in their violent crime rate from 1996 to 1998.

 The decline in property crime rates reveals a similar overall pattern.

Moreover, very small cities made up a disproportionate amount of the sample. There were

3,353 cities with population less than 10,000 and 2,306 cities with population greater than 10,000

in the COPS funded final sample. However, despite having a much larger number of cities, the

total population for the small cities included in the sample was 21,062,834 (both funded and nonfunded) and for large cities it was 133,001,630 (both funded and non-funded). Therefore, the

population of cities that received COPS funding with greater than 10,000 residents is more than

10 times that in the small cities. The skewed nature of the relationship between the number of

cities in the sample and the total population size makes the possibility of an interaction effect
25

between COPS funding and population size a possibility that should be examined further and

thus dictates the analysis by population size. It should however be noted that the effect of city

size itself is standardized and controlled for in the models through the examination of crime and

COPS funding rates.

Analysis of Cities with Populations Greater than 10,000. Results from the multivariate analysis

of violent and property crime rates are displayed in Tables 3 and 4, respectively. The findings

suggest that both hiring grants and innovative grants have had a significant effect on crime

reduction in this group, after controlling for previous crime rates (the 1994 crime rate),

demographic variables, and unobserved systematic variation. The R2 of the model predicting the

violent crime rate is 92.96% indicating that the independent and control variables are able to

explain a very high percentage of variance in the model; similarly, the R2

 for the property crime

rate model is 93.6%.19

----- Tables 3 and 4 About Here -----

Our analyses indicates that an increase of one dollar in grant funding spent for hiring

purposes resulted in a corresponding decline of 10.95 violent crime incidents per 100,000

residents. A dollar increase for hiring community police officers contributed to a decline of

27.88 property crime incidents per 100,000 population. Similarly, regarding innovative grant

programs the coefficients indicate that a one dollar increase in innovative grant funding

contributed to 4.30 fewer violent crime incidents per 100,000 population and 10.07 property

crime incidents per 100,000 population between 1995 and 2000. MORE grants did not have an

 

19 The R2

 for all the models increased noticeably from the previous analysis that was based on the data from 1994 to

1999. Also, it is important to note that the city and time dummy variables contributed significantly to the high level
26

effect on violent crime rates in these cities, but was related to a decrease in property crime rates

of 17.12 per 100,000 persons.20

The effects of demographic variables on violent and property crime rates in the two

models are similar. For example, the percentage of unemployment and percentage of male are

negatively associated with violent crime rates, while per capita income are positive correlated

with violent crime rates. Similar patterns of effects of demographic variables can be found in the

analysis of property crime rates. In addition, the magnitude of coefficients of demographic

variables in predicting property crime rate is much higher than in predicting violent crime rate

since the number of property crime incidents are higher than the number of violent crime

incidents in American cities. 

Analysis of Cities with Populations Less than 10,000. Tables 5 and 6 show the effects of

COPS grants on violent and property crime rates in very small cities. Unlike the results observed

in large cities, these findings do not show that the hiring grants are negatively correlated with

reported violent and property crime rates. The influence of hiring grants on both violent and

property crime rates were small and insignificant. In addition, the analysis shown in Tables 5

and 6 indicate that innovative and MORE grants are also not significant predictors of violent and

property crime rates in the sample. At the same time, demographic variables and dummy

variables (not reported) contributed to the explained variance of the models with 72.79% for

 

of R2 in these statistical models. 20 Two reviewers recommended the use of log transformation of the dependent variables because the dependent

variables, crime rates, might be skewed. We decided not to use log transformation for the following two reasons. 

First, in our analysis, the dependent variable is the crime rate, which is defined as the crime incidents known to the

police divided by city population, as a common practice, the dependent variables times 100,000, the crime rate. In

addition, the regression models are estimated by using the population variable as weights. The standardization of

crime rate adjusted the problem of a skewed dependent variable (the presumed reason for using a log
27

violent crime model and 84.46% for property crime model. Thus, the analyses for cities with

more than 10,000 population fit the models better than cities with population between 1,000 to

10,000.

----- Tables 5 and 6 About Here -----

Analysis of the Full Sample. The impact of COPS grants on violent crime for the full

sample appears in Table 7. Here we see the effects of COPS grants on the rate of violent crimes

reported to the police, using a two factor fixed effect panel model for the entire sample. The

results show that hiring and innovative grants are significant predictors of violent crime rates. 

The coefficient for hiring grants showed that a one dollar increase in hiring grant funding lead to

a decrease in 10.95 violent crime incidents. Similarly, the coefficient of innovative grants

indicates that a one dollar increase in innovative grant funding contributed to a decline of 3.78

violent crime incidents per 100,000 residents. Receiving MORE grant funding was not

significantly associated with a declining violent crime rate for the entire sample.

-----Tables 7 and 8 About Here -----

The effects of COPS grants on property crime rates for the full sample are shown in Table

8. All three categories of COPS are shown to significantly reduce property crime rates in the

analysis. Each dollar spent per person on hiring grants produces a reduction of 19 property

crimes per 100,000 residents. Similarly, both innovative (-15.76) and MORE grants (-9.85) were

significant predictors of property crime reduction in these cities. However, it is important to

point out that the significant effects found in cities over 10,000 population obviously influence

 

transformation). Second, since many smaller cities have zero crime incidents, the log transformation is not feasible.
28

the significance of the full model. For the control variables, the percentage of single parent

households does not have an appreciable influence on the property crime rate while the other

control variables prove statistically significant.

DISCUSSION AND CONCLUSION

We conclude that crime reduction in the U.S. due to COPS grants likely varies by city

size. The differences observed between cities with populations of less than 10,000 people and

larger cities is apparent with respect to crime patterns over the seven-year period of time when

national crime rates overall dropped substantially (Table 2). In addition, these data indicate that

COPS hiring, innovative and MORE grants contributed significantly to decreasing crime in U.S.

cities with populations greater than 10,000 people. According to the FBI, approximately 89

percent of people in the U.S. reside in jurisdictions with police departments that serve

populations greater than 10,000.21 Therefore, it appears that the significant crime reducing effect

of COPS grants are concentrated in areas affecting the majority of the U.S. population. It should

be noted that we do not feel that we are in a position to judge the relative size of this impact (in

terms of program effectiveness regarding crime rates). This is because the effect likely varies

across cities and it is difficult to assign a relative worth or value of a specific amount of crime

reduction. Because there are inherent limitations of social science research, because it is difficult

to determine how much a specific amount of crime prevention is worth to society, and because

other stated program outcomes (enhancing trust in police, reducing fear of crime, producing a

more technologically advanced and efficient police force, building community relationships etc.)

are not examined here, we leave discussion of this issue to others.
29

The findings are in line with our previous report examining a very similar statistical

model that did not include 2000 Census data and contained one less year of analysis (COPS

Grants 1994-1998 on reported crime from 1995-1999) (Zhao and Thurman 2001). The inclusion

of 2000 Census data allowing for time-varying demographic data, an additional year of analysis

and the focus on the use of city level dummy variables are the primary improvements and central

differences between the studies. In the previous study, county dummy variables were used

instead of city dummy variables because the use of city dummy variables would have wiped out

the effects of the other control variables, because only 1990 Census data was available. The lack

of variation in this Census data made it so that the contribution of these variables would be

eliminated if city dummy variables were used. However, when conducting this previous study, to

ensure the reliability of the results, these previous models were also run using city level dummy

variables and the substantive results were the same, however these results were not fully reported

as is the case in the present study. In addition, the present study includes both funded and nonfunded agencies at the request of external reviewers. We believe that this inclusion may result in

slightly biased estimates towards finding an effect for COPS Grants (at least in small cities where

non-funded agencies are of significant size) because virtually all of the non-funded agencies are

small and have experienced smaller declines in crime. To assure ourselves that this was not the

case, we ran the models reported here without the inclusion of non-funded agencies and these

results were substantively the same. Finally, at the request of external reviewers we also

included two additional control variables in the present study, percent male and per capita

income.

 21 This figure is also 89% in the sample.
30

Comparing the previous results from the December 2001 report to the results that are

reported in this report reveals very consistent findings. For example, in both studies, hiring and

innovative grants both had a significant negative effect on violent and property crime in cities

with populations greater than 10,000. In addition, in the previous study, MORE grants failed to

reach significance in either model, however, in this more recent study, MORE grants

demonstrated a significant negative effect on property crime rates in these cities.

This paper examined the effect that COPS Office grants awarded between 1994 and 1999

had on crime rates in 7,597 U.S. cities between 1995 and 2000 (both funded and non-funded

cities). It was found that cities with populations greater than 10,000 who receive greater amounts

of COPS Office innovative and hiring grant funding have experienced significantly greater

decreases in violent crime the following year than those who have received less funding, over the

seven-year period controlling for a wide range of time varying observed and unobserved city

level socio-demographic variables. In addition, this relationship holds for all three grant

programs with respect to property crime. As with all social science research, the extent to which

this means that COPS grants have contributed in a “causal” way to these reductions is an open

question. However, this study provides evidence that is in-line with accepted criminological

practice that attempts to satisfy the conditions for causal statements (addressing causal order,

spuriousness, and correlation) to the best extent practicable given the available data. In doing so,

it demonstrates that there is a significant relationship between COPS funding and property and

violent crime reductions in cities with populations greater than 10,000. This study should not be

viewed as definitive, but rather as an addition to the literature regarding the effectiveness of
31

COPS programs and understood and interpreted within that context.
32

Table 1: Descriptive Statistics of the Full Sample (The Mean from 1994 to 2000)*

____________________________________________________________________________

Variables Mean Standard Deviation

____________________________________________________________________________

Dependent variables (1995-2000)

Violent crime rate (per 100,000 population) 769.63 674.50

Property crime rate (per 100,000 population) 5016.39 2820.74

Independent variables (1994-1999)

Hiring grants (per resident) $2.65 3.76

Innovative grants (per resident) $0.49 2.56

MORE grants (per resident) $0.76 1.52

Control variables (1994-1999)

% of unemployment 4.75 2.11

% of minority 37.56 24.44

% of single parent households 11.89 4.34

% of young people 14.94 4.70

% of home owners 57.24 14.77

% of people in the same house 51.13 8.73

% male 48.21 2.19

per capita income $18.947a 8.85

Number of cities =5,659

____________________________________________________________________________

* The weighted average method is used to estimate the means of COPS grants and control

variables.

 a in thousands.
33

Table 2: City Size and Crime Rates per 100,000 Population (1994 to 2000)

_____________________________________________________________________________

94 95 96 97 98 99 2000 % Change

1994 to 2000

_____________________________________________________________________________

Violent Crime Rate

Cities between 1k to 10k 384 377 355 379 385 349 349 - 9.1%

Cities 10k and up 1027 971 902 856 786 710 691 -32.7%

Property Crime Rate

Cities between 1k to 10k 3651 3757 3742 3763 3635 3420 3323 -9.0%

Cities 10k and up 6032 5950 5706 5513 5134 4716 4545 -24.7%

____________________________________________________________________________
34

Table 3: The Effect COPS Grants (1994-1999) on Violent Crime Rates (1995-2000) in Cities

with Populations Greater than 10,000 (Both COPS Funded and Non-funded): Two Factor

Fixed Effect Panel Model (City Dummy Variables) a b

______________________________________________________________________________

Robust

Variables Coefficient Standard Error

______________________________________________________________________________

Hiring grants -10.95* 2.85

Innovative grants -4.30* 1.33

MORE grants -4.44 3.22

1994 Violent crime rate 0.42* 0.20

Percentage of minority -5.54 3.37

Percentage of unemployment -8.92* 3.90

Percentage of single parent households 32.28 17.68

Percentage of young people 15.63 8.68

Percentage of home owners -14.18* 7.20

Mobility 7.51 4.28

Percentage of male -57.46* 13.26

Per Capita Income 77.07* 14.71

Adjusted R2 92.96.

F Values= 73.56*

__________________________________________________________________________

* p <.05

a COPS dollar amounts are per person and crime rates are per 100,000. A total of 2,659 city

dummy variables and 6 time-variance dummy variables are not reported.

b

 The coefficients shown in the tables are unstandardized.
35

Table 4: The Effect of COPS Grants (1994-1999) on Property Crime Rates (1995-2000) in

Cities with Populations Greater than 10,000 (Both COPS Funded and Non-funded): Two

Factor Fixed Effect Panel Model (City Dummy Variables) a b

__________________________________________________________________________

Robust

Variables Coefficient Standard Error

__________________________________________________________________________

Hiring grants -27.88* 7.56

Innovative grants - 10.07* 4.90

MORE grants -17.12* 5.77

1994 Property crime rate 2.59* 0.56

Percentage of minority - 85.42* 11.62

Percentage of unemployment - 16.79 9.11

Percentage of single parent households 151.06* 68.32

Percentage of young people - 1.61 29.06

Percentage of home owners -134.66* 19.90

Mobility - 34.50* 10.34

Percentage of male -273.81* 52.73

Per Capita Income 178.31* 39.95

Adjusted R2 93.6

F Value= 81.4*

__________________________________________________________________________

* p <.05

a

 COPS dollar amounts are per person and crime rates are per 100,000. A total of 2,659 city

dummy variables and 6 time-variance dummy variables are not reported.

b

 The coefficients shown in the tables are unstandardized.
36

Table 5: The Effect of COPS Grants (1994-1999) on Violent Crime Rates (1995-2000) in

Cities with Populations Between 1,000 and 10,000 (Both COPS Funded and Non-funded):

Two Factor Fixed Effect Panel Model (City Dummy Variables) a b

__________________________________________________________________________

Robust

Variables Coefficient Standard Error

__________________________________________________________________________

Hiring grants - 0.39 0.40

Innovative grants 0.64 0.66

MORE grants 0.92 1.66

1994 Violent crime rate -1.68 1.01

Percentage of minority -9.26* 2.22

Percentage of unemployment - 2.41 1.81

Percentage of single parent households 10.65 6.53

Percentage of young people - 7.70* 3.25

Percentage of home owners - 6.75* 2.17

Mobility 3.97* 1.18

Percentage of male - 9.84 5.37

Per Capita Income - 4.66 3.15

Adjusted R2 .72.79

F Value= 14.31*

__________________________________________________________________________

* p <.05

a COPS dollar amounts are per person and crime rates are per 100,000. A total of 4,936 city

dummy variables and 6 time-variance dummy variables are not reported.

b

 The coefficients shown in the tables are unstandardized.
37

Table 6: The Effect of COPS Grants (1994-1999) on Property Crime Rates (1995-2000) in

Cities with Populations Between 1,000 and 10,000 (Both COPS Funded and Non-funded):

Two Factor Fixed Effect Panel Model (City Dummy Variables) a b

__________________________________________________________________________

Robust

Variables Coefficient Standard Error

__________________________________________________________________________

Hiring grants - 0.78 2.69

Innovative grants - 2.86 3.06

MORE grants 9.58 16.08

1994 Property crime rate 0.82* 0.30

Percentage of minority -89.51* 12.18

Percentage of unemployment 6.22 6.99

Percentage of single parent households 181.68* 32.01

Percentage of young people -124.61* 17.75

Percentage of home owners -123.80* 15.72

Mobility 23.58* 8.01

Percentage of male - 31.00 21.14

Per Capita Income - 77.78* 19.73

Adjusted R2 .84.46

F Value= 81.41*

__________________________________________________________________________

* p <.05

a

 COPS dollar amounts are per person and crime rates are per 100,000. A total of 4,936 city

dummy variables and 6 time-variance dummy variables are not reported.

b

 The coefficients shown in the tables are unstandardized.
38

Table 7: The Effect of COPS Grants (1994-1999) on Violent Crime Rates (1995-2000) in

Cities with Populations Greater than 1,000 (Both COPS Funded and Non-funded): Two

Factor Fixed Effect Panel Model (City Dummy Variables) a b

__________________________________________________________________________

Robust

Variables Coefficient Standard Error

__________________________________________________________________________

Hiring grants - 10.95* 2.19

Innovative grants - 3.78* 1.38

MORE grants - 3.89 3.70

1994 Violent crime rate - 2.04* 1.03

Percentage of minority - 13.02* 2.67

Percentage of unemployment - 7.67* 3.56

Percentage of single parent households 39.42* 15.40

Percentage of young people 9.03 7.31

Percentage of home owners - 13.92* 5.63

Mobility 7.07* 3.38

Percent Male - 27.56* 7.76

Per Capita Income 61.34* 12.95

Adjusted R2

= .92*

F Value= 58.55

__________________________________________________________________________

* p <.05

a

 COPS dollar amounts are per person and crime rates are per 100,000. A total of 7,596 city

dummy variables and 6 time-variance dummy variables are not reported.

b

 The coefficients shown in the tables are unstandardized.
39

Table 8: The Effect of COPS Grants (1994-1999) on Property Crime Rates (1995-2000) in

Cities with Populations Greater than 1,000 (Both COPS Funded and Non-funded): Two

Factor Fixed Effect Panel Model (City Dummy Variables) a b

__________________________________________________________________________

Robust

Variables Coefficient Standard Error

__________________________________________________________________________

Hiring grants - 19.01* 5.65

Innovative grants - 9.85* 4.10

MORE grants - 15.76* 6.51

1994 Property crime rate - 2.83* 1.04

Percentage of minority -107.74* 9.77

Percentage of unemployment - 12.62 8.19

Percentage of single parent households 184.68* 60.90

Percentage of young people - 34.15 23.93

Percentage of home owners -139.77* 15.98

Mobility - 15.77 8.12

Percent Male -124.57* 30.43

Per Capita Income 126.99* 34.41

Adjusted R2

=.93*

F Value=64.37

__________________________________________________________________________

*p <.05 

a

 COPS dollar amounts are per person and crime rates are per 100,000. A total of 7,596 city

dummy variables and 6 time-variance dummy variables are not reported.

b

 The coefficients shown in the tables are unstandardized.
40

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44

APPENDIX A: STATISTICAL MODEL SPECIFICATION

The two-way fixed effect model has the following form:

(1) it i t it it it it it y = μ + γ + α HIRE +α MORE +α INOV + β x + ε '

1 2 3

where yit is the number of crime incidents per 100,000 resident for city i at year t. μi is the fixed

effect for city i to be estimated. γt is the fixed effect for year t. HIREit, MOREit and INOVit are

the three COPS funding variables hiring, MORE and innovative grants, measured as per resident

in 1994 dollars, for city i at year t. αi (i=1,2,3) are the associated coefficients to be estimated. 

Their values, together with the estimated standard errors, will determine whether COPS grants

have measurable impact on crime reduction. xit is a set of control variables (e.g., the city level

unemployment rate) and β are the associated coefficients. Finally, εit is the error term.

MacKinnon and White (1985 p. 309) propose a modified heteroskedascity-consistent

covariance matrix estimator for the linear regression models on cross-sectional data. This 

estimator has better finite sample properties than what White suggested previously. Cao,

Stromsdorfer and Weeks (1996, p. 215) extend the MacKinnon and White estimator to panel

data, where as Cao et al note that there exists within-unit correlations in panel data, i.e.,

( ) ≠ 0, E it jt ε ε for i ≠ j .

Let Z be the design matrix containing all the right-hand-side variables in (1), Ti be the

number of repeated observations for I, N be the total number of observations across all i, and

(1 ) * ˆ

it

it

it k u − = ε , where kit is the ixtth diagonal element of the matrix Z Z′Z Z′ −1 ( , the panel data )

version of the MacKinnon and White estimator is
45

(2) 1 * * *' 1 ( 1/ ) ( ) [ (1/ )( )]( ) − − N − N Z′Z Z′Ω Z − N Z′u u Z Z′Z

where the k x mth of the * Ω is

(3) (( )( )) *

1

*

1 1

it

T

t

it itm

N

i

T

t

xitku x u i i

∑ ∑ ∑ = = =

Thus, (2) and (3) correct for not only the heteroskedasic errors across units, but also the

correlated errors within units over different time periods. It also allows for the number of

observations to be different by i. All our t-tests and F-tests in this report are based on the

corrected variance and co-variance estimates.
46

APPENDIX B: DIFFERENCING ISSUE

There is little difference between the model used in the current report and using the

differencing technique. In addition, using the differencing technique will lead to a loss of oneyear of observation in the analysis. The regression models that we estimated have the following

general form: it it it y = β′

x + ε . This specification is equivalent to the double-differencing

model: it it it ∆y = β′∆x + ∆ε , where both the dependent variable and the explanatory

variables are differenced. However, one observation per unit will be lost due to differencing.

Note that in the above model specifications, the coefficients associated with three COPS funding

variables are the same, although the models look different.

In addition, let’s also consider another model specification in which the dependent

variable is first-order differenced but not the explanatory variables. That is, the model looks like

this: it it it ∆y = β′

x + μ . Since the change in crime rate, it ∆y , can be positive, zero or

negative, and the funding variables are always non-negative, the interpretation of the coefficients

on these funding variables cannot be made.
47

APPENDIX C: ANALYSIS OF COPS FUNDED AGENCIES USING CITY DUMMY

VARIABLES AND EXCLUDING % OF MALE AND PER CAPITA INCOME

Table C1: The Effect COPS Grants (1994-1999) on Violent Crime Rates (1995-2000) in

Cities with Populations Greater than 10,000 (COPS Funded Cities Only): Two Factor

Fixed Effect Panel Model (City Dummy Variables) a b

______________________________________________________________________________

Robust

Variables Coefficient Standard Error

______________________________________________________________________________

Hiring grants -11.40* 3.10

Innovative grants -4.30* 1.41

MORE grants -4.21 3.41

1994 Violent crime rate 0.49* 0.16

Percentage of minority -16.27* 3.13

Percentage of unemployment -10.04* 4.17

Percentage of single parent households 49.58* 16.01

Percentage of young people -8.79 8.82

Percentage of home owners -0.48 8.12

Mobility 10.71* 4.61

Adjusted R2 .93

F Values=67.5*

__________________________________________________________________________

* p <.05

a COPS dollar amounts are per person and crime rates are per 100,000. 6 time-variance dummy

variables and 2,305 city dummy variables are not reported.

b

 The coefficients shown in the tables are unstandardized.
48

Table C2: The Effect of COPS Grants (1994-1999) on Property Crime Rates (1995-2000) in

Cities with Populations Greater than 10,000 (COPS Funded Cities Only): Two Factor

Fixed Effect Panel Model (City Dummy Variables) a b

__________________________________________________________________________

Robust

Variables Coefficient Standard Error

__________________________________________________________________________

Hiring grants -27.90* 7.88

Innovative grants -10.18* 4.94

MORE grants -16.58* 5.98

1994 Property crime rate -0.06 0.17

Percentage of minority -121.70* 11.65

Percentage of unemployment - 20.10* 9.65

Percentage of single parent households 297.87* 51.33

Percentage of young people -94.15* 29.36

Percentage of home owners -98.14* 21.83

Mobility -20.99* 11.34

Adjusted R2 .94

F Value=76.05*

__________________________________________________________________________

* p <.05

a

 COPS dollar amounts are per person and crime rates are per 100,000. 6 time-variance dummy

variables and 2,305 city dummy variables are not reported.

b

 The coefficients shown in the tables are unstandardized.
49

Table C3: The Effect of COPS Grants (1994-1999) on Violent Crime Rates (1995-2000) in

Cities with Populations Between 1,000 and 10,000 (COPS Funded Cities Only): Two Factor

Fixed Effect Panel Model (City Dummy Variables) a b

__________________________________________________________________________

Robust

Variables Coefficient Standard Error

__________________________________________________________________________

Hiring grants -.17 0.39

Innovative grants -0.44 0.65

MORE grants 0.28 1.71

1994 Violent crime rate -2.40* 1.14

Percentage of minority -10.00* 2.52

Percentage of unemployment -2.89 2.22

Percentage of single parent households 17.84* 7.82

Percentage of young people -12.85* 3.79

Percentage of home owners - 3.34 2.69

Mobility - 3.50* 1.48

Adjusted R2 .77

F Value= 16.9*

__________________________________________________________________________

* p <.05

a COPS dollar amounts are per person and crime rates are per 100,000. 6 time-variance dummy

variables and 3,352 city dummy variables are not reported.

b

 The coefficients shown in the tables are unstandardized.
50

Table C4: The Effect of COPS Grants (1994-1999) on Property Crime Rates (1995-2000) in

Cities with Populations Between 1,000 and 10,000 (COPS Funded Cities Only): Two Factor

Fixed Effect Panel Model (City Dummy Variables) a b

__________________________________________________________________________

Robust

Variables Coefficient Standard Error

__________________________________________________________________________

Hiring grants 1.30 2.39

Innovative grants -1.04 3.03

MORE grants 7.35 16.93

1994 Property crime rate 0.70* 0.06

Percentage of minority -93.21* 15.37

Percentage of unemployment 9.24 8.33

Percentage of single parent households 207.40* 38.81

Percentage of young people -132.20* 20.53

Percentage of home owners -76.55* 14.91

Mobility 2.89 8.01

Adjusted R2 .87

F Value= 32.60*

__________________________________________________________________________

* p <.05

a

 COPS dollar amounts are per person and crime rates are per 100,000. 6 time-variance dummy

variables and 3,352 city dummy variables are not reported.

b

 The coefficients shown in the tables are unstandardized.
51

Table C5: The Effect of COPS Grants (1994-1999) on Violent Crime Rates (1995-2000) in

Cities with Populations Greater than 1,000(COPS Funded Cities Only): Two Factor Fixed

Effect Panel Model (City Dummy Variables) a b

__________________________________________________________________________

Robust

Variables Coefficient Standard Error

__________________________________________________________________________

Hiring grants -7.64* 2.38

Innovative grants -3.64* 1.46

MORE grants -3.60 3.81

1994 Violent crime rate - 2.43* 1.20

Percentage of minority - 21.53* 2.57

Percentage of unemployment -8.70* 3.90

Percentage of single parent households 55.35* 13.36

Percentage of young people -7.52 7.85

Percentage of home owners -3.12 6.67

Mobility 8.11* 3.89

Adjusted R2 .92

F Value= 61.1*

__________________________________________________________________________

* p <.05

a

 COPS dollar amounts are per person and crime rates are per 100,000. 6 time-variance dummy

variables and 5,658 city dummy variables are not reported.

b

 The coefficients shown in the tables are unstandardized.
52

Table C6: The Effect of COPS Grants (1994-1999) on Property Crime Rates (1995-2000) in

Cities with Populations Greater than 1,000(COPS Funded Cities Only): Two Factor Fixed

Effect Panel Model (City Dummy Variables) a b

__________________________________________________________________________

Robust

Variables Coefficient Standard Error

__________________________________________________________________________

Hiring grants -18.32* 5.90

Innovative grants -9.41* 4.15

MORE grants -14.84* 6.49

1994 Property crime rate 0.70* 0.06

Percentage of minority -135.60* 9.84

Percentage of unemployment -16.17 8.84

Percentage of single parent households 321.93* 42.93

Percentage of young people -88.98* 25.46

Percentage of home owners -102.60* 18.04

Mobility - 13.71 9.47

Adjusted R2 .94

F Value=84.7*

__________________________________________________________________________

*p <.05 

a

 COPS dollar amounts are per person and crime rates are per 100,000. 6 time-variance dummy

variables and 5,658 city dummy variables are not reported.

b

 The coefficients shown in the tables are unstandardized.
53

APPENDIX D: ANALYSIS OF COPS FUNDED AGENCIES USING COUNTY DUMMY

VARIABLES AND EXCLUDING % OF MALE AND PER CAPITA INCOME

Table D1: The Effect COPS Grants (1994-1999) on Violent Crime Rates (1995-2000) in

Cities with Populations Greater than 10,000 (COPS Funded Cities Only): Two Factor

Fixed Effect Panel Model (County Dummy Variables)a b

______________________________________________________________________________

Robust

Variables Coefficient Standard Error

______________________________________________________________________________

Hiring grants -5.49* 2.10

Innovative grants -5.31* 1.67

MORE grants -2.00 3.24

1994 Violent crime rate 0.59* 0.02

Percentage of minority 1.94* 0.45

Percentage of unemployment 5.14 3.80

Percentage of single parent households 11.76* 2.44

Percentage of young people -1.80 0.73

Percentage of home owners -4.75* 0.39

Mobility 1.94* 0.45

Adjusted R2 .89

F Values=114.6*

__________________________________________________________________________

* p <.05

a COPS dollar amounts are per person and crime rates are per 100,000. A total of 970 county

dummy variables and 6 time-variance dummy variables are not reported.

b

 The coefficients shown in the tables are unstandardized.
54

Table D2: The Effect of COPS Grants (1994-1999) on Property Crime Rates (1995-2000) in

Cities with Populations Greater than 10,000(COPS Funded Cities Only): Two Factor Fixed

Effect Panel Model (County Dummy Variables) a b

__________________________________________________________________________

Robust

Variables Coefficient Standard Error

__________________________________________________________________________

Hiring grants -25.22* 6.01

Innovative grants -20.65* 7.24

MORE grants -21.47* 8.08

1994 Property crime rate 0.44* 0.04

Percentage of minority 11.34* 2.00

Percentage of unemployment 32.93* 11.14

Percentage of single parent households 18.52* 7.18

Percentage of young people -12.02* 3.96

Percentage of home owners -40.91* 4.26

Mobility 22.81* 3.78

Adjusted R2 .87

F Value=87.82*

__________________________________________________________________________

* p <.05

a

 COPS dollar amounts are per person and crime rates are per 100,000. A total of 970 county

dummy variables and 6 time-variance dummy variables are not reported.

b

 The coefficients shown in the tables are unstandardized.
55

Table D3: The Effect of COPS Grants (1994-1999) on Violent Crime Rates (1995-2000) in

Cities with Populations Between 1,000 and 10,000 (COPS Funded Cities Only): Two Factor

Fixed Effect Panel Model (County Dummy Variables) a b

__________________________________________________________________________

Robust

Variables Coefficient Standard Error

__________________________________________________________________________

Hiring grants 1.47* 0.42

Innovative grants 0.60 0.85

MORE grants 2.92 1.46

1994 Violent crime rate 0.43* 0.02

Percentage of minority 1.57 0.42

Percentage of unemployment - 0.73 2.25

Percentage of single parent households 13.55* 1.51

Percentage of young people - 1.30* 0.55

Percentage of home owners - 2.41* 0.33

Mobility - 0.15 0.53

Adjusted R2 .68

F Value= 23.10*

__________________________________________________________________________

* p <.05

a COPS dollar amounts are per person and crime rates are per 100,000. A total of 1,532 county

dummy variables and 6 time-variance dummy variables are not reported.

b

 The coefficients shown in the tables are unstandardized.
56

Table D4: The Effect of COPS Grants (1994-1999) on Property Crime Rates (1995-2000) in

Cities with Populations Between 1,000 and 10,000 (COPS Funded Cities Only): Two Factor

Fixed Effect Panel Model (County Dummy Variables) a b

__________________________________________________________________________

Robust

Variables Coefficient Standard Error

__________________________________________________________________________

Hiring grants 7.91* 2.58

Innovative grants 1.30 3.57

MORE grants 30.51* 15.24

1994 Property crime rate 0.70* 0.02

Percentage of minority 8.00* 2.15

Percentage of unemployment 3.51 9.26

Percentage of single parent households 12.69 7.87

Percentage of young people -20.18* 3.12

Percentage of home owners -19.69* 2.44

Mobility - 3.55 2.26

Adjusted R2 .79

F Value= 41.38*

__________________________________________________________________________

* p <.05

a

 COPS dollar amounts are per person and crime rates are per 100,000. A total of 1,532 county

dummy variables and 6 time-variance dummy variables are not reported.

b

 The coefficients shown in the tables are unstandardized.
57

Table D5: The Effect of COPS Grants (1994-1999) on Violent Crime Rates (1995-2000) in

Cities with Populations Greater than 1,000(COPS Funded Cities Only): Two Factor Fixed

Effect Panel Model (County Dummy Variables) a b

__________________________________________________________________________

Robust

Variables Coefficient Standard Error

__________________________________________________________________________

Hiring grants -2.51 1.43

Innovative grants -4.20* 1.74

MORE grants -1.37 3.46

1994 Violent crime rate 0.57* 0.02

Percentage of minority 2.13* 0.39

Percentage of unemployment 6.06 3.65

Percentage of single parent households 11.50* 2.06

Percentage of young people -1.82* 0.56

Percentage of home owners -4.41* 0.32

Mobility 3.63* 0.55

Adjusted R2 .88

F Value= 116.16*

__________________________________________________________________________

* p <.05

a

 COPS dollar amounts are per person and crime rates are per 100,000. A total of 1,884 county

dummy variables and 6 time-variance dummy variables are not reported.

b

 The coefficients shown in the tables are unstandardized.
58

Table D6: The Effect of COPS Grants (1994-1999) on Property Crime Rates (1995-2000) in

Cities with Populations Greater than 1,000(COPS Funded Cities Only): Two Factor Fixed

Effect Panel Model (County Dummy Variables)a b

__________________________________________________________________________

Robust

Variables Coefficient Standard Error

__________________________________________________________________________

Hiring grants -14.67* 4.46

Innovative grants -19.30* 7.54

MORE grants -19.01* 9.03

1994 Property crime rate 0.50* 0.04

Percentage of minority 11.22* 1.89

Percentage of unemployment 32.00* 10.44

Percentage of single parent households 9.49 6.16

Percentage of young people -16.73* 3.00

Percentage of home owners -35.10* 3.67

Mobility 12.31* 2.82

Adjusted R2 .84

F Value=88.72*

__________________________________________________________________________

*p <.05 

a

 COPS dollar amounts are per person and crime rates are per 100,000. A total of 1,884 county

dummy variables and 6 time-variance dummy variables are not reported.

b

 The coefficients shown in the tables are unstandardized.
59

APPENDIX E. THE POSSIBLE INFLUENCE OF THE EFFECT OF POLICE

EXPENDITURES ON THE EFFECT OF COPS GRANTS ON

CRIME RATES FROM 1994-2000

One possibly important omitted variable in this analysis is an indicator of police expenditures

minus COPS funding. Other studies (the Heritage Foundation) attempted to control for this

variable by including budgetary figures for law enforcement expenditures at the county level. 

This is inaccurate for two reasons. First, it does not account for agencies in a county that did not

report crime data (the dependent variable) for all of the study years in question. Secondly, it does

not explicitly exclude COPS funding from these estimates. This is important to avoid issues of

multicollinearity and to ensure relative independence between the independent variable of

interest and the control variables. However, because there may be some negative relationship

between police expenditure and crime rate in cities and their willingness to pursue COPS funding

it is an important variable to consider. Since it is impossible to collect information on police

expenditures (minus COPS funding) in the over 7,500 agencies examined between 1994 and

1999, this analysis used 1994 crime rates to measure the level of crime rate in a city prior to the

inception of COPS grants, thus standardizing for budgetary funding at this point in time. After

1994, minor increases (or decreases) in budgetary funding may have occurred in individual

agencies, but it is very unlikely that these changes could account for a large amount of the

variation in crime rates in the cities in question. In addition, because of the panel nature of this

design, this study was able to include city dummy variables to account for such unobserved

factors as police expenditures.

To address this issue further, in the spring of 2002, we conducted a survey of the 70 largest

municipal police agencies in the country. The purpose of the survey was to collect the annual

budgetary expenditure information of these law enforcement departments (minus COPS funding)

from 1990 to 2000. 55 police agencies completed and returned the survey.

With the information on police annual expenditure in these 55 largest departments, we are able to

test the effect of police expenditure on crime rates, controlling for COPS hiring, innovative, and

MORE grants. The same dependent, independent, and controlled variables are used to estimate

the relationship among police expenditure, COPS grants, and crime rates between 1994 and

2000. Our results indicate that COPS grants are independent of the departmental annual

expenditure. That is, the magnitudes of coefficients of COPS grants remain relatively stable with

or without police annual expenditure variable. 

COPS funding variables are relatively independent of police expenditure and city demographics

(comparison between city dummy results and county dummy results). We make this case

because it is reasonable to postulate that the funding decisions made by COPS Office were free

from considering police expenditure in a particular department between 1994 and 1999. There is

no evidence to suggest that COPS officials made funding decision based on the increase or

decrease of annual expenditure in a municipal police agency, and the vast majority of agencies
60

applied for and received funding indicating that the agencies themselves did not consider this

variable fully in their decision making process. As long as the coefficients of COPS grants are,

to a large degree, independent, the estimates are generally unbiased.