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This is to certify that the
thesis entitled

FEDERAL INCENTIVES TO ADDRESS GUN VIOLENCE:
A MODEL OF SUCCESS AND FAILURE

presented by

Carol Anne Zimmermann

has been accepted towards fulfillment
of the requirements for the

Ph.D. degree in Criminal Justice 3

 

 

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MSU is an Affirmative Action/Equal Opportunity Institution

 

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MAY BE RECALLED with earlier due date if requested.

 

DATE DUE DATE DUE DATE DUE

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

2/05 p:lCIRC/Date0ue.indd-p.1

FEDERAL INCENTIVES TO ADDRESS GUN VIOLENCE:
A MODEL OF SUCCESS AND FAILURE

By

Carol Anne Zimmermann

A DISSERTATION

Submitted to
Michigan State University
In partial fulﬁllment of the requirements for the degree of

DOCTOR OF PHILOSOPHY
School of Criminal Justice

2006

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ABSTRACT

FEDERAL INCENTIVES TO ADDRESS GUN VIOLENCE:
A MODEL OF SUCCESS AND FAILURE

By
Carol Anne Zimmermann

The federal government invests billions of dollars to aid state and local
jurisdictions in importing policies, but such efforts often are unsuccessful. This analysis
is designed to provide insight into conditions that enhance or inhibit policy adoption. An
example of such a federally incentivized policy is Project Safe Neighborhoods (PSN),
which grants funds to the ninety-four federal judicial districts to address gun violence.
This study investigates the competing external factors in PSN districts that are associated
with the degree to which jurisdictions adopt the policy. Few studies have been
undertaken to determine why federally-incentivized policy adoption succeeds or fails, but
based on limited results and informed by organizational, economic, and risk theories, a
model is proposed based on the premise that the less risky and the more beneﬁcial a
policy appears to be, the greater the level of policy implementation. It is argued that the
more the policy is congruent with the needs, infrastructure, attitudes, and provides needed
resources to the jurisdiction, the less risky the policy will appear and the greater the
likelihood of adoption. Support was found for the external factors model, especially for

factors related to the structure and infrastructure of the district.

 

Copyright by
Carol Anne
Zimmermann
2006

To George, the best thing that ever happened to me.

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ACKNOWLEDGEMENTS

I wish to express my heartfelt appreciation for the scholarship and the guidance of the
wonderful committee members who smoothed my path and challenged my ideas
throughout my graduate work at Michigan State University. To Dr. Edmund McGarrell,
Dr. Christina DeJong-Schwitzer, Dr. Steven Dow, and Dr. William Jacoby, I will
remember and will reﬂect your legacy of intellectual vigor, personal ethics, dignity, and
good humor in my academic career and my life. I especially want to thank my husband
George, who gives me the support and the courage to go bounding off in pursuit of my
dreams and my son Brian, a truly good soul, who reminds me why investment in the
teaching of our children is important and joyﬁil. I want to thank my parents who never
had the luxury of a college education but fought ﬁercely to give that gift to me. Thank
you to my faithful companions Pumpkin and Boots, reﬂections of unconditional love. So
too my thanks go to the many friends, colleagues, and students who encouraged me to

follow my passion. Finally, I thank God for taking me down this path, a teacher who

treats me with more kindness, mercy, and gentleness than I could ever deserve.

 

 

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TABLE OF CONTENTS

LIST OF TABLES ............................................................................. ix
LIST OF FIGURES ............................................................................ x
CHAPTER ONE: INTRODUCTION ...................................................... 1
The Promise of F ederally-Incentivized Criminal Justice Policy ....................... 1
The Rise and Failure of Incentivized Policy Aimed at Collaborative Policies ...... 3
The Normative Choice ...................................................................... 8
Building a Model Based on Theory and Research ...................................... 9
Project Safe Neighborhoods: A Natural Laboratory ................................... I 1
Overview .................................................................................... 11

CHAPTER TWO: SHOULD F EDERALLY-INCENTIVIZED

POLICY ADOPTION SUCCEED? ........................... . .......................... 13
Rational Choice and the One Best Way ................................................. 13
Wicked Problems and the Search for Legitimacy ...................................... 16
Policy Adoption: Mythical or Measurable Outcomes ................................. l9
Mythical Outcomes ........................................................................ 21
Beneﬁts to Criminal Justice Agencies of Myths and Coercive Mimetic

Isomorphism ........................................................................... 22
Deﬁnitions of Success ..................................................................... 23
Contagion and Diffusion: A Different Beneﬁt ......................................... 26
Enacted versus Crescive Change ......................................................... 27

CHAPTER THREE: THEORY AND FINDINGS ON F EDERALLY

INCENTIVIZED POLICY ADOPTION ................................................. 28
Theories and Patterns of Policy Adoption ...... ' ......................................... 29
Factors of Production, Protocols, Structures, and Infrastructure ..................... 33
Goals and Policy Adoption ............................................................... 41
Whose Goal is to be Served? ............................................................. 44
Civic Capacity .............................................................................. 52
Goals and Formal Rules .................................................................. 53
Leadership, the Holy Grail ............................................................... 54

CHAPTER FOUR: THROUGH THE LENS OF RISK ...................... . ..... 57
Risk: The Perceived Need and the Expected Return .................................. 58
Limiting Risk ............................................................................... 6O
Hierarchical, Closed-System, Multi-agency Adoption ............................... 62
Matrix or Process Structures ............................................................. 63
Goal and Governance Congruence and Partnership Competency .................. 63
Networks and Information F low. ......................................................... 65
Risk and Investment ........................................................................ 65
Dealing with Interoperability Problems ................................................ 66
The Bowtie Model .............................. . ......................................... 67

vi

Recognition-Based Models .............................................................. 70

The Symbolic Group ..................................................................... 71
Risk and Policy Adoption ................................................................. 72
CHAPTER FIVE: A MODEL OF INCENTIVIZED POLICY ADOPTION. 76
Framing the Factors ........................................................................ 76
The Quinn Model ......................... .- .' ............................................... 77
The External Level of the Model ........................................................ 80
Within Agency Competing Factors ..................................................... 82
Competing Factors in the Task Force .................................................. 83
Testing and Utilizing the Model ......................................................... 84
CHAPTER SIX: RESEARCH CONTEXT, DATA, METHODS, AND
ANALYTICAL STRATEGIES ........................................................... 86
The PSN Initiative ........................................................................ 86
The Rewards and Challenges for Research Design .................................. 92
Research Design .......................................................................... 96
Beyond Regression Analysis ............................................................ 97
The Model and the Hypothesis .......................................................... 98
Sources of Data .......................................................................... 105
Assumptions and Limitations .......................................................... 107
Summary and Introduction to Variable Construction ............................... 110

CHAPTER SEVEN: VARIABLE CONSTRUCTION AND DESCIPTIVES.. 111

Policy Adoption Elements and Measures ............................................ 111
Describing the Policy Adoption Elements and Overall Score ...................... 115
Construction and Description of Competing External Factors ..................... 116
Summary ................................................................................... 126
CHAPTER EIGHT: ANALYSIS AND FINDINGS .................................. 127
Hypothesis 1. The external competing factors model will predict
the level of policy adoption ........................................................ 127
Mapping Competing External Factors ................................................. 133
Summary of Findings for the Broad Test of the Model ............................. 135
Subgroup Analysis: High and Low Technology Groups ............................ 136
Comparison of the High and Low Information Technology Subgroups .......... 142
Further Investigation of the Effect of Population Density .......................... 143
Summary for Hypothesis One ......................................................... I46
Hypothesis 2. The external competing factors model will differentially
affect the elements of policy adoption ........................................... 147
Summary of the Findings for Hypothesis Two ...................................... 155

Hypothesis 3. Patterns of policy adoption-will be found that reﬂect

symbolic, recognition-based decision-making groups, and

strategic groups ...................................................................... 157
Summary .................................................................................. 165

vii

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CHAPTER NINE: CONCLUSIONS ................................................... 168

Is PSN Being Adopted? ................................................................. 168
A Culture of Vertical and Horizontal Competencies and Costs ................... 168
Resources and Cost-Shifting ........................................................... 176
The Need for PSN ........................................................................ 17 7
Policy Implications ....................................................................... 178
Questions Related to Other Levels of the Model .................................... 179
Limitations ................................................................................ 1 80
The Quinn Heuristic ..................................................................... 181
In Conclusion ............................................................................ 181
APPENDICES .............................................................................. 182
APPENDIX A. RESEARCH PARTNER SURVEY INSTRUMENT .............. 183
APPENDIX B. PROJECT SAFE NEIGHBORHOODS SEMI-ANNUAL
REPORT TO THE ATTORNEY GENERAL ................................... 187
APPENDIX C. DIAGNOSTICS .................................................... 188
REFERENCES ............................................................................. 192

viii

 

Table

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Table

Table 1.

Table 2.

Table 3.

Table 4.
Table 5.
Table 6.
Table 7.

Table 8.

Table 9.

Table 10.

Table 1 1.

Table 12.

Table 13.

Table 14.

Table 15.

LIST OF TABLES
Measure of Central Tendency and Dispersion
for Dependent Variables ......................................................... 116

Measure of Central Tendency and Dispersion
for Scaled Independent Variables ............................................... 117

Frequencies for Independent Variables for Similar Program
Implementation .................................................................... 1 I9

Bivariate Analyses of Policy Adoption and the Independent Variables 128
Policy Adoption Regressed on the External Competing Factors Model .. 130
Lower Bound of Quartiles for Independent Variables ...................... 133
Descriptives of High and Low Information Technology Subgroups ...... 137

Subgroup Analysis: High Information Technology Group with Adoption

Regressed on the Simpliﬁed External Factors Model ....................... 139
Subgroup Analysis: Low Information Technology Group with

Adoption Regressed on the Simpliﬁed External Factors Model .......... 141
Correlation Matrix for Policy Adoption Elements ........................... 148
Comparison of StandardizedCoefﬁcients (Beta) for Regression

Analyses of Policy Adoption and the Three Adoption Elements .......... 149
Collaborative Implementation Regressed on the External Competing
Factors Model .................................................................... 150
Research Integration Regressed on the External Competing

Factors Model ..................................................................... 152
Enhanced Federal Prosecution Regressed on the External Competing
Factors Model ..................................................................... 153
Numbers of Districts in Each of the Pattern Categories ................... 159

ix

 

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Figure 6.
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Figure 8.

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Figure 11.

Figure 12.

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Figure 14.
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Figure 16.

Figure 17.

Figure 18.

Figure 19.

LIST OF FIGURES

Simple Hierarchical Collaboration ................................................ 62
Matrix and Process Structures ..................................................... 63
Example of Goa] Foci Incongruence ............................................. 64
Fan Model — Based on the Bowtie Model (Csete & Doyle, 2004) .......... 69
Example of a Recognition-Based Group ........................................ 70
Symbolic Group ..................................................................... 72
Risk Decision Factors .............................................................. 73
Risk Decisions for Group Participation .................................. . ....... 74
Competing Values Model (Quinn, 1991) ....................................... 77
Chaotic Adaptive Organization ................................................... 78
Model of Implementation Based on Task Force Dynamics ................... 79

Illustration of External Competing Factors in Deinstitutionalization

Of Status Offenders ................................................................ 81

Within-Agency Competing Factors ............................................. 82

The External Competing Factors Model ........................................ 98

PSN National Research Team Data Quality Rating Scale ................... 107

Diagram of Policy Adoption and Policy Adoption Elements ............... 111

External Competing Factor Maps Comparing the Independent

Variable Means of the Highest and Lowest Quartiles of Adopters ......... 134

Comparison of the External Competing Factors Model

for Low and High Information Technology Subgroups ..................... 142

Competing Factors Maps of Categories of Policy Adoption

by Group Type .................... 161
x

 

Figure 20. Competing Factors Maps of the Recognition-Based Group
And Each Group Member ....................................................... 167

xi

 

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CHAPTER ONE: INTRODUCTION

The Promise of Federally-Incentivized Criminal Justice Policy

Although there is high concern among politicians, professionals, and the

general public with the outcomes of correctional policies, there has been

little systematic study of the factors that affect policy adoption.

(McGarrell & Duffee, 1995, p. 1)

Why do some jurisdictions fully implement criminal justice policies that are
promoted through federal grants, while in other jurisdictions such strategies are ignored,
rejected, symbolically implemented, or raised to the level of discussion but not
meaningful action? The question is of import because the political and professional
culture of justice and corrections promotes the paradigm of “policy uptake” or “policy
grafting” and because billions of federal dollars are spent incentivizing the adoption of
such policies (McGarrell & Duffee, 1995, p. 59). The federal government routinely
provides ﬁscal incentives aimed at promoting national adoption of policies that are
purported, assumed, or hoped to reﬂect the “best practices” available to the justice and
corrections sector (GAO, 2003). The same process, to a lesser extent, exists at the state
level with state governments attempting to inﬂuence local jurisdictions. Given the
complex nature of crime and justice, federal policy incentives generally require
collaboration among agencies and among levels of government. These grant-funded
strategies may suggest or require the participation of community representatives,
nonproﬁt and for-proﬁt entities, along with the participation of public entities.

Examples of federally-incentivized criminal justice policies include the

deinstitutionalization of status offenders in the 19605, reintegration centers for adult

offenders under the Safe Streets initiative in the 19705, and current efforts to focus state

 

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and local jurisdictions on offender reentry from secure conﬁnement and on the reduction
of gun violence (McGarrell & Duffee, 1995; OIP, 2005a; Schwartz, 1989). The
magnitude of the efforts and the ﬁnding that has cascaded to states and local
governments in order to foster uniﬁed national criminal justice policies has been
substantial, but the actual implementation and measurable impact of such efforts have
been much less impressive (Cochran, 1992; GAO, 2003; McGarrell & Duffee, 1995).

Despite the failure of many federal incentives to equip and entice local
jurisdictions into full and successful adoption of criminal justice policies, the efforts and
the funding continue. It is argued here that an integration of various theoretical
approaches can provide an explanation for the popularity of this paradigm of replication,
despite the lack of results indicating that such strategies are cost beneﬁcial or effective. It
also is argued here that the integration of various theories can provide a model to explain
how the perception of the risk associated with the adoption of a policy inﬂuences the
willingness and ability of implementing agencies to engage in and invest in the activities
required for successful implementation. It is further argued that until and unless we can
understand the factors that promote or inhibit incentivized policy adoption by the
collaborative groups tasked to such implementation, investments of federal and local
resources will be wasted.

In terms of policy implications, a better understanding of competing factors in
policy adoption would aid federal decision-makers in targeting funding incentives toward
those jurisdictions that are primed for adoption. Funding directed to jurisdictions that are

poor candidates for the proposed change could be linked to programs designed to

 

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ameliorate those factors that limit the jurisdiction’s readiness to adopt the proposed

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The Rise and Failure of Incentivized Policy Aimed at Collaborative Policies

The federal govemment’s strategy of employing grants-in-aid for the promotion
of federally-endorsed social policy began in earnest in the 19305 and funding of these
strategies expanded through the 19705 (Salamon, 1983). Grants-in-aid are federal funds
to “state and local action that advances a federal objective” (Salamon, 1983). The
impetus for such federal grants in the 19305 was the poverty of the Great Depression and
the intent of decision-makers at the federal level to equip state and local governments
with programs and resources to provide jobs and job training for the unemployed
(Anderson, 1975). Conditions associated with poverty and unemployment in the 19605
and during the Kennedy administration and continuing with the Great Society programs
of the Johnson administration led federal agencies to provide grants to address a variety
of societal problems. New agencies, including the Law Enforcement Assistance
Administration (LEAA) and the Office of Juvenile Justice and Delinquency Prevention
(OJJDP), were created to oversee newly initiated federal grants-in-aid to incentivize
creation of programs to address issues associated with delinquency, crime, and public
safety. Generally focused on the amelioration of social conditions theorized to inﬂuence
delinquency and crime, these grants-in-aid spanned across levels of government and
across local and state agencies and policy subsystems.1 Federally incentivized programs

continued through successive administrations, though the focus of those programs shifted

 

’ An example of policy adoption that crosses policy subsystems is juvenile justice reform instituted during
the Kennedy administration that attempted to integrate educational, judicial, and correctional subsystems
(Marris & Rein 1970). .

 

 

 

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over time. Deinstitutionalization and de—carceration were incentivized in the 19605,
while incentives to build more and more secure correctional facilities were incentivized
in the 19905 (Torbet et al., 1996).

In the 19605 and early 19705, few theories and models on policy adoption had
been proposed (Lester, Bowman, Goggin, & O'Toole Jr., 1987). Two conjoined factors,
that became routine aspects of federal incentivized programs, proved to confound
theorists, researchers, and the implementers themselves. These factors were the oft-
mandated requirements that programs be planned to meet the needs of the local
jurisdiction, and the requirement that planning and implementation processes engage a
wide range of governmental agencies as well as stakeholders outside the formal structure
of government. As the prevalence and visibility of these federally-funded policy
adoption efforts increased, the interest of social scientists in investigating the mechanisms
and the outcomes of such implementation likewise increased. Planning processes for this
vision of collaborative implementation began to developed in the 19705, and some
implementation evaluations were conducted (MacIver, 1970; Marris & Rein, 1970).

An example of such endeavors in the early 19705 is a planning model proposed by
MacIver (1970), to provide insight into the processes he proposed as key to successﬁil
implementation of these federally incentivized social programs. He envisioned a tree that
branched from one central trunk in which that trunk was a clear deﬁnition of the problem.
From the trunk, theories of causation branched off. Potential interventions sprouted off
each theory, and the target populations affected by the interventions were growing from
those intervention branches. Finally, value considerations would make up the highest

limbs of the tree. The implementation group would then pick the path up the tree that

 

 

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best suited their particular situation and implement that policy. Federal policy frames
envisioned that grants-in-aid, since they delineated both the goals of the policy and
provided ﬁnding to at least seed the effort, would greatly clarify the path groups should
follow up the planning tree. Not only was the government supplying funds for planning,
it also was endorsing and incentivizing speciﬁc policy objectives. For criminal justice
issues, federal support for local planning to implement federally- approved policies was
prophesized to create a level of state and local COOperation and capacity that would, in the
future, ensure implementation success.

For the most part, the staffs of these units are too small, their resources too

modest and their origins too recent to permit substantial progress toward

complete models of comprehensive plans. However, as they build strength

and get their bearings, these specialized state and local criminal justice

agencies should be able to demonstrate increasing effectiveness and

themselves assume major roles in providing technical assistance for
coordinated criminal justice planning.

(Skoler, 1970, p. 460)

This vision of coordinated criminal justice planning, spurred by federal support
was not to come to fruition. For while the planning and implementation models proposed
in the early 19705 identiﬁed many of the factors that would be included in later models
and theories, such models failed to address the enormous problems that ensued when
policy groups attempted to clearly deﬁne even the ﬁrst step of the planning process.
Deﬁning the problem — let alone the planning morass associated with choosing what to
implement based on value considerations was to stymie the action of many collaborative
groups. As social scientists began to investigate implementation success or failure, they
found the path to implementation success peppered with factors that often caused

programs to be implemented in ways not envisioned by those crafting the federal policy —

 

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or not implemented at all. An evaluation of federally incentivized juvenile justice reform
efforts targeting large cities in the 19605 produced results indicating such efforts resulted
in no meaningful program implementation in areas where youth were at highest risk for
delinquency and that “the strategy succeeded most where it was needed least” (Mam's &
Rein, 1970, p. 471). The study indicated that racial divisions in Chicago resulted in the
inability to deﬁne the mission of juvenile justice reform. In Cleveland, social scientists
could not provide a single, agreed-to causal theory for delinquency and the “absolute”
answers that groups wanted before picking an intervention. That, coupled with the
political differences and power struggles that arose between the Cleveland mayor, citizen
groups, and the school board, a consensus choice on the intervention and the group to be
served became impossible. Reform was implemented in Los Angeles, but the researchers
termed it “bland” (Marris & Rein, 1970, p. 479). The goals and values within this
racially heterogeneous city thwarted consensus decision-making. An uneasy compromise
resulted in the implementation of a juvenile education and employment program targeted
—— not at non-white or black youth — the population most at risk, but only at Hispanic
adolescents. The researchers note the lost potential of the program, and the failure of
meaningful implementation, citing the 1965 riots in Los Angeles as proof of the unmet
needs of black teens.

A project boardroom is no magic circle, where long-standing conﬂicts

dissolve under the spell of planning. On the contrary, it introduces new

hopes and fears, new ambiguities, which can harden resistance and

complicate intrigue, embittering old rivalries.

(Marris & Rein, 1970, p. 479)

Researchers and scholars recognized the need for both a well-articulated theory

for policy adoption and for a model delineating the factors theorized to promote success.

 

 

 

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By the mid-19705 and through the 19805, theories and multiple models of policy adoption
were developed (Lester et al., 198 7). Top-down, bottom-up, adaptive, and evolution
models were proposed (Sabatier, 1986; Sabatier & Mazmanian, 1980; Van Meter & Van
Horn, 1975). Some theorists began to explore the importance of the interaction among
levels of agencies and within and among policy subsystems (Goggin, Bowman, Lester, &
O'Toole Jr., 1987; Sabatier, 1986).

As the 19905 dawned, theorists were beginning to understand the complexity of
policy implementation and that multiple forces and factors inﬂuenced the level of policy
implementation (Sabatier, 1993). Mazmanian and Sabatier’s models identiﬁed seventeen
factors to be considered (Sabatier, 1993; Sabatier & Mazmanian, 1980) and identiﬁed
three domains or categories in which to organize the factors. Theories and researchers
were beginning to focus on the environment in which policies were implemented and
began to see relationships between the policy being incentivized and the locality into
which it was being imported. While interest in implementation theory grew, researchers
were somewhat frustrated by the lack of progress that had been achieved in building
scholarly understanding and a viable model to predict policy implementation success. As
Lester et al. note, “there is no shortage of variables purported to explain implementation”
(1987, p. 19). Rather, what they found lacking was a framework in which to understand
how those variables interacted and a means to weigh their relative importance in the
policy implementation process

From this initial burst of theories and models, scholars have gone on to create
policy implementation models that attempt to integrate the effects of competing factors

using conceptual and theoretical frameworks based on concepts of networking, social

 

 

 

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capital, civic capacity, vertical and horizontal capacity. They borrow from theories in
organizational studies, economics, political science, criminology, postmodernism, and
even cellular biology (Agranoff & McGuire, 1998; Bardach, 2001; Bedian, 1980; Bolman
& Deal, 2003; Clayton, Cattarello, & Johnstone, 1996; Cobb, Ross, & Ross, 1976;
Colvin, Cullen, & Vander Ven, 2002; Csete & Doyle, 2004; Goode & Ben-Yehuda,

1994; Katz & Kahn, 1966; Kingdon, 1984; Lieber, 2003; Rengert, 1996; Weber, Lovrich,

& Gaffney, 2003; Wright, 2005).

The Normative Choice

In tandem with the continuing debate over theories and models of federally-
incentivized policy adoption, scholars such as Aaron Wildavsky (1971) entered into a
debate over the normative question of whether or not the federal government should
direct or coerce policy choice at the state and local level. The examination of this
normative question is beyond the scope of this paper and will be left to those successors
of Wildavsky who wish to continue that debate. However, Wildavsky’s assertion that the
federal government may be following a faulty paradigm in incentivizing policy adoption
is worthy of note within the context of this paper. Wildavsky linked the modern federal
strategy of policy adoption to the rise of the Planning, Programming, and Budgeting
System (PPBS). This system, developed as a Department of Defense model, was a
means of managing and advancing policy by linked funding to programs that directly
supported the policy goal. Wildavsky claimed that this paradigm was then translated into
a top-down, federal-to-local effort to manage policy through grants-in-aid. Wildavsky
asserts that one fallacy in the reasoning that tranSIated this policy adoption to social

programs is that social policy, and crime policy speciﬁcally, is not implemented through

 

 

 

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a hierarchically controlled system but through a highly federated system of state, county,
and city agencies that often overlap in jurisdictions and often fail to overlap in terms of
core goals. Wildavsky asserts that a faulty assumption undercuts the possible success of
such policy adoption, the assumption a system guiding policy implementation in a
military context would be generalizable to other policy subsystems. His argument, as
applied to the criminal justice system, would be that military culture and theories of
engagement, while not universally held, provide much less ambiguity than theories about
the source or prevention of criminal activity or what constitutes justice. Therefore,
though the normative question will not be addressed, the factors Wildavsky argued
complicate and hamper incentivized policy adoption resonate in later theory and research

and in the arguments offered here.

Building a Model Based on Theory and Research

Although there is high concern among politicians, professionals, and the

general public with the outcomes of correctional policies, there has been

little systematic study of the factors that affect policy adoption.

(McGarrell & Duffee, 1995, p. 1)

Upon review of the literature on federal-incentivized policy adoption in the area
of criminal justice, it quickly becomes clear that few theory-based studies of policy
adoption, and more speciﬁcally federally-incentivized policy adoption, have been
undertaken. While this dearth of literature poses some challenges, there are still concepts
and ﬁndings from which to build and test theory-based models. In addition, theories,
theory-based models of collaborative policy implementation, and adoption research

ﬁndings can be borrowed and translated from other ﬁelds. Theory-based research in the

criminal justice ﬁeld, even though not focused on factors inﬂuencing adoption, or not

 

 

 

 

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focused on incentivized adoption, are useful in the search for mechanisms that promore or
inhibit policy uptake. Atheoretical evaluations in criminal justice also provide insights
into the contexts and factors that inﬂuence the degree to which a federally-incentivized
policy is implemented.

The model proposed and tested in this study hypothesizes that the adoption of
such a policy is inversely related to the risk of that adoption, as perceived by the leaders
of local agencies tasked with implementation. It is further hypothesized that the costs
and beneﬁts relate to factors external to implementing agencies, internal to those
agencies, and internal to the task force or group given the responsibility to oversee such
implementation.2 The model predicts that: 1) policy adoption will be related to the level
of organizational functioning of the task force; 2) the level of organizational functioning
of the task force will inﬂuence, and be inﬂuenced by, the investment of member agencies
in the task force; and 3) competing factors, internal and external to each participating
agency, will inﬂuence the willingness and ability of agencies to invest. The more the
policy complements the goals, infrastructure, attitudes, and survival of the implementing
agencies, the less risky the policy will appear and the more agency leaders are willing and
able to invest in the task force. Project Safe Neighborhoods, a national program intended
to reduce gun crime, provides a laboratory for a test of the complex and inter-woven

factors hypothesized to inﬂuence policy adoption.

 

3 Recent federal incentivized policies in criminal justice have acknowledged the need to engage multiple
agencies and entities in policy planning and implementation. To assure this collaboration. many policies
require representation from speciﬁed entities in planning and implementation task forces.

10

 

 

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Project Safe Neighborhoods: A Natural Laboratory

The model will be tested on a federally incentivized policy initiative, ProjectSafe
Neighborhoods (PSN). PSN provides ﬁscal support to each of the federal judicial
districts to develop a broad-based local, state, and federal effort to address gun violence
through the use of data-driven, collaborative strategies. This federal program provides a
challenging but important research environment. It reﬂects the current trend in federal
incentives to criminal justice agencies which mandate collaborative planning and
implementation of programs and the use of data collection and analysis while allowing a
good deal of ﬂexibility in the choice of the target areas and the speciﬁc programs to be
implemented. As theory and research will indicate, multiple actors and multiple
implementation structures makes research designs complex. Deﬁning, measuring, and
modeling factors proposed to inﬂuence policy adoption raises difficult issues in terms of
adequately reﬂecting theoretical concepts and creating a framework for analysis. Using
theory and research to build and test a model of the inﬂuence of external factors on PSN
adoption is offered as a step toward building better understanding of the competing
factors and contexts that inﬂuence policy adoption and a model and methodology for

exploring those factors through the lens of risk analysis.

Overview

The theory and the research that help explain why federally incentivized policy
adoption continues to be employed by the federal government, and what factors are
linked to the success or failure of that adoption will be addressed in Chapter 2. Chapter 3
reviews the research on the success and failure of such initiatives, and the theories that

have and can be used to frame and test factors and contexts that could inhibit or enhance

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incentivized policy adoption and research ﬁndings related to those theories. This review
will draw from ﬁelds beyond that of criminal justice to enrich the information available
to identify potentially important factors and contexts for inclusion in the model of
incentivized policy adoption. Chapter 4 introduces the elements of risk analysis and
integrates those elements into the context of policy adoption. Chapter 5 presents a model
for incentivized policy adoption, a research strategy based on risk analysis and a
competing factors model developed by Quinn (1991). Chapter 6 outlines the research
context, the hypothesis and key research questions, and the research design and
methodology applied to the Project Safe Neighborhoods initiative to test the model.
Chapter 7 focuses on variable construction. Chapter 8 presents the analysis and ﬁndings.
In the ﬁnal chapter, the potential for the utilization of the theory-based model, and the

implications for scholarship and practice are discussed.

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CHAPTER TWO:
SHOULD FEDERALLY-INCENTIVIZED
POLICY ADOPTION SUCCEED?
Several theoretical perspectives argue for the likelihood of adoption of federally-
incentivized policies by state and local criminal justice agencies. Though differing in the
manner in which they conceptualize the factors and processes supporting replication, all

theoretically link some perceived beneﬁt, or risk reduction, to the adoption of a policy

that is, or is touted as, successful.

Rational Choice and the One Best Way
In every case, one measure after another had been tried out, until the proper

remedy has been found. That series of proper elimination, that evolution is
what is called scientiﬁc management.

(Taylor, 1996, p. 96)

Scientiﬁc management theory proposes that a best practice can be developed.
Both this theory and rational choice theory offer related explanations of why adopting
such a strategy would serve the goals of the entity. Scientiﬁc management, espoused by
classic organizational theorists, such as Fredrick Winslow Taylor, held out the promise
that the one best way to provide a service or product can be discovered or identiﬁed
(Taylor, 1916/1996). With appropriate investment, protocols, and training, scientiﬁc
management contended that the adoption of this “best way” would provide the most
efﬁcient and effective means to the desired outcome.

One of the simplest theories explaining why a public entity, or any entity, would
adopt a seemingly successful practice from another is the rational choice model (Frank,

2000). Assuming that entities share a common goal, if one jurisdiction has greater

13

 

success in achieving that goal, then a rational choice would be for the less successful
jurisdiction to import the strategy and, thereby, bolster its own success. In economic
theory, market-driven models predict that ﬁrms that are able to innovate initially proﬁt
from the novel processes and products and enjoy a competitive advantage. However, this
idea of rational choice predicts that, eventually, all ﬁrms in the sector will have an
economic incentive to replicate or adopt similar processes (Frank, 2000). Based on this
theory, any organization would be expected to make a rational choice to adopt the best
practices in the ﬁeld.

This theory was reﬂected, if not overtly, in public policy uptake efforts
. popularized in the 19905, aimed at “reinventing government” by having public entities
adopt policies from the private sector (Kettl, 1995; Osborne & Gaebler, 1992). Phrases
such as “evidence-based”, “exemplary,” or “best” were used to describe policies and
programs, imported from the private sector, that were held up as models for criminal
justice and other public agencies to mimic (Osborne & Gaebler, 1992). The positive
outcomes expected from the adoption of these private sector strategies often were
expressed in costs and beneﬁts and efficiency (Osborne & Gaebler, 1992). While the
focus on replicating private sector policies has abated somewhat, the public sector is still
focused on promoting policies that appear to provide beneﬁcial results.

Such economic models, by the nature of the assumptions implicit in the models,
reduce policy implementation to a rational and objective choice among alternatives aimed
at obtaining the maximum positive outcome (Frank, 2000). As an example, economists

Hennessey, Gray, & Conover (1977), propose a criminal justice policy implementation

14

 

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model capable of predicting the mix of correctional alternatives that will produce the

lowest recidivism.

We treat the corrections department as analogous to a ﬁrm that must

choose among production processes...
(Hennessey et al., 1977, p. 266)

However, the development and utilization of such economic models of policy
choice and policy adoption have been stymied by problems deﬁning the expected
criminal justice outcomes and monetizing those outcomes. As Noam (1977) notes,
despite the huge investment in the criminal justice system, “the scarcity of an economic
contribution to research of the criminal justice system is notable” (p. 41). In explaining
the difﬁculty of utilizing economic models for criminal justice policy, Reich (1977)
argues that the limiting factor is the inability to operationally deﬁne a state of maximum
beneﬁt, as “justice cannot be optimized” (p. 69). Pointing to the tension and competition
between criminal justice goals, such as a speedy trial and a fair trial, Reich frames the
fundamental hurdle to the construction of an economic theory of criminal justice policy
uptake in the question “What, after all, is to be optimized?” (Reich, 1977, p. 59). To
explain why rational choice and economic models are still linked to the popular notion
that efﬁcient criminal justice policies will stem from the uptake of model policies, Reich
theorizes that these models provide a comforting paradigm in which crime, a threat to the

fabric of society, is solvable and controllable - if only we devote sufﬁcient research, time,

and resources to its eradication (Reich, 1977).

15

 

 

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Wicked Problems and the Search for Legitimacy

Reich’s question of what is to be optimized for a criminal justice program to be
considered successful points to the goal-setting problems endemic to the criminal justice
system. The problems of understanding, preventing, and controlling crime, of
intervening with criminals, providing justice to and for victims and offenders, and
assuring public peace and safety are what Rittel and Weber (1973) deﬁne as wicked
problems. Wicked problems, they assert, defy the search for the one best answer.
Wicked problems are laden with judgments about what is bad or good —— not just effective
or ineffective. How a wicked problem is perceived and deﬁned may determine how
people choose to solve it. The problem can often be deﬁned as a symptom of another
problem. Wicked problems elude deﬁnition in terms of when to stop, when the ideal
state has been reached. Solving the seemingly intractable problem of crime ﬁts the
deﬁnitions well. Decision-makers in the criminal justice ﬁeld are hard-pressed to offer
clear and universally acceptable deﬁnitions of crime or justice and ever more hard-
pressed to offer deﬁned and measurable programs to meet those goals.

The value-laden and seemingly endless debate over the roots of and remedies for
crime swirl around weighty issues of social conditions, discrimination, sexism, and even
biological propensities. In assessing similar issues in education policy adoption Stone,
Henig, and Perannunzi (2001) describe systems that deal with these wicked problems as
“high reverberation systems” (p. 48). In these systems, value-laden ideas bounce back
and forth among policy makers whose institutional boundaries overlap and whose gdals

are often unclear or conﬂicting.

16

 

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Organizational theories attempt to describe the factors outside and inside such
institutions that both create high levels of reverberation and offer explanations of the
advantage of adopting a “best practice” when coping with wicked problems. Classical
organizational theorists, such as Max Weber (1922/1946) and Henri F ayol (1916/1949),
conceived of the rule-driven bureaucracy as a way to overcome the complexities of

administration.

It is obvious that technically the great modern state is absolutely dependent
upon a bureaucratic basis. The larger the state, and the more it becomes a
great power, the more unconditionally this is the case.

(Weber, 1946, p. 211).

According to this theoretical view, policy adoption of a standardized, federally
incentivized policy would be seen as a rational decision, providing the policy provided
documentation of rules and protocols sufficient to assure adequate organizational
structure.

As organizational theory evolved, theories began to encompass a range of factors,
internal and external to the organization, that were proposed to inﬂuence organizational
behavior and survival. The change from a mechanistic view of organizations to a more
organic perspective, changed the lens through which policy adoption was viewed, but
these newer perspectives also predicted the allure and the beneﬁt of incentivizing policy
adoption, albeit through different motives and processes.

Beginning in the 19605, theorists began to consider organizations as open systems
(Burns & Stalker, 1995; Katz & Kahn, 1966). The internal ﬁrnctioning of the
organization was a determining factor in the success of the organization, but ability to

successﬁrlly manage meant the ability to manage in the context of the external

17

 

 

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environment. For decision-makers in public entities in high—reverberation systems, such
as criminal justice, theorists in the new institutionalism school offer concepts that explain
why policy adoption or policy importation provides a beneﬁt. New institutional theory
predicts that when “when goals are ambiguous, or when the environment creates
symbolic uncertainty” organizations may choose to model themselves after other
organizations that appear to be successﬁil (Dimaggio & Powell, 1991, p. 151). This
mechanism - mimetic isomorphism - if it acts as theorized, predicts that a criminal justice
agency struggling with conﬂicting, competing, or unclear goals but needing to maintain
legitimacy with internal and external stakeholders might be more likely to adopt a policy
that is touted as a model or as a national standard (Meyer & Rowan, 1977).

Strategies that are rational for individual organizations may not be

rational if adopted by large numbers. Yet the very fact that they are

normatively sanctioned increases the likelihood of their adoption.

(Dimaggio & Powell, 1991, p. 148)

Meyer and Rowan (197 7) theorize that isomorphism, or mimicking successful
policies ﬁom like entities, transforms policies into myths. These myths become credible
symbols of success that provide some evidence that the entity is performing at industry
standards. Myths increase legitimacy. While Meyer and Rowan see this as an aspect of
all organizations, scholars attempting to research organization differences and outcomes
between private and public sector organizations point out the added need for such myths
in the public sector. Public entities, especially those with missions related to social
issues, are high reverberation systems. These systems - such as the criminal justice
system - deal with wicked problems, vague goals and conﬂicting goals, and with

constituencies who often view the issue from very different perspectives. The ability to

18

 

 

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gain legitimacy through adoption of a policy with mythical qualities provides the
insulation of legitimacy to agency leaders who, otherwise, might ﬁnd proving program

effectiveness to multiple constituencies difﬁcult or impossible.

Policy Adoption: Mythical or Measurable Outcomes

The theories explored thus far present a ﬁ'amework for viewing the resilience of
the concept of federally-incentivize policies on the basis of a perceived beneﬁt or a
lowered risk for those agencies adopting such a policy. Within these theoretical contexts
there emerge two streams of beneﬁt or risk: mythical or measurable outcomes.

When output can be easily evaluated a market often develops, and

consumers gain considerable rights of inspection and control. In this

context, efﬁciency often detemiines success.

(Meyer & Rowan, 1977, p. 354)

In 1940, V0. Key ( 1940/1992) stated that, ideally, all policies instituted by a
governmental entity would assure the same marginal return for investment (1940/ 1992).
In order to gage the marginal return, policy implementation would involve a clearly
deﬁned goal, process, and measurable outcome. If each policy had such a clear process
and product, myths would not be required as a substitute for measures.

In some criminal justice policy adoption, it might appear that despite the issues of
wicked problems in a high reverberation subsystem, that simple measures might suffice.
An example is a federal program that incentivized states to build pre-release centers to
provide community-based correctional alternatives to state prisons (McGarrell & Duffee,
1995). Researchers were able to state with certainty how many centers and how many

beds (or placements) were developed in each state as a result of the federal incentivized

program (Duffee & Wright, 1990). Tangible, physical assets were added to the state’s

19

 

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infrastructure and were, arguably, a measurable beneﬁt accruing from the adoption of the
federal policy. While, on its face, this example appears to be an example in which
outcomes could be based on measure and not myth, in actuality competing factors in the
adoption context of this example highlight the role of myth even in circumstances where
outcome measures might appear viable.

While V.O. Key set the ideal of a budgetary theory to guide government action,
the title of his 19405 article is “The Lack of a Budgetary Theory.” In this work he noted
that no theories had been developed that would guide government into such an ideal
process. Scholars and theorists attempting to develop such a theory to guide policy
implementation have found that concepts such as marginal return on investment become
muddied by the attributes of public goals and public goods (Downs & Larkey, 1986).
This issue becomes apparent if the measure in the example of the pre-release centers is
scrutinized. The goal of the federal program was to shift from a prison-based
correctional system to a system that focused on community-based corrections. The goal
was not just to build a pre-release center or centers. In measuring the marginal beneﬁt of
the policy, a state that built a twenty-ﬁve bed, pre—release center but continued to ﬁll
prison beds in a massive system would meet the process goal of construction of a center,
but not meet the goal of reducing reliance on prisons through the enhancement of
community-based corrections. The beneﬁt to the state would be increased capacity but
the beneﬁt in terms of the federal policy is far less clear. In terms of mythical outcomes,
since the federal government is promoting pre-release centers as state of the art
correctional policy, the state’s building of one pre—release center might increase its

legitimacy while allowing it to continue its correctional policy virtually unchanged.

20

 

Mythical Outcomes

Institutionalized products, services, techniques, policies, and programs
ﬁJnction as powerﬁil myths, and many organizations adopt them

ceremonially.
(Meyer & Rowan, 1977, p. 340)

In the early 19005 George Sorel wrote on the power of governmental and societal
myths (Sorel, 1908/1999). He described four aspects of such myths. First, they are
social. Myths involve the action of people and often are framed to inspire or explain
social action. Secondly, Sorel described myths as political. Myths are developed to
change the ways in which the affairs of state and the affairs of men were conducted.
Third, myths are intentional. Those who developed myths want to mold social
conditions, not reﬂect the current reality. Lastly, myths are magical. In social science
terms, myths cannot be falsiﬁed — they cannot be disproved.

The power of myths to provide the aura of legitimacy as an alternative to a
measurable outcome is central to the idea of institutionalist theory and the value of
isomorphism. Crank and Langworthy, (1992)studying policy adoption by law
enforcement, adapted and updated Sorel’s deﬁnitions, deﬁning the characteristics of
myths as having power, being contextualized by the social or geographic areas, marking
the transformation of something, and being tied to political interest or interest groups
(Crank & Langworthy, 1992, p. 34).

While some outcomes, such as the number of pre-release centers, can be
measured, others cannot. Enhancing community options for offenders is subject to debate
— not just about how that is measured, but as to whether community-correctional facilities

are a better alternative to prisons, and a better alternative in whose View. If the building

21

 

 

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of community corrections centers is accepted as an indicator of state-of-the-art
corrections, it becomes a myth in this policy environment and lends the power of
legitimacy to those agencies adopting the policy. Yet, the lack of consensus at the local
level on the value of community correctional centers may result in uneven adoption,

ranging from symbolic, to minimal adoption, to more ﬁmdamental shifts in correctional

practice.

Beneﬁts to Criminal Justice Agencies of Myths and Coercive Mimetic Isomorphism

The concepts of coercive isomorphism and myths are easily integrated when
applying them to federally-incentivized policies to solve wicked problems in high
reverberation systems (Dimaggio & Powell, 1991). The federal government, by the use
of such grants-in-aid, attempts to exert pressure on local governments to adopt a policy,
which is the deﬁnition of coercive isomorphism. This is an intentional, social, and
political act. The attractive and the potential beneﬁt to the implementing agency is the
insulation it provides when outcomes are not easily measurable. As important for
criminal justice system agencies, adoption can provide insulation when outcomes are
measured and that measurement is unﬂattering.

In most cases it is impossible to completely falsify or prove the value of a
particular criminal justice policy. Controlled studies of policies are practically and
legally difﬁcult, and even if a control group is selected or a comparison region assessed, a
host of uncontrollable social, political, and economic factors continually loom,
threatening to confound the study. Policies well grounded in theory and well executed
may not result in lowered crime rates or speedier trials due to factors well beyond the

scope of one policy change. Adhering to a federally promoted policy provides some

22

insulation and some legitimacy when performance measures are absent or headed in the

wrong direction.

Deﬁnitions of Success

When the federal government offers ﬁscal incentives. to adopt policies it is a
formal attempt to inﬂuence policies at the state and local level. Success to those driving
the policy at the federal level may differ from success as deﬁned at the state or local
level. While the policy—makers at the federal level may have in mind the creation of a
policy that molds social action, this formal coercive strategy may or may not result in
hoped-for results. For the framers of the policy, modeling the policy in all aspects and
institutionalizing that policy is the deﬁnition of success. Some local ofﬁcials may share
that deﬁnition of success, but for others the deﬁnition of success may be based on their
own calculation of risks and beneﬁts. The very characteristics of the wicked problems
that create an incentive for agencies to embrace myths that lend legitimacy, in lieu of
measurable results, create the conditions that may result in less than full and robust
implementation.

According to Crank and Langworthy, effective myths are context-speciﬁc (1992).
F ederally-incentivized programs are developed outside the context of local jurisdiction
and hence, may or may not be complementary to local context. Goals or protocols may
conﬂict with local conditions, attitudes, and practices. Given this potential conﬂict and
the often limited ability to provide measurable outcomes, agencies may decouple policy
and practice. Success, as deﬁned by the framer of the federal policy, is full
implementation of the policy in every aspect. However, new institutional theory would

consider such conditions ripe for decoupling (Meyer & Rowan, 1977). In the federally

23

 

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incentivized policy aimed at deinstitutionalizing status offenders, such decoupling was
found to occur. An example of such formal acceptance with little practical effect is
illustrated by the adoption of deinstitutionalization in Ohio in the 1970s to the late 19805.
State law and policy were aligned with the new mandates for deinstitutionalization, and
the state reported full compliance and was granted ﬁill funding. However, status
offenders continued to be placed in secure detention at a higher rate than occurred in any
other participating state (Grossman, 1999; J. Katz, 1982a; Schwartz, 1989).3

In terms of success, reports and evaluations indicate that some jurisdictions do
successﬁilly adopt incentivized policies. The degree to which this is true appears to be
subject to some interpretation. To the degree that the processes or performance measures
attached to the policy can be evaluated, it can be documented that policy adoption
typically ranges from full implementation, implementation of certain aspects of the
policy, or no meaningful implementation. For those policies for which such evaluation is
not possible, not required, or cost-prohibitive to undertake, the level of implementation
may be masked.

Affrxing the right labels to activities can change them into valuable

services and mobilize the commitments of internal participants and external

constituents.
(Meyer & Rowan, 1977, p.350)

One manner in which local policy makers can harness the power of the myth to

enhance legitimacy with no additional local investment is to rename existing programs to

 

3 To accomplish this decoupling legally, the judges of the juvenile court used the charge of Violation of a
Court Order (VCO). For example, to elevate a truancy from a status offense to a criminal offense, judges
would place a child under charge order to attend school. If he or she were truant, the child was then subject
to secure placement

24

 

match the requirements of the federal incentive. Decoupling the policy from practice,
espousing adherence to the policy but not translating that into activities could also
provide some beneﬁt to the local agency while requiring little local effort or investment.

The outcome of many studies, spurred on by the movement to “reinvent
government” and import private sector policies and protocols found that accountability
for proﬁtability or cost-beneﬁt were difﬁcult to translate into the public sector (Foreman,
1995;Kett1, 1995; Osborne & Gaebler, 1992). Public agencies do not always enjoy the
luxury of being assigned projects that are possible to accomplish. The expected rate of
return of a public project is frequently less important to elected officials than the
praiseworthy and electoral appeal of the goals that the project is supposed to accomplish
(Thurow, 1986, p. 43).

When Thurow (1986) attempted to study the relative efficiency of private and
public entities, he concluded that expected outcomes are subject to different forces in the
public sector. At some level, costs versus proﬁts limit survival of private sector ﬁrms.
Funding and survival for government entities is more often linked to the perception that
“praiseworthy” work is being accomplished (p. 43 ). Hence, the idea of importing a
policy that has gained legitimacy in another jurisdiction provides the aura of credible,
legitimate, and important policy adoption in the importing jurisdiction. This
isomorphism allows organizations to “incorporate elements which are legitimized
externally,” reducing turbulence in the organization and with its external stakeholder and
thus protecting “the organization from having its conduct questioned” (Donnermeyer &
Davis, 1998, p. 348-349). Therefore, it can be argued that rather than offer a positive

beneﬁt, the importation of legitimized policies reduces agency risk. Not adopting those

25

 

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practices perceived as the industry standard could call the legitimacy of the organization
into question and threaten its ability to obtain resources (Crank & Langworthy, 1992).
An example is Katz’ study of the adoption of gang units in police departments (C.
M. Katz, 2001). Katz concluded that many law enforcement entities mimicked other
departments and set up units not because investments in those units provided a
measurable return, but rather these were policy decisions “that have not been objectively
tested, but are considered to be truths because they are known and accepted by all within

the institutional environment” (p. 39).

Contagion and Diffusion: A Different Benefit

While open systems models and the new institutionalism predict the policy
adoption through a perception of lowered risk and perceived beneﬁts through increased
legitimacy, contagion and diffusion models focus on another aspect of perceived beneﬁts.
Contagion and diffusion models, based on economic development theory, link
transmission of a behavior or practice to a process that begins with modeling and then
involves investment in the pattern (Berry, 1969). The success of the policy transmission
is driven by the perceived risk or beneﬁt of that investment. Such models have been
translated into theories to explain the pattern of drug market emergence, the proliferation
of gangs, and to promote the value of positive peer pressure programs for inmates
(Rengert, 1996; Vorrath & Brendtro, 1985). In translating this model to incentivized
policy replication, the federal government could introduce a “best practice’ from another
jurisdiction. The modeling and promotion of that policy could be aided by the infusion of
federal ﬁrnds. However, education policy researchers note the failure of this model in

high reverberation systems (Clayton et al., 1996). While the initial investment from the

26

 

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local jl
lﬂSiitu
that le
mach

urnC

 

“Eed:
ante.

thOSzl

 

federal government may assure some level of exposure to the new policy, Stone, Henig,
and Persannunzi (1996) (2001) argue that factors in the local environment will greatly
inﬂuence the perceived beneﬁt or risk and inﬂuence of such adoption and, ultimately,
whether the jurisdiction will invest in implementation. Among the factors they cite are
public attitudes and political costs. Using the example of deinstitutionalization of status
offenders, while the federal government provided funding to incentivize alternatives to
placement for non-criminal youth, contagion was not automatic. Utah initially refused to
participate as the governor believed the costs of adoption might outweigh the funds
provided (Katz, 1982b). Similarly, Wyoming’s leadership complained during
Congressional hearings that, given low population density of the state, provision of
alternatives was ﬁrnctionally and ﬁscally impossible in many areas of the state (Hearings

on Juvenile Justice and Delinquency, 1977).

Enacted versus Crescive Change

The power of the federal incentive to create a contagion and diffuse policies to
local jurisdictions may be a ﬁmction of their power to lend legitimacy, as the new
institutionalists would predict. However, as Stone, Henig, and Persannunzi (2001), assert
that legitimacy may depend on the local context. Daniel Bell, in studying responses to
enacted versus crescive change indicates that changes imposed from outside are more
difficult to ﬁrlly implement that those that those that are crescive, or growing out of local
needs or changes in technology (Bell, 1961). Therefore, while many theories point to the
attraction and the organizational beneﬁts of policy importation, local context may temper

those beneﬁts and increase possible risks.

27

risks ar
policies

shctx 1i

underm
fact t .e

year 2i.

more 1
the G;
and ﬁt
3003)
PTOgy-

ll‘lffag

 

panl;

 

CHAPTER THREE:
THEORY AND FINDINGS ON
FEDERALLY INCENTIVIZED POLICY ADOPTION

While theory suggests that adopting federally-incentivized policies could lower
risks and increase beneﬁts, research ﬁndings suggest that full implementation of such
policies is less than a certainty. National and international studies of incentivized policies
show limited success.

The US. Government Accounting Ofﬁce’s (GAO) evaluators stress the need to
understand the level at which incentivized criminal justice policies are adopted, citing the
fact that the U. S. Department of Justice (DOI) spent nearly four billion dollars in ﬁscal
year 2002 alone to provide grants-in-aid to encourage such policy adoption (GAO, 2003).
This GAO study indicates that poorly-articulated plans limited the ability of the GAO to
assess implementation in many of the programs under evaluation in 2003, but even in the
eleven programs that began with well-designed implementation and evaluation strategies,
more than half (six programs) failed in implementation (GAO, 2003). Another study by
the GAO reviewed the evaluations of ﬁve justice programs funded through Byrne grants
and ﬁve programs funded through the Violence Against Women Office (VAWO) (GAO,
2003). GAO investigators noted implementation and evaluation problems in all but one
program. Data collection was hampered by the lack of a management information
infrastructure in many of the implementing groups, interoperability problems among
participating agencies meant limited data sharing, and together with legal and
administrative barriers policy implementation was inhibited. In the majority of cases

under evaluation, GAO investigators found that policies were not implemented, not

28

 

 

implement]
federally-i:
1.10.89) for.
l
implemen
reports fr.
Preventic
ll‘
noted a r
based Ct
uneven l
Their in
Deeds tr
Workini

the Dell

 

patien-

Theo,

hat

 

 

implemented as planned, or only partially implemented. In researching the adoption of
federally-incentivized efforts to remove status offenders from institutions, Schwartz
(1989) found that the policy was not implemented in some jurisdictions and symbolically
implemented in others. Even after a quarter of century of federal pressure and incentives,
reports from the US. Justice Department’s Ofﬁce of Juvenile Justice and Delinquency
Prevention show only seven states in ﬁrll compliance (OJJDP, 1978).

In a theory-based study of policy adoption McGarrell and Duffee (1995) also
noted a range of implementation outcomes. Their study of the adoption of community-
based, correctional pre-release centers indicates that many “federal initiatives generated
uneven and varied implementation at the state level” (McGarrell & Duffee, 1995, p. 15).
Their investigations led them to conclude that to understand policy adoption, each case
needs to be evaluated in light of the anticipated impact of that policy, the internal
workings of the implementing organizations, and the environmental context (including
the political context) in which that implementation will be played out. Starting from
these insights and integrating theory and research ﬁndings, categories of factors and

patterns of policy adoption can be drawn.

Theories and Patterns of Policy Adoption
It seems clear that the rational-model approach uses a closed-system
strategy. It also seems clear that the developers of the several schools

using the rational model have been primarily students of performance or

efﬁciency, and only incidentally students of organizations.
(Thompson, 2005, p. 6)

Early organizational theorists, as well as contemporary theorists and researchers.

have hypothesized about the relationship between employee attitudes and the ability of an

29

organlla'
those of
inﬂuence
imweme
1941. WE
the desir
greater g
workers
146.). Tl
rdanons
ltnou'led
1996) 5
member

Biebel‘ ‘

Professi
that the

“Smith

 

1991.;
iSOmOr

organization to implement policy change. The neoclassical organizational theorists and
those of the human relations school focused on the power of employee attitudes to
inﬂuence the workings of an organization, including the ability of an entity to adopt and
implement novel strategies (Follett, 1926/1996; McGregor, 1960; Roethlisberger,

1941/ 1988).4 Chester Barnard, writing in 193 8, noted the power of “ideal benefaction” or
the desire of workers to participate in efforts that satisﬁed a need to contribute to the
greater good and satisfy personal ideals (Barnard, 1938, p. 142). He also emphasized
workers’ needs for solidarity and an “enlarged feeling of participation” (Barnard, 193 8, p.
146). This concept was born out in policy studies of education reform which found a
relationship between wider understanding of the agency mission (as opposed to
knowledge of only operational goals) and staff support for novel policies (Clayton et al.,
1996). Studies of the implementation of problem-oriented policing found that staff
members’ perceptions of the value of the policy affect policy adoption (Cordner &
Biebel, 2005).

Institutional theorists suggest that if employees of an agency are affiliated with
professional organizations that sanction a particular policy, there is a greater likelihood
that those employees will champion the adoption of that policy as a means “to establish a
cognitive base and legitimation for their occupational autonomy” (Dimaggio & Powell,
1991, p. 70). Dimaggio and Powell (1991) link the power of professional groups to
isomorphic change, a mechanism through which one organization takes on the attributes

of another. They theorize that if professional staff from different organizations within the

 

" The power of employees or subservient workers, slaves, and foot-soldiers to enhance or degrade the
adoption of a policy or strategy has been the topic of treatises through recorded time. Ideas not dissimilar
to those expounded by human relations theorists can be found in The Prince and Discourses, written by
Niccolio Machiavelli, circa 1513 (Machiavelli, c.1513/1950).

30

same pol
— a policj
that are i
Van Dus
deinstitt
professi

espouse

same policy subsystem belong to associations that espouse support for — or opposition to
— a policy, the greater the pressure there will be on these organizations to adopt strategies
that are in harmony with the professional group’s beliefs and to reject those in disfavor.
Van Dusen’s (1981) research on policy adoption of federally-incentivized
deinstitutionalization of status offenders found that congruence between the stance of
professional groups in juvenile courts and correctional systems and the philosophy
espoused in deinstitutionalization inﬂuenced the level of policy adoption.

The crux of this dimension of the theory is the matter of to what degree

legislative philosophies are resonant or dissonant with the philosophies of

practitioners who must implement the legislation.
(Van Dusen, 1981, p. 802)

However, research results on the impact of professionalism on adoption have been
mixed. Some studies have found no signiﬁcance and other ﬁnd professional groups to
have measurable effects on policy adoption (Crank & Langworthy, 1992; McGarrell &
Duffee, 1995; Thurman, Zhao, & Giacomazzi, 2001). The most compelling results
appear to be associated with a specialized form of employee group, the union. Scholars
have linked this affect not just to professionalism but also to a desire to maintain
autonomy and job security (McGuire, 1997). Studies of juvenile correctional reform
indicate that the most resistance for policy shifts came from labor groups who reframed
the question from what will provide the best outcome for children to “what will protect
my career?” (Campbell, Converse, Miller, & Stokes, 1965, p. 455).

While linked to employee and professional attitudes, the impact of unions also
will be discussed in terms of the role such unions play as special interest groups. Studies,
especially in the area of policing, have noted the power of unions and professional

associations to inhibit, as well as promote, the adoption of a new policy (McGuire, 1997).

31

Chang:
unesi
adopn
organi
hhﬁ3u
exantr
accust
oﬁen

eXpen
foHov
stand;

“tun:

Hterar
Physi
their ‘
Inefa
pTOIQ
depar
OU101
Sharp

at odc

Researchers have found criminal justice agency culture is particularly resistant to
change (Tong, 1984). The level of discretion and autonomy that is characteristic of many
roles in this system has been proposed as a reason that change can be difﬁcult. Policy
adoption research indicates that implementation can be problematic in decentralized
organizations where actors exhibit a good deal of discretion and autonomy (Agranoff &
McGuire, 1998). Evaluations of policy adoption in criminal justice areas provide
examples of that difficulty. Policies perceived as limiting discretion for those
accustomed to exercising high levels of discretion, such as judges and police ofﬁcers,
often have been difﬁcult to fully implement (W eisburd, 2000). In a sentencing
experiment for those convicted of drunk driving, results indicate that judges did not
follow the sentencing protocol in more than half of the cases (W eisburd, 2000). Craft
standards for autonomous work have been noted in the failure of policy implementation
within law enforcement agencies (Kuykendall & Roberg, 1982; McGuire, 1997).

Uneven policy adoption is not limited to the arena of criminal justice. Medical
literature on adoption of evidence-based practices indicates adoption failure is linked to
physicians’ perceptions that the policy limited their individual judgment and devalued
their training and experience (Spring et al., 2005). Another study from the medical
literature assessed the adoption of an incentivized health education and screening
protocol in medical clinics. The researchers noted that the autonomy with which the
departments within the clinics operated contributed to the policy adoption failure in ﬁve
out of the six clinics receiving the grants-in-aid (Cunningham, Michielutte, Dignan,
Sharp, & Boxley, 2000). When organizations have a culture that is resistant to change or

at odds with the particular policy, internal organizational issues are a signiﬁcant predictor

32

of adoptiO
policy apt

“necessarj

Factors 0

\K'
policy ad.
affect org
managem
formalin

employee

inﬂuence

 

 

of adoption failure. The ability of an organization to fully implement an incentivized
policy appears to be aided if there is a consensus among staff members that the policy is

“necessary and possible” (Gargeya & Cydnee, 2005, p. 511).

Factors of Production, Protocols, Structures, and Infrastructure

While human factors are the focus of many theories used to develop a model of
policy adoption, structures and systems within organizations also are hypothesized to
affect organizational behavior. Organizational literature focused on administration and
management stresses how the factors of production - such as the number of processes, the
formality of the production protocols and oversight, the tools and training given to
employees, and the physical infrastructure - and the relationships among those factors
inﬂuence organizational functioning and change (F ayol, 1949; Simon, 2005; Weber et al.,
2003). According to this perspective, the differences in technologies and the technical
functioning of organizations, the manner in which physical resources and people are
developed and deployed, and the degree to which the technical core of the organization
can support the policy change may all determine the fate of an attempt at policy uptake
(D. Katz & Kahn, 1966; Thompson, 2005). Broadly categorized, these factors of
production are theorized to inﬂuence policy adoption through the impact of the level of
organizational competence and the congruence of organizational assets to the policy
being incentivized.

In terms of organizational competence, the better equipped the organization is to
support its technical core and conduct routine functions, the greater its stability, a factor
theorized as necessary for the organization to support the pressure of change (Kettl, 1995;

Quinn, 1991). If payroll cannot be calculated and the computer system reﬁrses to

33

Gouvis.
Safefut
and tecl
adoptio
accoun‘
Similar
techno?
care re
physic;

Ber ral

adopu
the pr;
lIlVeS:
Can in
the 31'
that E'

impie

operate, implementing a new policy becomes more problematic than it might in an
agency that has routinized such functions (Morley, Rossman, Kopczynski, Buck, &
Gouvis, 2003). An example is found in the evaluation of a criminal justice program,
SafeFutures, in which investigators found that agencies with poorly developed protocols
and technologies had difﬁculty implementing the policy. Agencies that failed in policy
adoption were less able to deal with “aspects of program implementation such as
accountability, recordkeeping, reporting, program evaluation” (Morley et al., 2003, p. 3).
Similarly, adoption of problem oriented policing was inhibited by the lack of
technological resources and data collection protocols (Cordner & Biebel, 2005). Health
care researchers ﬁnd that even when agencies are supportive of policy adoption, lack of
physical resources left agencies unable to implement the desired policy change (Tumiel-
Berhalter & Hershey, 2005).

The second area in which factors of production are hypothesized to affect policy
adoption is the congruence of organizational assets to the implementation requirements of
the proposed policy. Public budgeting theories explain the power of ﬁxed costs and
investments in infrastructure (Frank, 2000; Hyman, 1999). These investments and costs
can involve physical and human resources and the building of tangible assets, as well as
the creation of standard practices and protocols. If the tools, technologies, and protocols
that exist within the organization are the same, or closely related, to those needed to
implement a new policy, this increases the likelihood of policy adoption.

In government agencies, as in any private or public ﬁrm, costs for the physical
infrastructure represent an investment and a possible barrier to change. An incentivized

policy requiring pre-release centers provides an example. If a state were heavily invested

34

 

in large.
to small
abandot
often re
operari'
require
the pct
employ
at least
protecr
contini
Stafﬁrt

facilitz

DEV." if
2005 )

SYSIer

 

 

 

in large, high-security prisons and the incentivized policy required shifting some inmates
to small, low-security facilities, policy adoption might be impeded if this meant
abandoning or under-utilizing existing resources. Closing down programs or facilities
often requires legislative action and politically-mandated phase-down periods so
operating expenses may continue for extended periods. In addition, since most jobs
require an investment in training, policy changes may mean a loss of this investment and
the potential of new training costs. Perhaps more importantly, since most government
employees have civil service protection, employees also can be considered a ﬁxed cost —
at least for the short term (Hyman, 1999; Stillman, 1995).5 Civil service and union
protections generally require a substantial transitional period during which salary costs
continue. Therefore, if policy uptake complements the physical plants as well as the
stafﬁng and training plans in which the organization has invested, uptake should be
facilitated. Ifthe reverse is true, implementation may be more problematic.

An example of the impact of infrastructure comes from a studies of adoption of
new technologies among Fortune 1000 companies (Ettlie, Perotti, Joseph, & Cotteleer,
2005). Researchers found that companies were more successﬁil at adopting the new

systems if they were not heavily invested in an older technology and had a culture that

 

5 The majority of jobs in government services, as opposed to those in the private sector, are under civil
service protection and in many cases under collective bargaining agreements. It is estimated that more than
90 percent of civilian federal government employees are under civil service protection with similar
estimates across the states. More than one-third of civilian state and local employees are estimated to be
members of a union and covered under a state collective bargaining agreement (Stillman, 1995). In
contrast to at-will employees in the private sector, if a policy eliminates a physical plant or a program. these
protections generally afford the employee either continued employment in another position or an extended
period of time in transition under the legal and procedural processes necessary to eliminate jobs or
programs. Therefore, the cessation of operation of a facility or a program does not result in an immediate
reduction in variable costs (Hyman, 1999). An example of this is the deinstitutionalimtion of the mentally
ill. States attempted to institute policies that would shift both the mentally ill and the ﬁmding to local
providers. However, the infrastructure of state hospitals and staff meant that the variable costs of running
the physical plants and the time required to transition employees off the payrolls continued Little of the

35

favorer

build r
becorr
invest
comp:

inhibit

lnsti:

 

 

favored importing such technologies from the outside rather than building the computer
architecture themselves. This ﬁnding on upgrading computer systems reﬂects two
aspects of organizational investment that may affect policy adoption. The investment in
the physical system may inhibit the adoption of a newer system, even one that promises a
good return on investment. In addition, structuring agency personnel and practice to
build rather than buy technological advances creates a staff resource. That resource
becomes ingrained in the organizational framework. Beyond the more tangible
investments in systems and staff, setting up an organizational protocol for internal
computer system development creates a decision-making and action structure that may
inhibit openness to other alternatives. Organizational theories that distinguish between
mechanistic and organic systems suggest that policy uptake may depend on the degree to
which this internal investment in physical assets and organizational protocols creates an
organizational mindset that options are limited and decisions are effectively
“programmed” (Burns & Stalker, 1995).

Non-programmed decisions involve much stirring about, deliberation,

discussion and often vacillation. . .Programmed decision-making is what it
is because of the existence of an institutional framework.

(Burns & Stalker, 1995, p. 115)
Implementation is eased when the decision to adopt the policy ﬁts well in the
institutional framework. Rather than requiring rethinking and redirecting resources, the
adoption of a policy with requirements congruent with existing structures and protocols
brings the policy adoption choice closer to a programmed decision. This illustrates what

Thompson refers to as “the search for certainty” or the desire to be a determinate system,

 

variable costs. or operating funds were available to pass along to the local providers, thus crippling policy
implementation (Mechanic & Rochcfort, 1990).

36

an orgar
theorist:
(Burns t
systems
which r

1995. p

pohcies
should
more tl
the org
adOpIe
Wright
pnson
crutgrt

Senten

 

liorn
mulrj-
Entre;
Crau.
regeé
PY0\

that

 

 

an organization that can control its own destiny (Thompson, 2005, p. 4). Contingency
theorists offer the homeostatic principle to explain this search for stability and certainty
(Burns & Stalker, 1995). That principle states that while most organizations are open
systems “that any internal or external force making for disruption is countered by forces
which restore the system as closely as possible to its previous state” (Burns & Stalker,
1995,p.28)

Drawing on this concept, if policy uptake involves an expansion of current
policies or just a repackaging of existing policies to meet ﬁrnding requirements, uptake
should be facilitated (Roth et al., 2000). Burns and Stalker (1995) also argue that the
more the policy veers from existing practice and commonly understood goals, the more
the organization must be nimble, organic, and less constrained if that new policy is to be
adopted. In evaluating the adoption of offender reintegration programs, Duffee and
Wright (1990) noted that reintegration programs that could be “tacked on” to the end of a
prison stay were implemented across many jurisdictions (p. 188). These policies were
congruent with “existing programs and did not require extensive changes in the
sentencing patterns or large capital budgets” (Duffee & Wright, 1990, p. 188). Borrowing
from environmental policy adoption literature, researchers ﬁnd that the adoption of a
multi-system approach is hampered by elements of organizational structure, including
entrenched organizational policies and rigid organizational systems (Ma-Wen &
Crawford-Brown, 1999). In a program ﬁrnded by the National Cancer Institute,
researchers found that ﬁve of the six health clinics that were ﬁnancially incentivized to
provide a new health education protocol to their patients failed to do so. Only the clinic

that did not have a health educator on staff until one was funded as part of program

37

smrc
Orga
theor
bure;
2005
comr
orgar
such
\t ell .

&Du

 

implementation implemented the new protocol as intended. This ﬁnding led researchers
to conclude that adoption was realized only when implementation did not compete with
existing routine and protocols (Cunningham et al., 2000). “The structure of the health
departments was such that changes could not be easily integrated into careﬁrlly deﬁned
routines” (Cunningham et al., 2000, p. 23).

The degreeto which both the internal and external environment are formally
structured also has been theorized to affect organizational behavior and policy uptake.
Organizational scholars, building on the work of classical and neoclassical organizational
theories, have investigated the relationship between internal organizational or
bureaucratic structure and organizational behavior (Fayol, 1949; Merton, 1940; Simon,
2005; Weber et al., 2003). Fayol’s (1949) ideas about the division of labor and chain of
command and Weber’s ideas about the systematic requirements and administration of
organizations to achieve efﬁciency, shape current research on the degree to which factors
such as centralization versus decentralization, uniﬁed versus differentiated systems, as
well as size and complexity, inﬂuence the adoption of new policy directions (McGarrell
& Duffee, 1995; Stone, Henig, & Perannunzi, 2001).

Especially in the area of policing, research into policy adoption has investigated
the relationship of organizational structure to policy uptake (Kuykendall & Roberg, 1982;
McGuire, .1997; Roth et al., 2000). Kurkendall and Roberg (1982) in studying the uptake
of team policing (a variant of community policing) found that mechanistic departments
with strict, hierarchical structures were less likely to adopt a team policing approach —

which stresses individual discretion and accountability. More organic and less formally

structure

8; Rober

theorists
be easie
highly h
McGuir
the am
propose
speciali
centrali
policy ;
163me
police 1
that leg
protoc

PTOIO-g

 

the ca

There
ll

t0 ll. '~

 

structured departments were more successful in this speciﬁc policy uptake (Kuykendall
& Roberg, 1982).

These ﬁndings would appear to be in conﬂict with modern implementation
theorists studying collaborative policy adoption who propose that implementation would
be easiest to achieve if all the implementing organizations were similar in structure,
highly hierarchical, and allowed little discretion in decision making (Agranoff &
McGuire, 1998; Bardach, 2001). The intersection of these viewpoints may be sought in
the amount of discretion exercised by various levels of staff and the type of policy that is
proposed for adoption. Research into policy adoption in systems that are more
specialized, decentralized, or fragmented as opposed to those that are more uniﬁed and
centralized indicates that success may require congruence between the nature of the
policy and the system structure, that policy adoption may “vary by the very nature of the
reform itself” (McGarrell & Duffee, 1995, p. 5). Therefore, while one could propose that
police departments with highly formal and militaristic practices would be able to dictate
that team policing be employed, team policing protocols are incongruent with the
protocols in such agencies. However, if the policy being proposed involved a structured
protocol for employing S.W.A.T. teams, both the centralized structure of the agency and
the culture of militarist practice might work together to enhance policy adoption.6
Therefore, there are competing factors to consider when gauging the likelihood of policy
adoption.

This idea of the mission of the policy reﬂecting the system structure also extends

to the policy subsystem, what Dimaggio and Powell (1991) term the organizational ﬁeld,

39

mdnha
Sll 'Tl
parncip:
pubhc e
inthe p.
includir
nakeir
hasbee
bettvee
degree
tshich:
OlgOVt
control
Control
States 5
(\ch;
a§€nc'

techn;

 

lhec:
Subs}

1999

 

and what economists might characterize as the business sector (Handler & Zatz, 1982, p.
81). That ﬁeld or sector encompasses those entities that provide resources or services,
participate in the production process, consume output, or provide substitute products. For
public entities that policy subsystem is generally deﬁned as “the major agencies involved
in the particular substantive area and those responsible for the operation of the program,
including the executive, legislature, judiciary, and various interest groups that have a
stake in the issue” (Handler & Zatz, 1982, p. 81). The structure in the policy subsystem
has been hypothesized to inﬂuence policy adoption. One example is the congruence
between the degree to which the policy requires local decisions and actions and the
degree to which governance of that sector and jurisdiction is federated, or the degree to
which actions, power, control, and accountability are devolved to lower and local levels
of government (Handler & Zatz, 1982). Policies that require more local activities and
control, such as migration of some correctional services and inmates from state-
controlled facilities to community-based pre-release centers, were more easily adopted in
states systems with governance structures that distributed power to local entities
(McGarrell & Duffee, 1995). The attributes of the subsystem also affect the ability of the
agency to meet the technical requirements of the policy under consideration. If tools,
technologies, and training do not exist within the agency, but do exist in the environment,
the cost of implementation will be reduced if such resources exist in the policy
subsystem, compared to the cost if such technologies need to be developed (Hyman,

1999).

 

6 SWAT. is an acronym for Special Weapons and Tactics, or the original title Special Weapons Attack.
Team. Such teams are highly trained to use specialized weapons and tactics in particularly perilous
situations (Wells, Falcone, & Rabe-Hemp. 2003).

40

implen'
ofpoht
uptake
to mee-
lVhile
ﬁnny,
a net g
agency

SOUrce

 

 

In terms of policy adoption, research ﬁndings indicate that the more the new
policy complemented the existing infrastructure and structure of the agency and the
policy subsystem - be that physical plants, technical resources, staff skills, or
organizational structure — the greater the likelihood of ﬁill adOption (Kuykendall &

Roberg, 1982; McGarrell & Duffee, 1995; NI], 2000; Roth et al., 2000).

Goals and Policy Adoption

Congruence between the core goals of the agencies participating in the
implementation and the goal of the incentivized policy is prOposed to inﬂuence the level
of policy adoption. Using contingency theory as a guide, the expectation is that policy
uptake would occur most readily when it increases the ability of the participating entities
to meet their goals and increases the resources available to do so (Burns & Stalker, 1995).
While incentivized policies do, by deﬁnition, bring some new ﬁscal resources into the
entity, the true effect of adoption may depend on the degree to which that policy provides
a net gain or a net loss. If the implementation of the policy degrades the ability of the
agency to perform its central mission or creates problems in the resource ﬂow from other
sources in the external environment, the policy might result in a net loss.

From a very practical standpoint, those policies that increase requirements and tax
the resources of implementing agencies may increase adoption costs and reduce beneﬁts
(Sabatier & Mazmanian, 1980). In a study of problem-oriented policing, implementation
requirements, including increased paperwork, degraded policy adoption as the
requirements sapped resources that were expected to be expended in goal-oriented
activities (Cordner & Biebel, 2005). As noted earlier, if policies require changes in

protocols and infrastructure, costs increase as well and may increase substantially.

41

This also ll
greater am
for a parti
cost of th:
2000i
they will
1982). 'f
are best
(Harris
lﬂdyp
includii
Popular.
been pi
& Reir
agree i

and ar

the at
Soﬁe]

c035

This also leads to the question of whether adoption is more likely if the level of need is
greater and the goal more pressing. Economic theory indicates that if there is a demand
for a particular product or service, supplies will appear to meet that demand, and if the
cost of that service is lowered by a subsidy that product may even more attractive (Frank,
2000). In terms of government policies, subsidies are offered with the presumption that
they will increase the provision of the subsidized good or service (Ripley & Franklin,
1982). For federally-incentivized social policies, research indicates that often programs
are best adopted in those sectors in which the need for the speciﬁc policy is lowest
(Marris & Rein, 1970). Intuitively, congruence with documented need would seem a
likely predictor of adoption. However, other factors that may be related to the need
including lack of consensus on interventions to address the need, consensus on target
populations for programs, and lack of organizational capacity to facility adoption have
been positively associated with high need in problem areas (Clayton et al., 1996; Marris
& Rein, 1970; Morley et al., 2003). For example, jurisdictions that have been unable to
agree on the solutions for educational problems were both areas in high need of reform
and areas least likely to adopt new policies (Clayton et al., 1996)

Just how much the policy and the incentives may enhance agency goals and the
goals of the implementing group are dependent on a clear understanding of those goals.
Cohen (2000) argues that successﬁil implementation is not likely without a consensus on
goals and a clear understanding on the part of the participants about the manner in which
the goal and the proposed policy will inﬂuence human behavior in concert with greater
societal goals. Research on collaborative policy adoption has found that the clarity of the

goals in the coordinating group has been linked to adoption success (Sridharan &

42

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Gillespie, 2004). In research on federally-incentivized adoption of low-income housing
programs, well-articulated goals led to greater implementation success (Basolo, 2003).

However, institutional theorists warn the relationship between the new policy and
the overall mission of an organization or coordinating group may be difﬁcult to discern.
The complexity of goals in the criminal justice system and the difﬁculty of obtaining
consensus on core goals for any policy can be illustrated by the effort to establish goals
for trial court performance. According to the analysis of the Trial Court Performance
Standards (TCPS) process, the working group of court officials was tasked to reach a
consensus of goals for trial courts. The process was contentious and fraught with
political and philosophical disagreement over the core missions of the court (Keilitz,
2000). In attempting to build consensus, the working group compiled seventy-ﬁve
measures of “justice.” Even this complex goal deﬁnition was not sufficient to quell
debate.

Such research, as well an contingency theory and institutionalism, suggests that
complex organizations ofien have competing and conﬂicting missions (Burns & Stalker,
1995). Herbert Packer points to an inherent conﬂict between two core models in the
criminal justice system, the due process model and the crime control model (Packer,
1968). He contents that the tensions in the underlying assumptions and values of these
models helps to account for the high reverberation and seemingly endless activities of
framing and reframing policies. The fact that criminal justice agencies function within
the political ﬁ'amework of representative government lays the foundation for even further
complications. Rubin’s (2000) theories of public budgeting indicate that the perception

of agency legitimacy often is based more on public and political reactions than on proven

43

 

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performance, a premise that aligns well with institutionalism. A policy that calls into
question the legitimacy of the organization could hamper the organization’s political
support that could translate into a loss of funding. In attempting to frame cost-beneﬁt
analysis tools for criminal justice, Mark Cohen notes that not only are costs difﬁcult to
estimate, but that the Costs and the beneﬁts can be perceived by different actors in very
different ways and at different levels. Costs and beneﬁts can be framed in terms of their
costs to society as a whole — as social costs. Those costs also can be calculated as
individual, external costs, meaning the cost imposed on one person through the activity of

another (Cohen, 2000).

Whose Goal is to be Served?

The inﬂow of federal funds to support policy adoption may have wildly different
impacts given the context of that implementation. The more novel the adoption, the more
stakeholders and implementers must bond in a “community of interest” if the adoption is
to be successful (Burns & Stalker, 1995, p. 121). The ability to have this level of
commitment throughout the organization increases the chances of “put-through” or the
ability of the organization to effectively use the resources gathered from the environment
in service to the intended policy (Burns & Stalker, 1995, p. 28). One of the problems
noted in collaborative policy adoption in high reverberation systems that deal with .
wicked problems is that it is often difficult to assemble a uniﬁed community of interest.
How the problem is deﬁned and who is perceived as (benefiting from the policy can
inﬂuence how broadly this community of interest is deﬁned, and if a community of

opposition will arise. These deﬁnitions are inherently political.

44

 

 

 

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Politics here are stronger than the purse.

(Salamon, 1983, p. 9)

In evaluating the effects of federal grant-in-aid on policy adoption at the state and
local level, Salamon (1983) noted that political power exercised at various levels of
government and by various groups could, and often did, outweigh the power of the
federal purse. Research in collaborative policy adoption indicates that when the goals of
federal programs conﬂict with local goals, administrators often ﬁnd themselves expected
to support local attitudes and values as opposed to those espoused by federal policy
makers “or expect to exit.” (Agranoff & McGuire, 2004, p. 505). Political power was
noted as a key factor in research on policy adoption in education (Clayton et al., 1996).
Given the nature of criminal justice policies, that they are wicked problems, that any
change in the manner in which an alleged or adjudicated offender is handled “may raise
important political questions” and “for criminal justice practitioners, there may be
political costs to experimentation” (Weisburd, 2000, p. 181).

In their research and critiques of theories of policy adoption in high reverberation
systems, Stone et a1. (2001) argue that politics is not sufﬁciently integrated into such
studies. While political party afﬁliation or political ideology may be included as a factor
in an equation or a case study, politics as “a way of resolving conﬂicts that cannot be
resolved by other means” is not as easily or as ﬁilly integrated in social science research
and particularly in criminal justice theory. According to Reich (1977), justice and
correctional entities, compared to other public entities, are especially prone to political
pressures, given the range of constituents and beneﬁciaries and conﬂicting goals. In

attempting to frame the complexities of focusing on consensus goals for this sector,

45

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Reich notes that the political discussions of criminal justice goals do not center on the
costs and beneﬁts, but “how the problem is perceived and deﬁned” (Reich, 1977, p. 67).
While economic models have attempted to frame the mission of criminal justice entities
as “maximizing social welfare with respect to the use of scarce resources,” problems arise
around the public and political consensus of what social welfare is and in what manner it
might .be maximized (Phillips & Votey, 1977, p. 89).

Combating crime, therefore, is often just another way of subsidizing

certain economic interests.

(Palumbo, Levine, & Musheno, 1977, p. 86)

Mark Cohen’s (2000) attempts to frame cost-beneﬁt parameters for criminal
justice evaluations points out the issue of beneﬁciaries. He notes that beneﬁts and costs
differentially accrue to — “taxpayers, crime victims, offenders, government agencies, and
so forth” depending on the structure and goal of the policy (Cohen, 2000, p. 273). The
provision of criminal justice policy, ”like the provision of any governmentally-ﬁinded
public good, involves public choice (Hyman, 1999). Unlike a transaction with a private,
for-proﬁt ﬁrm, the person paying for a criminal justice program is usually not a direct
client (Rubin, 2000). For example, taxpayers paying the cost of building and running
government-operated prisons are not often direct clients or recipients of that service, but
rather those taxpayers expect the indirect beneﬁt of enhanced public safety. Public
attitudes toward criminals, the degree to which criminals are constructed as “deviants or
miscreants” has been found to inﬂuence support and ﬁrnding for correctional programs
aimed at treating health issues such as HIV/AIDS (N icholson-Crotty & Nicholson-Croty,

2004). Ripley and Franklin’s (1982) theories of policy implementation stress the power

46

 

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of public and political perceptions of beneﬁciaries and payers, of winners and losers, in
the likelihood a policy will be supported or derailed.

According to these theories, beneﬁciaries of the policy and advocates for those
beneﬁciaries can become important sources of public and political support for policy
uptake. However, that power is balanced by the strength of groups opposing the
beneﬁciary groups or from restricted clients (those who believe that resources channeled
to this group of clients and beneﬁciaries drew resources away from them). The interplay
of competing advocacy groups as well as their relative strength and their access to key
decision-makers have been found to have dramatic effects on the adoption or
abandonment of policies (Hyman, 1999). Using an example of instituting pre-release
centers in the correctional continuum, direct clients would be prisoners. Advocacy
groups for prisoners and better prison treatment could weigh in to support such a policy.
However, in this case, prison system staff also could be beneﬁciaries. If the policy called
for an increase in the number of state-run centers and the hiring of more prison workers,
these individuals and their advocates could stand in support. In contrast, those opposing
this policy could include advocacy groups who believe that prisoners should not have
these (at least perceived) beneﬁts. Restricted clients, or those people who feel they have
been shut out of possible beneﬁts, could also mount opposition. In this example, if state
workers were slated to lose their jobs because the pre-release centers would be contracted
out to private vendors and not state-run, these workers and their representatives or unions

would be restricted clients and likely to oppose the policy.

[In government] deﬁning customers is much harder than it looks.
Different people tend to want different things, and these preferences are

not easily aggregated.
(Kettl, 1995, p. 59)

47

 

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How citizens perceive criminal justice policies, how advocacy and interest groups
react, what media attention is aroused, how legislative decision-makers respond, and
ultimately what political or legal action is precipitated are all dependent on the type of
policy that is proposed. The dynamics of policy uptake for increased victim services will
differ in terms of beneﬁciaries, opponents, restricted clients, and interested groups ﬁ'om
the dynamics if the policy involves prisoner rights. Therefore, any integrated theory that
attempts to capture the factors affecting policy uptake must recognize the dynamics of
this interplay of interests and also evaluate support and opposition to each policy in terms
of the parties most likely to support or oppose it. Research indicates that public support
for government intervention in controlling crime is dependent on the level of perceived
risk from crime (Gerber & Neeley, 2005). That risk may be inﬂuenced by the context of
the public or publics viewing the need for and the appropriateness of the proposed policy
adoption.

The need for a contextual model is highlighted by attempts to use political
partisanship, state political culture, or political ideology as factors in models of criminal
justice policy uptake. To date researchers investigating this relationship have not found
consistent or signiﬁcant relationships and have called for ﬁrrther research reﬁning the
manner in which political attitudes are described and may be inﬂuenced by regional or
cultural contexts (Lieber, 2003; McGarrell & Duffee, 1995; Stucky, Heimer, & Lang,
2005). Similarly, structural factors such as economic development, urbanization
(population density), industrialization, wealth, and geographic area have all been posed as
possible factors affecting policy uptake (Elazar, 1984; Hero, 2003; McGarrell & Duffee,

1995; Stone et al., 2001). Unraveling the relationship between these factors and policy

48

policy uptake has been difﬁcult. Different interpretations of the potential affect of these
factors also have led researchers to propose both positive and negative associations.
Those same researchers also have cautioned that the relationship between these structural
factors and policy support may be confounded with other community-level factors (Hero,
2003; Leiber & Mack, 2003; McGarrell & Duffee, 1995). As an example, urbanization
has been linked, through legal evolution theory, to modernization and the use of more
formal and more punitive sanctions (F eld, 1973; McGarrell & Duffee, 1995). F eld
(1973) found such a relationship in his studies of juvenile court policies, but noted that
local context might confound the results. For example, F eld found that judges of juvenile
courts in urban areas detained more children, but he also recognized that urban areas also
had detention centers as a ready resource, and rural courts did not. In studies replicating
some of the factors employed by F eld (1973 ), Traut and Emmert (2004) found citizen
ideology inﬂuenced the severity of juvenile sentencing laws, but regional differences did
not. A study by Leiber (2003) found less compelling the effects of urbanization on
juvenile court processing, and found citizen ideology signiﬁcant in predicting the severity
of juvenile sentencing laws in the state, but regional differences had little effect. Given
the complexity of the factors proposed to inﬂuence judicial decision, Leiber, like Feld,
stressed the need to investigate structural factors in a richer context (Leiber, 2003).
McGarrell and Duffee (1995) in their study of policy uptake of pre-release centers
for adult prisons, attempted to integrate institutional theory and legal evolution theory to
develop a framework of factors representing the political dimension (incarceration rate),
political culture, demographics (racial heterogeneity), characteristics of the correctional

sector, and the institutionalization of the organizational form. In this research, they found

49

that the number of centers per inmate (as a measure of the effort devoted to community-
based centers versus state prisons) was larger in jurisdictions with a more racially
homogeneous population, where previous centers had existed for the longest time, and
with smaller correctional systems. The number of centers in a jurisdiction was associated
with more professionalism among staff and in states in which the state government
shared more resources with local government. While racial homogeneity was associated
with having more centers per inmate, the opposite was true and signiﬁcant for predicting
the number of centers.

Support or opposition to policy adoption may be related to various racial and
demographic factors. Public support for the death penalty and for changes in the death
penalty differ by race (Unnver & Cullen, 2005). However, these studies also indicate the
need to view demographics and racial attitudes in a broader context. While attitudes
about race appear to inﬂuence public attitudes and support for various criminal justice
policies, social context, such as the level of racial heterogeneity or homogeneity in the
community, have been found to inﬂuence support for criminal justice policies (Chiricos,
Welch, & Gertz, 2004; Eugen, Steen, & Bridges, 2002; Hero, 2003; Jackson, 2003;
Leiber, 2003; Leiber & Mack, 2003; McGarrell, 1993; McGarrell & Duffee, 1995;
Sampson, 1991; Sampson & Laub, 1993; Sampson, Morenoff, & Gannon-Rowley, 2002;
Sampson & Wilson, 1995; Schwartz, 1989). An example is a recent study on the
implementation of restorative justice programs which indicates that those programs were
more often employed in more racially homogeneous communities (Rodriguez, 2005).
Race may affect attitudes toward policy change, as studies show difference between

whites and non-whites. An example is the 2004 study by Chiricos, Welsh and Gertz,

50

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which found that support for punitive policies was associated with racial prejudice, but
was also inﬂuenced by attitudes about crime, political conservatism, and regional culture
(Chiricos et al., 2004).

These structural factors may need to be subsumed under the broader umbrella of
attitudes. Research evidence on the impact of racial heterogeneity as it relates to support
for punitive policies suggests that it may be attitudes - fueled by structural factors - that
relate to policy uptake. Recent studies linking racial heterogeneity in the population to
support for punitive policies indicate that when there are few minorities in the area, the
relationship is not signiﬁcant, but as the minority population increases, it passes a ﬁrst
threshold. Above that threshold, as the percentage of minority residents increases, so
does support for sanctions until a second threshold is reached. When minorities become
a substantial portion of the population, support for such punitive policies begins to
decrease (Leiber, 2003).7 Bridges and Crutchﬁeld’s (1988) research on imprisonment
policies supported the hypothesis of a nonlinear relationship between minority population
and punitive policies but noted that the relationship was more pronounced when other
community-level factors also were present, in this case the degree of racial isolation in
areas of concentrated disadvantage. Social control theory would predict that as a threat to
economic, social, or political interest increases, “the level and scope of social control
increases” (Liska, 1997, p. 50). The social movement perspective, more than
functionalism, would predict this non-linear response, as the conﬂict can be seen not
merely as a threat to the general or majority of the population, but between different

segments in the population that differ in their interests and their power (Liska, 1997).

51

 

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Therefore, the public or political deﬁnition and the support for correctional policy may
depend on the perception of this policy as race-based and on the racial makeup of the
community. The value of these results in building an integrated theory may be the idea
that demographic and macro-structural variables may be researched and associated with
attitudes, but then it is those attitudes that should be investigated for their congruence

with the perceived winners and losers anticipated with the implementation of a speciﬁed

policy.

Civic Capacity

The ability of a community or a jurisdiction to exercise control over law-breakers
or those that violate the peace of the community has been linked to a shared value system
and trust among community members. These ideas are reﬂected in concepts such as
social capital or civic capacity (Clayton et al., 1996; Hero, 2003). Civic capacity differs
from social capital in that it focuses not on interpersonal networks and relationships, but
on actions that have a political and public component (Clayton et al., 1996).

Studies indicate that when community members are willing and able to identify
speciﬁc issues that require action and potential interventions for those issues, policy
adoption is facilitated (Clayton et al., 1996). Civil capacity, like social capital, appears to
be linked to goal-directed interactions. Researchers studying the effects of DARE on
middle-school students found that schools in which parents participated in additional
anti-drug programs, and where community organizations also were involved in substance

abuse prevention efforts, that students were more likely to resist using drugs (Clayton et

 

7 Leiber (2003) suggests that the relationship between racial heterogeneity and support for punitive policies
is a non-linear function He places the threshold for ascent at about one-quarter of the population of the
area, and the threshold for descent at somewhere under one-half of the total population of the area.

52

al., 1996). A similar study of high school students also found that youth who attended
schools in which other drug resistance programs were in place had lowered rates of
substance abuse (Donnermeyer & Davis, 1998). Care has to be taken when inferring
causation in such examples. Were students, families, and communities that participated

more ﬁrlly in the program already at less risk for drug and alcohol involvement by youth?

Goals and Formal Rules

While goal clarity is linked to effective and ﬁrll policy adoption, goal clarity is
often counterproductive in the legislative process. Unclear goals and vague policy
expectations are useﬁrl when attempting to frame formal processes to address wicked
problems. In a high reverberation system, such as criminal justice, the difﬁculty in
reaching a public and political consensus on the goal, the processes, and the beneﬁciaries
of policy often leads policy entrepreneurs, agency heads, and legislatures to draft and
codify vague policies. Nonspeciﬁc language provides a means to gloss over highly
contentious portions of the policy debate and focus negotiations on the over-arching goals
over which there is little disagreement. Assuring public safety, for example, is much less
politically volatile than debating the sentence length for powdered versus crack cocaine.

This lack of clarity, while a potent tool for passing legislation, creates two streams
of implementation problems. First, some statutes are so vague that it is difﬁcult for local
implementers to determine they have the legal authority to implement the federally-
incentivized policy. In Wyoming, a quarter century after the federal policy on
deinstitutionalizing status offenders, administrators and legislators report that state law
still needed redrafting to clearly comply with the federal mandates (CRC, 2004; Runner,

2004). Lack of clarity in the federal policy to be implemented also leads to adoption

prol
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problems. Since the politically-viable language often provides philosophical rather than
practical and measurable processes or outcomes, adoption can be differently deﬁned and
interpreted.

Collaborative policies magnify some of the formal barriers to implementation.
While agencies may struggle with legal constraints to policy adoption, requiring the
participation of multiple agencies and groups, private and public, and from different
levels of government increases potential barriers. Criminal justice systems, especially as
they deal with sensitive information pertaining to victims, juveniles, witnesses, and those
accused but not yet convicted of a crime, are constrained by scores of federal, state, and
local restrictions. In collaborative policies that attempt to incorporate treatment or
community support components, this network of participants can potentially expand to
private and public agencies including health and mental health providers, welfare and
social support agencies, substance abuse testing and treatment facilities, and community
and religious groups. Attempting to determine which group can share what type of
information with others and attempting to reframe rules or laws to allow for information
sharing can be a conﬁrsing and resource-draining prospect for those attempting to fully
implement collaborative strategies. The ability to negotiate through the politically
constituted environment of expectations and constraints is considered by modern
researchers of policy adoption to be a crucial, but poorly understood factor (Agranoff &

McGuire, 2004).

Leadership, the Holy Grail
Leadership is often considered as the factor that will determine if and how well a

policy is maneuvered through the political environment. Leadership has been linked to

54

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sup

(‘1

staff motivation, overcoming barriers, and framing issues in ways that garner public
support. However, a theory that captures the essence of effective leadership - or the
ability to inﬂuence the behavior of a group toward a goal — continues to be the “holy
grail” of organizational theory (Bryman, 1996, p. 276). In studying policy adoption,
leadership appeared as an important variable, but investigators were left with questions
about its relationship to other variables, “how much leadership” is required and how does
leadership relate to other factors in the implementation environment (Handler & Katz,
1982, p. 209)? Several theoretical schools of leadership have developed including the
trait approach, the style approach, contingency approaches, transformational,
transactional, charismatic, visionary, and political leadership (Bass, 1985; Bass & Avolio,
1987; Sims & Manz, 1984; Stogdill, 1948). The ﬁrst two approaches, trait and style, deal
almost exclusively with the individual characteristics of the leader. The later-developing
approaches attempt to frame leadership as a process that exists within and that is
inﬂuenced by context. While differing in the processes that are hypothesized to inﬂuence
group acceptance of the leader’s direction, in all of these theories a common element is
the leader’s ability to manage and provide information that by some mechanism (such as
exchange, value engineering, or inspiration) increases the likelihood that the group will
follow the leader’s direction.

Translating that component into research on policy uptake, Stone et al. (2001)
found that leadership was a necessary but not sufficient condition to predict policy uptake
success. These researchers noted that in the public sector, leaders often have short
tenures and frequently move fi'om one jurisdiction to another. However, transplanting a

leader from a jurisdiction in which he or she has had some success implementing a

55

et‘.‘

an.

3\

particular policy did not assure success in the new arena (Clayton et al., 1996). Similarly.
contextual factors such as organizational structure (especially fragmented organizational
cultures or sub-cultures), legal or regulatory constraint, the economic environment, as
well as social and cultural changes have been found to limit the ability of leaders to shape
change (Leavy & Wilson, 1994; Marris & Rein, 1970). Quinn (1991) conceptualizes
effective leadership as the ability to recognize organizational strengths and weaknesses
and to take the appropriate action to minimize threats and maximize the opportunities
available to the organization. This concept of leadership is one that assumes the effective
leader is a “tenant of time and context” (Leavy & Wilson, 1994, p. 113). Therefore, one
concept of leadership in policy uptake would consider effective leadership in terms of the
leader’s ability to function in a given context (F lelder, 1967).

The concept of leadership also has been expanded and viewed as permeating all
layers of the organization, not only as a top-down process (Bass & Avolio, 1987; Bolman
& Deal, 2003). Leadership and human relations theories have merged ideas of ‘
professionalism, ideal benefaction, commitment, and leadership to frame the idea of
informal leadership at all levels of the organization (Bass & Avolio, 1987; Bolman &

Deal, 2003; Bryman, 1996; Marris & Rein, 1970).

56

CHAPTER 4: THROUGH THE LENS OF RISK

It is a kind of warfare. Although it is possible to ﬁnd policies or changes

that are for the beneﬁt, as people themselves see it, of almost all,

ordinarily, despite some possible common beneﬁts, changes beneﬁt some

people by injuring others.

(Lindblom, 1959, p. 265)

The theory and research on policy uptake indicates both the need to ﬁnd a means
to integrate and assess the often-competing factors that originate from internal and
external sources and develop some framework to judge how those factors interact to
make policy uptake more or less likely. The work of organizational theorists can be
helpﬁil in framing and expanding a means to understand internal and external factors in
relationship to each other. The concept of risk or risk analysis may provide a new lens
through which to view the interplay of these factors.

Risk analysis can be simply deﬁned as “weighing the cost of the strategy against
the potential benefits” (Crocker, 2003, p. 13). While this seems a simple enough idea, the
ability to judge risk is complicated. First, there is the question of certainty. In all but the
simplest analysis, every source of risk and every possible outcome cannot be anticipated
(Machima, 2003). Second, there is the subjective component of risk. Risk analysis
shows much more robust results when studies take into account the particular level of risk
aversion for each decision-maker, although modeling individual risk attitudes remains a
difﬁcult task (Crocker, 2003; Machima, 2003; Pope, 2003).

Despite these hurdles, the lens of risk analysis can bring fresh eyes to the study of

criminal justice policy uptake. Informed by theory and research, those factors

hypothesized to inﬂuence policy adoption can be arranged on a continuum as to their

57

likelihood of minimizing risk to the entity if adopted or increasing costs and liabilities.
Using a framework that allows competing risk factors to be mapped, an image of the
potential risk associated with implementation can be drawn. This provides a starting
point for developing an integrated theory and a model to predict barriers to successful
implementation.

To address the issue of the subjective side of risk analysis, leadership will be
woven back into the framework. As noted previously, the relationship between
leadership and innovation remains a tantalizing but poorly understood concept, the “holy
grail” of organizational theory (Bryman, 1996, p. 276). Using a model developed by
Quinn (1991), effective leadership in policy uptake will be deﬁned as the ability to
recognize risk and either avoid that risk or maneuver the organization into a position

where that risk is minimized.

Risk: The Perceived Need and the Expected Return

Although there are exceptions, ﬁrst and foremost, politicians and those

they appoint, including correctional policy makers are committed to
survival, which usually translated into an aversion to what they perceive
as risk taking.

(Latessa, 2004, p. 547)

Risk can be deﬁned as the likelihood of an adverse event occurring. Risk, unlike
disaster, is related to events that occur with some regularity and, therefore, have a better —
though certainly not certain — chance of being anticipated. For example, risk planning for
a warehousing company would involve preparing for burglary or theft. A disaster plan
would involve a less routine event, such as a tornado or an anthrax attack. While risks

may involve circumstances that occur more frequently, risk is not fully calculable as risk

is dependent on knowledge ﬁltered through perception.

58

The availability and the quality of information available to decision-makers
inﬂuence the perception of risk. Those researchers attempting to understand decision
making in policy adoption ﬁnd that the amount and quality of information may not be
associated in a positive, linear fashion with certainty or goodness in decision-making.
Decision-making strategies have been posed as means to deal with missing, unknown, or
unknowable factors, as well with potentially huge numbers of factors that inﬂuence the
perception of risk in competing directions. Variants of incrementalism, satisﬁcing, and
garbage-can methods all have been proposed as ways for decision makers to reach
decisions in a risk environment (Lindblom, 1959; Machima, 2003). While not yet
adequately tested or proven, the hope of risk and disaster-planning agencies is that expert
systems will be able to aggregate information and provide rich, yet mentally digestible
information for decision makers.

However, decision-theorists and those studying risk analysis caution that risk is
still a matter of perception. Research indicates that the perception of risk is constructed
based on documented information, but also depends on less tangible factors related to
culture, context, and power (Gwillim, Dovey, & Wieder, 2005; Thompson, 2005). An
illustration of risk perception is given by Machima (2003). He poses the simple example
of making an omelet. You have been given six eggs. You crack ﬁve of the eggs into a
frying pan and then receive information that the sixth egg has a thirty percent chance of
being spoiled. Do you risk adding the egg to have a bigger omelet or do you stop with
the certainty of a ﬁve-egg breakfast? Assuming that some people would break the egg
and others would not, the question becomes how well can we model the competing

factors that inﬂuence any one omelet-maker’s decision to break or not break that ﬁnal

59

egg? Machima cautions that risk involves uncertainty, that the perception of risk may be
based on differences among people in their calculation of the likelihood of some event
occurring based on their beliefs. Beliefs are difficult to represent in a statistical model,
and perceptions of costs and beneﬁts are state-dependent, or dependent on the perceived
source and context of the risk.

An assessment of the effectiveness of personal decisions on risk is most certainly
context-dependent. Investment strategies, insurance plans, even decisions as to whether
or not to buckle a seatbelt bespeak the differences in perceptions of risks and beneﬁts
among individuals. When looking at the context of the working of a single public
agency, decisions on the policy to employ and the level of implementation can no longer
be assessed only in terms of their congruence with personal perceptions of risk. Effective
leadership in policy adoption efforts may be tantamount to the ability to judge the risks
and beneﬁts accrued by various levels of adoption in the context of the entire
implementation environment. Even in a single public agency, communication ﬂow
among staff and stakeholders is necessary in a ﬂuid environment in which
communication about policies both informs and shapes the perceptions of risk and

beneﬁt.

Limiting Risk
We make trade-offs in our personal and business lives between the burden

of risk exposure and the cost of risk mitigation. Financing the costs

associated with a bad outcome becomes the question.
(Crocker, 2003, p. 13)

Loss prevention involves tactics to limit the chance of a negative outcome

(Crocker, 2003). Decisions about whether to invest in policy adoption may depend on

60

the expected losses compared to the expected gain associated with adoption. One facet of
federally-incentivized policy adoption that is assumed by federally policy framers to
inﬂuence that perception of risk is the infusion of federal funding. This incentivized
policy adoption has the potential of lowering risk through cost-shifting (Crocker, 2003).
Research into the adoption of tax policy provides some insight into efforts to manage risk
in a context that includes the possibility of cost shifting. A study of the local imposition
of a sales tax by local governments in Georgia was more likely in jurisdictions with a
high property tax burden that could be off-set by the sales tax, providing what the
researchers labeled “motivation” (Zhao, 2005, p. 721). However, such motivation to
reduce taxes was insufficient to predict adoption. The model was improved by the
inclusion of the potential beneﬁt of shifting the burden cf the tax to non-residents (such
as visitors and tourists) and the inclusion of the perception of risk posed by tax
competitions

In assessing perceived risk and its effect on decision-makers, Gwillim, Dovey,
and Wieder ((2005) contrast “push” factors, or factors that enhance the perception of the
value of adoption, in relation to inhibiting factors. Mental calculations in the mind of the
decision-maker determine his or her level of perceived risk (Gwillim et al., 2005). These
researchers point to the power and autonomy of the decision-maker as a key component
in this risk evaluation. Linking back to both contingency and institutional theories, the
more the decision-maker is dependent on others for survival, the risk is evaluated in

relation to the need for support and legitimacy with those external entities.

 

8 Tax competition among jurisdictions involves the threat that if residents are able to move or shop
elsewhere, lower taxes in one jurisdiction will make living or shopping there more attractive. Where high
tax competition exists, raising taxes creates a risk of a lose in revenue, rather than a gain, from the tax
increase.

61

Hierarchical, Closed-System, Multi—agency Adoption

Returning to Wildavsky’s (1971) idea that federally-incentivized policy adoption
is modeled after the Performance Base

Figure 1. Simple Hierarchical

Budgets System, the most simplistic Collaboration
model of collaborative, federally-
incentivized policy adoption involves
adoption among government or non-

governmental entities that are similar in

structure, are hierarchically controlled,

 

and allow limited discretion in the actions

of staff. Policy adoption, in this highly simpliﬁed model, requires only that staff of each
agency modify their actions in the context of their existing workplace. As Wildavsky
argues, this type of policy adoption would be possible when the goal and the protocols
were commonly understood and held among all the adopting entities, and where
oversight and control of such entities provides sufﬁcient power to limit discretion and
variations in interpretation in adoption.

Figure 1 illustrates this simplistic decision-making collaborative. In this sort of
collaboration, policy is coordinated at the top level of the entity, communication is top
down with little or no room for discretion by staff. An example of such implementation
might be a declaration by the federal government that a certain drug should be considered
a controlled substance. States and local jurisdictions, as well as public and private

entities, have well-established protocols that govern the distribution of such controlled

62

substances. The oversight mechanisms and the risk faced by entities for subverting such

a policy make decisions for such adoption fairly clear—cut.

Matrix or Process Structures

Collaborative policy often involves an implementation group that mirrors the
organization structure described as matrix or process structures (Rainey, 2003). These
collaboratives involve the creation of a

Figure 2. Matrix and Process Structures

task group to plan and implement
adoption (Figure 2). These groups, even
within the context of one organization,
can be problematic if the authority over

the task group members is not clear.

This can be particularly problematic in

 

task groups assembled fiom various
organizations that receive competing or contradictory messages from the home

organization and the task group.

Goal and Governance Congruence and Partnership Competency

Collaborative policy groups also may include members who differ in terms of the
centrality of the policy goals to the mission of their particular organization. While a
policy may be envisioned as implemented by a task group that is inclusive, some
members of the group may view the goals of the groups as somewhat peripheral to the

mission of their home organizations. Figure 3 illustrates this incongruence of central

63

goals. In this case, a collaboration of three agenices - a city school district, a city police
force, and the sheriff from the county in which the city in located — are incentivized into a
task group to address gun crime. The control of gun crime is much more likely to be one
of the major goals of a sheriffs department and the city police force, and they address
this goal in an overlapping area. In contrast,while student safety and juvenile crime are,
no doubt, concerns for the city school system, goals related to the educational
achievement of students are the core goals and legal requirements central to the school
system. Research on collaborative policy adoption refers to partnership competency as
the ability of these member organizations to work together toward a common goal

(Bardach, 2001; Weber et al., 2003).

Figure 3. Example of Goal Foci Incongruence

 

 

 

 

    

 

 

 

 

 

City Schools City Police County Sheriff
County Gun County Gun /CountyGun
Crime Control Crime Control . .~ l Crime Control I}: ~.

City Gun /CrtyGun\ Cit-313%
Control \Control Control

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Borrowing from research on environmental policy adoption, this partnership
competence, is proposed to be a ﬁrnction of both horizontal and vertical competency
(Weber et al., 2003). Vertical competency is the ability of a group to meet the goals and
expectations as drafted in the policy. Horizontal competency is the ability to organize a
collaborative effort in the local jurisdiction for support and implementatation of the

policy. Horizontal competency can be conceptualized as including all members of the

64

affected community or jurisdication, or only those agents who are directly involved in the
policy adoption process. For the purpose of the development of an adoption model
horizontal competency will refer to the ability of the implementing agencies and groups
and will not extend to public reaction and support. That public reaction and support will

be considered as a factor affecting risk decisions of the implementing agencies and

agents.

Networks and Information Flow

In keeping with theories of organizations as open systems, for a task group to
function, it must be able to take in energy and convert that energy to some useful purpose
in order to complete the cycle by justifying continued investment. A simple model of
collaborative interaction might suggest a dense and highly reciprocal network (Scott,
1991). In such a model, all members of the group would communicate with all other
members and would provide as much information (outdegree) as they received
(indegree). In practice, collaborative groups appear to be highly unbalanced in terms of
density and information sharing. Sridharan and Gillespie’s (Sridharan & Gillespie, 2004)
network analysis of collaborative problem-solving groups tasked to implement the
federally incentivized juvenile crime prevention program indicates inequities in the
frequency — or density — of contacts among groups members. Some members indicated

no contacts at all.

Risk and Investment
One of the major hypotheses offered here is that members of collaborative policy
adoption groups will differ in their perspective of the risk and beneﬁt of participating in

the adoption of the proposed policy. Given that, and the theories and ﬁndings on the

65

factors and processes that inﬂuence collaboration, some group network formations are
posed as most likely given the nature of criminal justice policy.

One step in building understanding of the interaction and investment of group
members in these collaborative groups is to explore potential models of participation and
information exchange. Building from recent models of collaboration, three models of
PSN collaboraton are proposed. The ﬁrst model is the bowtie model, borrowed from
cellular biology. The second is recognition-based decision making and a varient of that
from social networking theory on ego-centric models. The third is a model of symbolic
or non-productive interactions.

While models are proposed as discreet representations of interactions and
investments of group members, this is an oversimpliﬁcation. Groups, for the most part,
probably do not possess all the characteristic of one model or another. This
categorization is meant to provide a framework for describing and predicting group
behavior. Borrowing from cellular biology, theory would predict that information and
resources ﬂowing into a fairly complex system and from different sources requires some
mechanism to manage the information and energy ﬂow. The cellular biology model is

called the “bowtie” model (Csete & Doyle, 2004).

Dealing with Interoperability Problems

Those studying organizational behavior in collaborative groups have turned to
cellular biology for a model that appears a useﬁil heuristic for group interactions. As
noted previously, projects such as PSN ideally involve members from organizations with
very different structures, from different levels of government, from public organization,

from community groups, proﬁt and non-proﬁt entities, with different core goals and

66

 

missions, with varying resources, and different levels of power and autonomy. These
differences can be thought of as creating problems of interoperability. While originally a
term used to describe the ability of communication systems to work together seamlessly,
the deﬁnition of the term has been expanded to mean the ability of humans, rules,
protocols, and management information systems to exchange information without
barriers. For collaborative task forces, such as those in Project Safe Neighborhoods
(PSN), interoperability issues can be legion. The more all human, legal, and mechanic
resources are interoperable, the less the cost of participation for collaborating members
and the less the risk faced by participants.

A truly interoperable collaborative would have law and rules that do not inhibit,
but actually promote, information sharing among members, common deﬁnitions for
variables of interest to the collaborative, common protocols for data collection, storage,
and analysis, congruent administrative policies and practices, and management
information systems and other resources that have complementary architecture. In
practice, completely interoperable systems within as varied a group as promoted by the
PSN initiative would be highly unlikely. Therefore, the three models posed here have
been theorized and found to be strategies that collaborative groups use to reduce the
investment cost and the risk of participating in these cross-entity policy adoption

processes.

The Bowtie Model

This architecture uses selective homogeneity. . .to facilitate control,
organization and management of the enormous heterogeneity in enzyme
speciﬁcity, action and regulation, and in substrate size, ﬂux and
concentration. All modern technologies, from manufacturing to the power

grid to the internet, are organized in bowties.
(Csete & Doyle, 2004, p. 447)

67

While very small groups with simple tasks and infrastructures might be able to
function as a dense social network in which information is shared in a fairly symmetrical
pattern, this investment becomes costly and cumbersome as complexity and membership
grows. To model the interactions and investments of complex, collaborative groups,
theorists turn to cellular biology (Csete & Doyle, 2004). The bowtie model of
information and energy exchange is so named for its shape. Information and energy ﬂow
in from a variety of sources to a hub -— or knot of the bowtie — and then fan out again in a
usable form. When adapting this theory of resource transformation from cells to
collaborative groups, one can consider the vast amount of data, ideas, needs,
requirements, goals, and resources that can be brought into the collaborative by various
members. While potentially useful to the collaborative, these factors may be non-
interoperable. A simple example would be data collection. In a given jurisdiction, the
sheriff might gather data based on individual offenders and keeps that data in paper ﬁles,
the criminal court might code by individual case and keep information in a hierarchical
database, and the corrections department for the state might code each inmate on his or
her most severe committing offense and store that information in a proprietary relational
database. While data sharing across these systems might provide a strategic resource to
the collaborative group, there is. a cost to making the information interoperable. In terms
of human interoperability, a community member or representative of a faith community
might have difﬁculty understanding the wealth of acronyms and the bureaucratic norms
that might be more easily communicated among state and law ofﬁcials. Conﬁdentiality
or due process requirements might be part of routine functioning for some members of

the group while completely foreign to others.

68

In order for these needs, requirements, and resources to be gathered in and
transformed into a form that powers the work of the group some mechanism —- the knot of
the bowtie — needs to be functional. Whether that mechanism is a person or group
formally or informally assigned, some collaborative infrastructure needs to be in place to
decrease the cost to members of this information and energy exchange. In the Quinn
( 1991) organizational model, this would be related to the organizational competence and
infrastructure. As data deﬁnitions, rules, communication structures, and protocols
become routinized for the group, the group is best positioned to perform as a high-
functioning organization. Groups following the bowtie model of collaboration would be
expected to be associated with a more inclusive, multi-ﬁrnctional task group and with the
greatest ability to act strategically.

Csete and Doyle (2004) note that this model is both robust and fragile. The
ability to draw in

Figure 4. Fan Model — Based on the Bowtie Model
resources from disparate (Csete & Doyle, 2004)

forms and translate it into

. . Participants in the Collaborative
something that rs more Group

universally useful

provides entities (cells in 3.3,,

their research) with a

potent agent. However, if

   

 

this central hub is —'> Information

inﬂow
4 - - - - Information
outﬂow

missing, damaged, or

 

 

 

non-functional the system

69

 

fails. As illustrated in Figure 4, for the purpose of looking at problem-solving networks,
the model is perhaps better pictured as a double fan. The information would fan into a
central source (or knot) but then return (fan out) in its modiﬁed form to the same

participants.

Recognition-Based Models

Given problems with interoperability in a group with a diverse membership, one
way to lower the cost of participation is to edit the group membership to those best able
to communicate and participate in a recognition-based decision making model. Those
researchers studying collaborative groups that attempt to respond in cases of high risk or
disaster note that effective, yet
Figure 5. Example of a Recognition-Based Group

narrow responses can be

expected from recognition-based

School

decision-makin on 5. These . .
g gr p Administrator

groups lower the cost of

 

information and energy sharing

 
   

by limiting information and

  

Probation
Administrator

  

Police Chief

 

activities to those for which

  
 

patterns of communication and

   

Detention
Center
Administrator

use already exist. An example

 
   
 

from risk and disaster response
literature is the recognition-
based decisions of ﬁreﬁghters (Comfort, 2002, , 2005). Fireﬁghters, be they state, local,

federal, inmate ﬁreﬁghter or professional, have common tools, language, training, and

70

protocols. Brought together, even on foreign territory, members and teams have the
ability to make decisions based on shared perceptions of risk and beneﬁts, with the cost
of information and decision-making lowered through a shared vocabulary, and common
policies and practices.

A collaborative policy adoption team that followed the recognition-based
decision-making model would ground their interactions in shared experiences. This
would mean that while this type of network would be open to information search and
analysis, the limitation would be the tendency to focus on information with which the
members have the most shared interest and knowledge, thus limiting problem deﬁnitions
and potential solution to those that align with the group focus. If this were not the case,
the costs of interaction and information would rise and the value of the recognition-based
decision-making strategy would be lost. So, while this interaction pattern beneﬁts those
with shared experiences, it would isolate members and increase the costs of information
and participation for those outside the recognition-based decision-making group. As
illustrated in F igure 5, this isolation of members who have dissimilar experience bases
has been noted in studies of policy adoption for juvenile programs in which groups of
juvenile justice system professionals and school officials were incentivized to merge into
a policy group. In general, participation by school ofﬁcials were peripheral to the policy

adoption processes (Lerman, 1975; Morley et al., 2003; Sridharan & Gillespie, 2004).

The Symbolic Group
In the symbolic network, members do not make meaningful investments in
information exchange and decision-making. They may be physically present and engage

in discussions, but a means of transforming individual entity inputs to energy and

71

information that canand is used to drive corporate decisions and actions does not occur.

The participation levels and the information may be symmetrical, as portrayed in Figure

6, or widely asymmetrical. Symbolic Figure 6. Symbolic Group

policy adoption groups may report / ,..
policy adoption in public statements \ I,
\
. . ,4- ----- y,“ .....
or required documentation but may M r' . . ~
g; _,' Drscussron <3: - - -,;,
jis: -' 5‘: ; 71,7 ‘\ ' i .....
decouple those goals from actual “-1”--- {i’
M,’ Vt”
. processes and practices.

Risk and Policy Adoption

Risk analysis provides a different lens, a different perspective from which to view
incentivized policy adoption. Some implementation or adoption research has asked if the
system is open to change, sometimes linked to public attitudes and political windows of
opportunity (Andersen, 1999; Kingdon, 1984; Miller & Ohlin, 1981). Policy adoption in
criminal justice often has been linked to major, and generally negative, events. Storied
examples are Jerome Miller’s use of abuse in training schools to close those facilities in
Massachusetts and the adoption of Boston’s Operation Night Light, a joint police and
probation effort launched in response to a rash of homicides of and by juveniles (Jordan,
1998; Schwartz, 1989).

Through the lens of risk, adoption is linked to a balancing of factors. In the case
of a pressing problem, the risk analysis perspective suggests that the entities that could
become involved in the policy adoption would choose to participate in policy adoption if
they believed that this would lower risk. In extreme cases, such as the abuse of

institutionalized children or a rash of homicides, some investment cost in policy adoption

72

is generally assumed to be a good tradeoff if it has the potential to lower an immediate
and high risk situation.

In incentivized and collaborative policy adoption, risk analysis provides a means
to model two facets of adoption decisions — how decision-makers balance the risks of
adoption versus the risks of non-adoption, and how such decisions could be
conceptualized in a group setting. Risk of implementing, which is deﬁned as the
perception of the potential costs versus beneﬁts of adoption, can be conceptualized as
dealing with four major factors. Those factors are illustrated in Figure 7. One decision

area is the need, including the symbolic need, for the product or service promised by the

policy goal. The second is the Figure 7. Risk Decision Factors

perception of the costs associated with

adopting that policy, given the current

status of the agencies that will be tasked Support Survival
to that adOption. The third is the level of

opposition or support for the policy Ability Need
within the agencies and outside the

agencies that could increase the costs

and liabilities of adoption or ease and

 

support it. The fourth is the perception of the potential gains and losses in power and
resources that may be associated with policy adoption. The risk decisions can be
summarized as 1) Is this policy answering a need? 2) How much will in cost to
implement it? 3) Who will support it, and who will oppose it? and 4) Will it increase or

diminish the agency’s power and resources.

73

In terms of collaboration among agencies, the decision of any one agency to
invest in the policy involves the assessment of risk, in terms of the factors noted above.
In addition, participating in the task group or task force implementing the policy involves
risk — or costs — as well as potential beneﬁts. The decision-making models outlined
earlier in this chapter provide three different means of balancing the risks of
organizational participation (Figure 8). In the symbolic group, members limit risk by
non-participation, balanced against no expectation of actual beneﬁt from policy adoption.
In the recognition-based decision group, members reduce the costs of participation by
limiting group membership to those with common protocols and goals. The task group or
task force can develop into its own organizational form, but the beneﬁts of a truly
strategic policy cannot be realized. Bowtie model groups reduce the cost of member
participation, not by limiting membership to those with similar schema, but by creating
an organization that can collect and translate information. The costs of participation in
this type of task force are balanced against the perception of ﬁrll and strategic policy

implementation.

Figure 8. Risk Decisions for Group Participation

 

 

. Organizational
35.22:; Eisner“
Force
Symbolic Group No N0 N0
Recognition-Based Mixedec/res and “mm Narrow
Bowtie Model Yes Yes Strategic

74

The next question in creating a model for policy implementation, grounded in the
analysis of risk, is what factors in what contexts inﬂuence decision-makers perceptions of

the risk, the costs, and the beneﬁts, of participating in policy adoption.

\I
Ur

CHAPTER FIVE:
A MODEL OF INCENTIVIZED POLICY ADOPTION
Framing the Factors

Hence, implementation efforts can vary according to the composition,

disposition, and interaction of the actors and the conditions of the

envrronment.

(Nakamura & Smallwood, 1980, p. 23)

One of the difﬁculties in studying the behavior of criminal justice entities — or any
organization — is developing a means to categorize the organization ﬁrlly enough to build
a rich proﬁle of the entity, detailed enough to make research results meaningﬁrl and
generalizable. In any organization, but especially given the highly politicized nature of
criminal justice organizations, organizational values and characteristics may compete and
conﬂict (Quinn, 1991; Reich, 1977). Those researching criminal justice policy have
identiﬁed the need to utilize an organizational model that captures the competing factors
present in and around the organization (Cochran, 1992). Bolman and Deal (2003) have
proposed that organizations be evaluated in four frames: structural, human resources,
political, and symbolic. Reﬂecting much of the same structure, Quinn (1991) developed
a model that attempts to capture the major characteristics of organizations and represent
the relative power of those characteristics, and importantly, represent those characteristics
in relation to one another.

The Quinn model provides a framework for developing a means to identify and
analyze sources of perceived risk. The competing values described for the purpose of

developing a model of risk perception within the organization are taken directly from

Quinn (1991). The external model of competing factors and the risk continuums for both

76

the internal and external model use Quinn’s model as a base and attempt to integrate

theoretically-based factors into an expanded framework to model risk in policy uptake.

The Quinn Model

The model Quinn (1991) developed outlines four quadrants of factors that he
found inﬂuenced the functioning of an organization (see Figure 9). When assessing the
functioning of one organization, Quinn found that the ability of that entity to balance four
areas leads to high organizational performance. Those four focus areas are:

1) output or productivity,
2) continuity and stabilization,
3) human relations, and

4) survival and expansion.

 

 

The Quinn Figure 9. Competing Values Model (Quinn, 1991)
model would predict Focus: Focus:
human relations survival & expansion

that an organization

Commitment Adaptability

with 1) well-deﬁned

- - - Resource
needs and agreed-to PanlClPa.M\ Acquisition
\ /

goals, 2) a stable and

 

 

 

 

A

 

 

 

 

 

a ro riate
pp p / \gmducrivny
. Technologies
rnfrastructure (roles,
. . Structure Goals
rules, and polrcres) and
Focus: Focus:
information system, 3) continuity & stability output

 

 

highly committed and prepared members, and 4) with few constraints on adaptability and

access to funding and political capital is a high ﬁrnctioning organization.

77

Quinn has developed a series of instruments and a scaling system to analyze and

 

display the effects ofthese Figure 10. Chaotic Adaptive Organization '

competing factors on an

organization’s performance. Commitment Adaptability

     
 
 

As an illustration, Figure 10 Participat'on _ . Resource
' ‘ Acquisition

is Quinn’s display of scaling

 

 

of an ineffective organization
that he characterizes as a Technolog1e\

chaotic adaptive organization Structure Goals
Adapted ﬁom Quinn, 1991, p. 98

Productivity

 

 

(p. 98). This scaling

 

illustrates organizational dysfunction based on overinvestment in staff issues and eﬂ‘orts
to garner resources in comparison to the investment in setting and meeting goals and
building the infrastructure needed to support the processes needed to produce goods or
services.

While Quinn designed this tool to help leaders and planners diagnosis
organizational dysﬁmctions and move toward high performance within one organization,
the model is a valuable starting point for identifying, organizing, testing, and displaying
the manner in which competing factors affect the functioning of various levels of
agencies and collaborative agency task forces engaged in incentivized policy adoption.
The sectors of the model align with the four major questions proposed as inﬂuencing risk
decision — does this meet a need, what costs are associated with meeting adoption, who

will support or oppose, and how does this inﬂuence resources and survival?

78

However, while this model provides an attractive base upon which to investigate
the inﬂuence of competing factors, both current scholarship and current federal ﬁmding
programs stress the need to integrate programs, policies, and practices to address complex
problems, such as gun violence, that cut across the jurisdiction of many agencies. The
crux of the problem in understanding implementation by a task force is what inﬂuences
the willingness and ability of members to create and participate in this collaborative
venture, and what hinders such collaboration. The model, outlined in Figure 11, indicates
the level of policy implementation will be based on decision-makers’ perceptions of risk
or beneﬁt, based on competing factors at three levels: external environment, within the
internal environments of participating entities, and within the task group. Within each
level, competing factors in the same four focus areas are hypothesized to inﬂuence the
degree to which members can and do invest in the task force. The challenge is then to
use, expand, and translate the framework suggested by Quinn (1991) to address the
various levels at which competing factors inﬂuence the risk associated-with policy

adoption.

Figure l 1. Model of Implementation Based on Task Force Dynamics

 

 

 

 

 

 

 

 

   

 

 

 

 

 

 

External
F __ ,. __
actors L Factors [ L l f
in e as
Factors within Group —-+ t1: Task [ .
Participating roup
Agencies

 

 

 

Viewing the Quinn model through the lens of risk analysis, a competing factors

model for such collaborative policy adoption would involve the scaling factors in four

79

major categories — or sectors — and at the three levels noted in Figure 11 — external,
within agencies, and within the task force. The four sectors proposed in the model are:
1) Needs and Products
2) Structure and Infrastructure
3) Human Factors

4) Resources and Survival

The External Level of the Model

At the external level, the competing factors that would be identiﬁed and translated
into variables are the system’s current need for the goals offered by the incentivized
policy, and its current ability to meet that need. The second area of stmcture and
infrastructure would assess the congruence between the structures and infrastructure in
the jurisdiction with those required for policy adoption. In the human factors sector,
public attitudes and pressure for or against the policy would be scaled. In the survival
sector, the ﬁscal and tangible resources offered as incentives as well as the political gains
or losses would be charted to complete the image of external competing factors.

To illustrate this, a simpliﬁed model of external factors gleaned from the literature
on deinstitutionalization of status offenders is presented in Figure 12 (CRC, 2004;
Handler & Katz, 1982; Handler & Zatz, 1982; J. Katz, 1982a, 1982b; Runner, 2004;
Schwartz, 1989). Four factors are constructed for the states of Wyoming and
Massachusetts. The four external factors proposed to inﬂuence perceptions of risk or
beneﬁt in policy implementation are the number of status offenders, the population
density, public attitudes toward deinstitutionalization, and the ﬁinds provided as an

incentive. Since ﬁinds were offered on a per capita basis, Wyoming’s ﬁscal incentive

80

were far less than that offered in Massachusetts. Also given the low population of the
state, the actual number of status oﬂ’enders in Wyoming was relatively small. The states

also diverged on public support and geography. Massachusetts is a compact state with a

Figure 12. Illustration of External Competing Factors in Deinstitutionalization
of Status Offenders

 

Attitudes Resources Attitudes Resources

 

 

 

Infrastructure Inﬁastructure Need

 

Wyoming Massachusetts
population density per square mile of 820 residents. In contrast, Wyoming’s population
density is 5 residents per square mile. Preceding the initiation of this federal incentive,
Massachusetts had barraged a public relations campaign by the juvenile corrections
director and heavy media coverage of abuse of children in institutions and the need for
deinstitutionalization. Wyoming residents had long supported a wise parent model of
juvenile justice allowing local judges to determine placements and sanctions for local
children. In statements that appear to reﬂect at least a tacit risk analysis, Wyoming
ofﬁcials noted that accepting the buds could create a net negative result for the state as
there were too few status offenders in any one area to make the creation of alternatives
economically efﬁcient (CRC, 2004; Runner, 2004). With the perceived negative ﬁscal

outcome coupled with, at best, public indifference toward the problem as conceived by

81

the federal policy framers, leaders in Wyoming did not even attempt a pilot period or
ceremonial adoption, fearing that the risk of either was too high. In contrast,

Massachusetts was an early and SUCCCSSlel adopter.

Within Agency Competing Factors

The second level of competing factors, those within agencies, again categorizes
those factors in the same four sectors. However, the focus of those factors shifts to
factors that are proposed to inﬂuence each agency’s perception of the risk of adoption.
This model diverges ﬁ'om the Quinn organization model in that it attempts to capture the
relationship between the policy and the functioning of the organization, rather than focus

strictly on organizational ﬁmctioning. For example in the goals sector, the question is not

 
   
 
  
 

whether the Figure 13. Within Agency Competing Factors Model
agency has Interest Group Constraints
goals and Attitudes
universally R
esources
eed Staff
agr —to Attitudes
goals, but
whether the
C Productivity

ongruence
goals 0f the with Structure
agency are

. Competency Goal Congruence

congruent Wlth
the policy goals.

Figure 13 illustrates the Within Agency model of competing factors. In the

context of each policy, factors would be identiﬁed to determine the extent to which each

82

agency perceived risks in each of the sectors. In the goal sectors, the factors proposed to
inﬂuence the perception of adoption risk are the degree to which the goal of the policy is
congruent with the goal of the agency, and the degree to which that goal is or was not
already being met. In the structure and infrastructure sector, the ability of the agency to
meet its basic operating needs is proposed to affect the perception of risk endemic in
attempting to implement novel strategies. The congruence of such structural components
as existing programs, physical plants, skills sets, protocols, and information management
systems are proposed to inﬂuence the perception that the new policy is more or less
costly to adopt. Human relations risks pertain to staff and professional group support for
the policy and the support or opposition to the policy by the agency’s constituent groups
and those interest groups that attempt to affect agency policy. In the survical sector, the
extent to which the agency is able or constrained or encouraged by law or rule to adopt
such a policy as well as the potential resources garnered by the agency are proposed to

inﬂuence the perception of beneﬁt or risk.

Competing Factors in the Task Force

To access the factors inﬂuencing the ﬁmctioning of the task group, the Quinn
model could be most directly applied. Since the premise of the overall model is that a
high ﬁmctioning task group should be associated with ﬁill implemenation, the task force
can be assessed much as any other organization. This organizational assessment also
includes an assessment of leadership. It is hypothesized that groups may reduce risk by
not forming a meaningful task group, limiting membership to those with similar
professional schemas, or enhancing task force operations to allow for broad membership

and broad data analysis. In the goals sector, the factors relate to the degree to which the

83

task group shares clear goals and has the sense that those goals are being accomplished.
In the structure sector, the formation of standard practices and standardized membership
along with the ability to collect and disseminate needed information are assessed. The
level of participation and the commitment and morale of the task force are measured in
the human relations section. In this sector, the willingness and ability of the leader to
invite in and enage a broad range of members is assessed and will be related to the
decision making group models. In the survival sector, the factors to be assessed are the

ability of the task force to adapt and innovate and to gain resources — political and ﬁscal.

Testing and Utilizing the Model

To ﬁrlly test, improve, and utilize the model, factors in each sector of each level
of the model would need to be indentiﬁed and translated into a measurable variables.

The inﬂuence of each level of the model and of the aggregate model would need to be
analyzed. To truly build a model of adoption success, theory would dictate building an
understanding of the perceptions of risk at all levels of the model. However, from a
policy-maker’s perspective, the external competing factors may be the most critical
component of the model.

For those framing incentives the most accessible measures would be those in the
external environment. Ofﬁcial data on needs and products, infrastructure and structure,
public attitudes, and resources are much more easily obtained and less sensitive than
information about the ﬁmctioning of and dynamics within agencies proposed to be part of
the collaborative effort. While similar information about the ﬁinctioning of the task force
charged with implementing the policy would be valuable to judge the factors inﬂuencing

adoption, this could not be done until the task force was in operation. Therefore, if the

84

external competing factors component of the model provides some predictive capacity, it
would be the portion of the model most easily and readily employed by legislators and
federal agency leaders as they attempted to frame incentives and determine the best
candidates for funding. Ifthe external factors component of the model has some
predictive capacity, it could be used to craft incentives targeted at those jurisdictions with
a higher probability of policy adoption and to framing alternatives for those distrists
facing barriers to success.

Given the difﬁculties deﬁning policy adoption in the environment of a ﬂexible,
incentivized, criminal justice policy, this study will be limited to construction of
measures of policy adoption success and the investigation of the extent to which the
external factors within the greater model can predict the level of adoption. The model
will be tested on Project Safe Nieghborhoods (PSN), the federally-incentivized policy
aimed at reducing gun violence. This study will will examine the extent to which
patterns of adoption can be found to support the theorized interaction of collaborative
groups. The possible policy implications of both the external factors on policy adoption

success and the patterns of adoption will be explored and discussed.

85

CHAPTER SIX:
RESEARCH CONTEXT, DATA, METHODS,
AND ANALYTICAL STRATEGIES
This chapter will describe the context of the study, the hypothesis and research
questions, and the methods that will be used in an attempt to test the model. How

concepts in the model will be linked to variables, the collection of data, and the

construction of those variables will be discussed.

The PSN Initiative
Project Safe Neighborhoods is a nationwide commitment to reduce gun
crime in America by networking existing local programs that target gun

crime and providing those programs with additional tools necessary to be

successful.
(OJP, 2005b, p. 1)

Funding and Organization

Federal funding was allocated to ﬁrnd Project Safe Neighborhoods (PSN)
beginning in 2001. The funding was intended to support hiring additional federal and
state prosecutors, target juvenile gun crime, distribute gun locks, promote community
outreach efforts and train law enforcement and other public and private members in the
Project Safe Neighborhood (PSN) programs (USDOJ, 2001). From the inception of the
program until federal ﬁscal year 2006, PSN implementation groups were required to
expend funds to contract with a researcher partner or crime analyst and with a person or
entity to provide a media outreach and a community engagement program.

Funds are distributed by the US. Department of Justice to the ninety-four federal
judicial districts through the ﬁscal agent. Accountability for the program and program

funds rests with the United States Attorney in each of those districts. A task force of at

86

least three members was to be appointed by the US. Attorney to decide how the funds
are dispersed. One person is to be designated as the project manager for each federal
judicial district. Funding for 2003 ranged ﬁom the lower bound of $285,000 and was
capped at $1,300,000 per district (BJA, 2003).
National and Local Missions

The goal is straightforward and clear — to disrupt gun violence

strategically and comprehensively, using all available enforcement and

prosecution tools; uniting federal, state and local efforts; and leveraging

new resources at all levels. (USDOJ, 2001, p. 2)

The federal guidelines indicate that the US. Attorney of each of the ninety-four
federal judicial districts is to oversee a collaborative team to plan and implement
strategies to address gun violence. The US. Attorney in the district must ﬁle reports with
the US. Department of Justice by January 31 and July 31 of each year to certify that a
PSN strategy is being implemented. PSN efforts are to address, and reports are to
document, the adoption of ﬁve core elements of the PSN policy. Those ﬁve elements
are:

l) partnerships,

2) strategic planning,

3) training,

4) outreach, and

5) accountability.

The general mission of the program is to enhance public safety by addressing gun
violence, but jurisdictions are given latitude in developing strategies speciﬁcally aimed at

solving the gun violence issues that affect their area. The team implementing PSN in

87

each of the jurisdictions can choose the geographical area that team wishes to target.
PSN can be implemented jurisdiction-wide (which in some cases means an entire state or
could mean one-fourth to one-half of a state), in a city, in a county, in a political
jurisdiction - such as a Native American reservation, in a sector of a city such as police

precincts, or a combination of such areas. PSN implementation has taken on all of the
forms listed above.

Partnerships
To implement Project Safe Neighborhoods, the Attorney General has
asked each United States Attorney for a commitment to assume a

leadership role for coordinating this effort and heightening awareness and
involvement at the local level.

(USDOJ, 2001, p. 2-12)
The United States Attorney in the district is charged by the Attorney General of
the United States to facilitate the PSN effort. US. Attorneys are to coordinate the efforts

of all agencies in their jurisdictions and all initiatives that have a relationship to gun

violence (U SDOJ, 2001).

Partnerships should include local district attorneys, chiefs of police,

sheriffs, as well as representatives from federal law enforcement,

including the US. Attorneys, United States Marshals Service, INS, ATF,

FBI, and probation and parole authorities. In some cases, it may also be

appropriate to include other community leaders, such as faith leaders,

educators, and citizen activists.

(USDOJ, 2001, p. 3-1)

Partnerships (PSN taskforces) are to include representatives of the public agencies

in the jurisdiction that are involved in policing, prosecution, parole, and probation of law

offenses and offenders. As noted earlier, the team also included two members or entities

whose services are provided under contract, a media consultant and a research partner.

88

Partnerships or collaborations with other representatives or leaders of
communities or faith communities is mentioned, but speciﬁcally leﬁ to the discretion of
the task force in the jurisdiction. PSN task forces are to join with other gun-violence
oriented task forces that may already exist.

According the US. Department of Justice, each of these PSN taskforces should

have written organizational documents including:

1) goals,

2) sub-goals,

3) methods,

4) activities,

5) responsibilities,

6) schedules,

7) resources,

8) and anticipated contingencies relating to the project (U SDOJ, 2001, p. 3-5)

The United States Department of Justice (DOJ) also encourages the PSN task

forces to formally adopt one of two problem-solving models, either:

1) SARA (scanning, analysis, response, and assessment), or

2) the SAC SI model (Strategic Approaches to Community Safety Initiative).

In addition, USDOJ also recommends formally developing a logic model for
program implementation, documentation of programs that are to be implemented, and the

articulation of impact (outcome) measures. Task group members are encouraged to adopt

89

a “risk-taking orientation” in order to move beyond current practices to embrace novel

strategies that appear to be supported by the data analysis (U SDOJ, 2001, p. 3—6).

Strategic Planning
Although the speciﬁc approach to combating gun violence will
accordingly vary ﬁom district to district, Project Safe Neighborhoods asks

each district to incorporate three national priorities in the federal

enforcement component or the strategic plan.
(USDOJ, 2001, p. 2-21)

The three national priorities the PSN taskforces are to address are:
1) increased prosecution of violent organizations (prosecution under conspiracy
laws, or 18 U.S.C. § 924C, for the prosecution of armed drug dealers),
2) increased enforcement of gun trafﬁcking laws, and
3) enforcement of federal laws against gun possession by felons and others

prohibited from such possession.

Each district is charged with collecting local information (from local law
enforcement and the ATF), and analyzing the extent, source, and location of gun crime.
This information is then to inform strategic planning. The strategic plan is to include
information gathering on the nature and location of gun crimes, the venues where gun
crime is prosecuted (state or federal), and the results of prosecution. F ollow-up
information collection should be used to identify success and failure of strategies.
Training

This component encouraged task force members to provide training classes.
Existing classes and workshops, such as those oﬂ‘ered by ATP, were to be augmented by

newly-developed regional training and locally-delivered workshops.

9O

Outreach
By conveying the priorities and subsequent results of this enhanced

enforcement effort to the media and community members, the district
partnerships can help shape the attitudes of law-abiding citizens and deter
those who would otherwise believe they can violate our gun laws with
rmpunrty.
(USDOJ, 2001, p. 2-30)
Media efforts were to be framed to educate citizens and build support for
aggressive prosecution (OJP, 2005b). The message is to be “gun crime means hard time”
(USDOJ, 2001, p. 2-31). One complication in the PSN strategy involves the mission to
create a non-proﬁt corporation or foundation to provide ongoing support for public
education and media efforts. US. Attorneys are prohibited from forming such non-proﬁt
organizations. Therefore, other members of the task force were asked to take the lead in
creating such entities to ensure the sustainability of outreach efforts (U SDOJ, 2001).
Accountability
US Attorneys are required to report on the implementation and impact of PSN
twice a year. A formal report is to be ﬁled on January 31 and July 31 of each year
(USDOJ, 2001). A form for such reporting was developed and covers four areas:
1) the nature of partnerships with federal, state, and local agencies,
2) how gun crime is being strategically addressed (the nature and extent of gun
crime, the strategies employed, and how impact is measured),
3) the media strategy, and
4) the training conducted and/or attended.

In the ﬁnal analysis, the ofﬁcials from the Department of Justice, Ofﬁce of Justice

Programs, counsel the US. Attorneys and those participating in the adoption efforts that

91

the success of Project Safe Neighborhoods will be based on the ability of multiple

agencies to collaborate effectively.

The eﬁectiveness of Project Safe Neighborhoods is based on the ability of
federal, state, and local agencies to cooperate in a uniﬁed offensive that is
led by the United States Attorney in every one of the 94 federal judicial
districts across America.

(OJP, 2005b, p. 1)
The Rewards and Challenges for Research Design

In the largest sense, this study attempts to enhance scholarship and inform policy
not by looking at whether or not a policy reduced criminality or crime, but what factors
increase or inhibit the willingness and ability of a jurisdiction to actually put that policy
in force. For policy makers, such knowledge could provide immense efﬁciencies in
terms of funding jurisdictions or agencies that are both willing and able to adopt the
policy, thereby enhancing the potential beneﬁt of federal funding. However, most
criminal justice policy research has focused on outcomes, such as crime reduction, aﬁer —
and assuming — policy adoption has taken place. Very few studies have attempted to
build understanding about the context in which adoption is most likely to take place, take
place with the most ﬁdelity to legislative intent, and take place most fully.

Chen (1989) and other scholars writing on the need for zealous, theory-driven
program evaluation, note that too often policy adoption or program implementation is
viewed as a black box. Success or failure in the outcome measure is assessed without
well-deﬁned and documented measures of the degree to which the core elements of the
policy were put in place. For policy-makers, this limits the ability to improve policies or

ﬁrnding criteria. Oﬁen policy makers, and those attempting to evaluate the effect of

92

 

policy adoption, do not have the luxury of in-depth case studies to determine the extent to
which core elements or protocols prescribed actually were followed (Weiss, 1998).

Both theory and research indicate that ofﬁcial reports may fail to capture the
reality of adoption (GAO, 2003; NI], 2000; Roth et al., 2000). Research attempting to
gauge the efﬁcacy of a program, such as PSN, often assumed policy adoption based on
formal reports. Studies of PSN have used formal self-reports of policy adoption and have
reported universal compliance, at least to some degree.

Every district seems to have experienced an increase in collaboration and
planning among local, state, and federal law enforcement agencies
(Ludwig, 2005 p., 690).

Ludwig’s (2005) assessment that PSN increased every district’s inter-agency
collaboration and planning was based on ofﬁcial reports. However, surveys with PSN
members call the validity of those ofﬁcial reports into question. District representatives
preparing the reports may vary in their deﬁnitions and interpretations of the policy, or
some may have decoupled goals from actual performance. Comparing surveys of PSN
research partners to the information contained in ofﬁcial reports indicates a reason to
question the sole reliance on ofﬁcial reports to provide valid and reliable measures of
policy adoption.9 One research partner surveyed noted that even though members of the
task force were listed in the ofﬁcial report, the task force existed only symbolically, did
not meet, and that the PSN effort was planned and directly by staff in the US. Attorney’s
’ ofﬁce. According to this research partner, this form of adoption resulted in increased

federal prosecutions, but no progress toward collaboration and cross-agency strategic

 

9 The survey conducted by the PSN research partners is explained later in this chapter, and the survey
instrument appears in Appendix A Research partners were assured that their direct responses would not be
linked to their me or district. so the quotes are not attributed.

93

planning, “. . . prosecutions may have been up, but there appears to be no meaningﬁil
bonds that were made in this federal/state interagency project.”

Similarly, while all PSN districts provided evidence of a legal contract with a
research partner, surveys of the research partners indicate that the presence of a contract
with a researcher or research team was not always a valid indicator of the intent or ability
to engage in strategic planning. Responses from research partners indicate aggressive
strategic planning efforts in some districts. One research partner described task force
efforts to seek out information from a wide variety of sources including “. .. data from
corrections, schools, police, sheriff, plus local interviews to determine local government
crime issues.” In contrast, other research partners indicated that data-driven decision-
making was actively discouraged despite the presence of the research contractor. One
researcher indicated that “[the researchers] were explicitly told by the USA [U .S.
Attorney] that they were not interested in using data to identify problems.” Discrepancies
between survey responses and formal reporting also indicate that collaborative
implementation of programs may, in some cases, be over-reported. Despite reports of
universal adoption, research partners offered counterclaims, such as . .the task force has
not yet chosen and implemented an intervention, so there is nothing yet to evaluate.”
Given the threats to the reliability and validity of measures that rely primarily on formal
statements of compliance with PSN adoption, the construction of policy adoption

measures is of great import.

The Need to Address Policy Adoption by Element and in the Aggregate
Because the elements of policy adoption involve potentially different protocols

and processes, those elements will be constructed as variables, and those variables will be

94

 

aggregated to create an overall measure of adoption and patterns of adoption. For
example, enhanced federal prosecution of gun offenders will be deﬁned as one of these
lower-level dependent variables or measures of an element of policy adoption. However,
an argument can be made that patterns of policy adoption could result in scenarios that
would be associated with static or declining federal prosecution of gun offenders. An
example would be a district in which the collaborative task force planning process
informed by research, resulted in the task force deciding that enhancing local and state
prosecution of gun crimes, as opposed to federal prosecution, would be the most effective
within the context of their district. Similarly, a district that identiﬁed gun carrying by
parolees as the major source of gun crime and social support programs as potentially the
most effective means to lower that recidivist activity might craft a range of prevention-
based services that actually lowered federal prosecutions.

Deﬁnitions of implementation success also are complicated by the fact that while
PSN is ﬁ'amed as a uniﬁed policy, adoption or rejection of one element of that policy
does not assure adoption or rejection of the other elements. For example, research-driven
strategic planning is mandated as an element, but the ability to produce good information
and create a plan does not guarantee that task forces will be willing or able to translate
that information into programs aimed at reducing gun crime. Similarly, adopting a wide
range of programs can be done based on history or the interests and attitudes of the

group, but in the absence of any meaningful research.

95

Research Design

The Population Under Study

PSN is a full coverage, non-competitive federal program. Funds are distributed to
each of the ninety-four federal judicial districts. In this case, the entire population, rather
than a sample from the population will be studied. The only districts excluded from the
research were the four serving US. territories. Given the differences in governance and
the lack of consistency across data sources between districts within the United States and

those districts in unincorporated territories and commonwealths, these four districts were

eliminated.
Analytical Strategy

From a research standpoint, a major limitation of recent federally-incentivized
criminal justice policies is that they are designed as ﬁrll coverage or non-competitive, so
that funding is — if not universal — it is virtually so.lo Ideally, to test a model that predicts
how the context and the perceptions of decisions and decision-makers affect the level of
policy adoption of an incentivized criminal justice policy, would involve a true
experimental design with districts randomly chosen for a funded group or for a control
group. While a true experimental design is the gold standard for research and evaluation,
full coverage programs are a reality, and dealing with this limitation has been addressed
in the literature on research and evaluation design (Chen, 1989;,Rossi, Freeman, &
Lipsey, 1999; Weiss, 1998). Many strategies, including reﬂexive control designs using
time series, simple pre-post studies, or connoisseurial assessments are utilized for ﬁrll

coverage evaluations (Rossi et al., 1999). However, in this case, the mission of the

 

‘0 An example is DOJ’s offender reentry, “Going Home” grant This grant is non-competitive so ﬁrnding
has been received by all states (OJP, 2005b).

96

research is not a classic study attempting to measure the effect of a treatment and link that
effect back to factors in the subjects or entities under study. In this case, the mission and
design is much simpler and better suited for study with a ﬁrll coverage sample.

In this study there is no treatment component in the traditional sense. Rather the
goal of the research is to determine if certain factors in the external environment of each
district make that district more or less likely to adopt the policy. Such a question lends
itself well to a multiple regression analysis (Allen, 1997). For each case, or district,
multiple factors within a model were deﬁned and tested to determine the extent to which
the model predicts variation in the level of policy adoption, and the extent to which each
of the factors in the model contribute signiﬁcantly. Three dependent variables for the
three core elements of policy adoption were constructed. The scores then were
normalized, and the model tested on the aggregate policy adoption score, using Ordinary
Least Squares (OLS) regression. This allowed the external competing factors model to
be tested for its ability to predict variation in overall adoption and in each of the
elements. The assumptions and limitations of the use of a multiple regression analysis

are discussed at the close of this chapter.

Beyond Regression Analysis

Risk theory predicts that patterns of policy adoption are likely to occur as a means
of managing risk. Given that, districts will be ranked on levels of implementation for
each policy element. Districts will be sorted to determine if patterns of policy adoption
reﬂecting symbolic, recognition-based decision groups and strategic groups emerge. The

attributes of those groups will be analyzed, and research questions formulated to inform

97

 

further testing of the two remaining levels of the model: perceived risk to agency

decision-makers, and risk factors within the task group.

The Model and the Hypotheses

The external competing factors model that will be tested in this study is illustrated

in Figure 14. The model is
Figure 14. The External Competing Factors Model
divided into four sectors: needs

Human Factors Resources and
and products, structure and . Survival
Punitive Allocation
infrastructure, human factors,
Resources

extent to which this model of

and resources and survival. The
Politics
central research question is the Race pm

external competing factors can Cr rrne

 

 

predict the level of policy Cases
adoption of PSN. To address

Structure and Needs and
this general question the Infrastructure Products

following hypotheses will be tested.

Hypothesis 1. The external competing factors model will predict the level of PSN
policy adoption.

The ﬁrst hypothesis to be explored was the degree to which the model predicts the
level of policy adoption. It is theorized that to truly understand the risk or beneﬁt of
adopting a policy such as PSN, that lowered risks and higher beneﬁts in each of the

sectors will affect the level of adoption. The inﬂuence of each of the variables in the

98

model are outlined in the following sub-hypotheses, each of which is based on the

hypothesized influence of the sectors within the general model.

Hypothesis 1A. Within the needs and product sector of the model, the greater the need
for the product associated with policy adoption, the greater the level of
implementation.

The degree to which the proposed policy provides a product or service that is
needed in the jurisdiction is theorized to lower the risk of policy failure. In this case of
Project Safe Neighborhoods, since the product or service promised by the federally
incentivized policy is the lowering of gun crime, a jurisdiction with no - or exceedingly
low -- gun crime would be hypothesized to have a lower potential return on investment
for adoption than a district with a higher gun crime rate, in which the costs of adoption
could be outweighed by potential beneﬁts.

Hypothesis 1 B. Within the structure and infrastructure sector of the model, the greater
the congruence of the competencies and the congruence of the
structures within the adopting jurisdiction with those needed for policy
implementation, the greater the level of policy implementation.

The structure and infrastructure within the implementing jurisdiction is
hypothesized to inﬂuence the degree to which policy implementation is a risky endeavor.
Jurisdictions that have experience with similar programs are hypothesized to have
increased competency with related policies and, therefore, a lower risk associated with
adoption. Ifthe infrastructure is congruent with the framework of the policy and
supportive of the policy, the risk also should be lower. In this case, since the policy
requires groups to be formed that use data-driven strategic planning processes to address

gun crime, it is hypothesized that two facets of the implementation environment could

inﬂuence the capacity and the level of risk. The degree to which information technology

99

 

is integrated into public agencies is theorized to be linked to the general competency with
data collection, aggregation, and system interoperability. These are the types of
infrastructure capacities that should enhance inter-agency strategic planning efforts and
decrease the risk of failure. Population density also is proposed to increase or decrease
the risk of failure. Highly diffuse, very rural, low population districts are proposed to be
less congruent with the policy structure. PSN policy mandates that task groups from a
broad array of agencies work closely across governmental levels and across agencies to
research, plan, frame, and implement gun policies. For very low density districts,
agencies are widespread, federal ofﬁcials (such as ATP or FBI) may not have a presence
in the district, and the locus of the proposed programs or interventions may be difﬁcult to

determine.

Hypothesis 1 C Within the human factors sector, the greater the congruence of pu blic
attitudes with the mission of the proposed policy, the higher levels of
policy adoption.

For the human factors sector, the model predicts that attitudes that are congruent
with the policy would be associated with higher levels of adoption. A district in which
the public and interest groups supported more stringent gun regulation, were more
punitive, but felt lower levels of racial threat, were hypothesized to be more supportive of
PSN adoption. Measured as codiﬁed laws or public action, attitudes that support tough
restrictions on gun use and ownership and higher punitiveness should lower the risk that
that PSN policy goals will be inconsistent with local norms and lower the risk of

opposition. In terms of attitudes toward minorities, racial threat theory would predict that

in localities in which the dominant population group imprisons minorities at rate greater

100

than their percentage in the population, that the dominant group would oppose limits on
their ability to defend against the perceived threat and would oppose higher levels of
control of guns. An alternative prediciton would be that greater the racial threat would
lead to support for the aggressive prosecution component of PSN (Liska, 1997).
Hypothesis ID. Within the resources and survival sector of the model, the greater the
degree to which the policy increases the resources and survival
capacity of the adopters, the greater the level of policy adoption.

The risk associated with policy adoption is theorized to be partially based on the
degree to which the resources provided by the federal program enhance the overall well
being of the decision-makers and citizens of the jurisdiction, when taken in the context of
the costs of policy adoption. Ifthe ﬁscal resources provided are low, that increases the
likelihood that - given some level of ﬁscal, human, time, and political investment
required for policy adoption — the return on investment might be low and the policy at
higher risk of failure. The need for ﬁrnding in speciﬁc areas also may affect the
perception of risk versus gain. Ifthe allocation provides resources in a critical area of
need, that additional infusion of funding may lessen an existing risk. A policy such as ‘
PSN, in which federal prosecution of gun offenders can and does result in federal
incarceration of those offenders, provides an opportunity to cost shift. Especially in
jurisdictions with low levels of ﬁscal effort for correctional systems, there could be
considerable risk reduction by shifting the cost of incarceration to the federal
government. In terms of political capital and survival, it is likewise hypothesized that it
would be less risky for a local ofﬁcial to adopt policies if those policies were championed

by his or her political party as opposed to those offered by the opposition.

lOl

Hypothesis 2. The external competing factors model will diﬂ'erentially affect the
elements of policy adoption.

The second general hypothesis focuses on the differing elements of policy
adoption that are imbedded in the adoption of complex, multi-goal policies, such as PSN.
Theory informing the external factors model would predict that the elements of policy
adoption would be differentially affected by external factors due to differing risks
associated with each. The risk associated with each element would depend on the ability
of the district to perform tasks associated with that policy element and the potential gains
or losses associated with its adoption.

Hypothesis 2A. Adoption of collaborative programs should be associated with all
sectors and factors within the model

Since collaborative implementation of programs requires broad-based
participation from multiple agencies, all sectors of the model - need for gun violence
intervention, experience and competencies with similar programs and with planning
processes and structure, congruence with local attitudes, and increased political and ﬁscal

resources — should enhance the level of policy adoption.

Hypothesis ZB. Adoption of research integration into strategic planning should be most
inﬂuenced by the structure and infrastructure sector within the model.

To integrate research into strategic planning, having both a technical
infrastructure and a culture that supports research-driven decision-making would be
hypothesized to be key to the adoption of this policy element. So while applying research
to gun violence issues would involve competing inﬂuences ﬁ'om all the sectors and
factors in the model, it would be hypothesized that the factors in the structure and

infrastructure sector of the model would assert the most inﬂuence.

102

Hypothesis 2C. Adoption of enhanced federal prosecution should be most strongly
inﬂuenced by the factors in the needs and products sectors and those
in the resource and survival sector. '

Increasing federal prosecution of gun offenders can be accomplished with little in
the way of collaboration across agencies in a jurisdiction. In the most narrow adoption
context, if resources were sufficient and were targeted toward the hiring and training of
federal prosecutors, and providing there was sufficient gun crime to allow for an increase
in such prosecutions, this policy element could be achieved. As will be discussed in the
analysis of Hypothesis 3, this policy element is the one which is hypothesized to be the
most likely candidate for recognition-based, or narrow, adoption and adoption to the
exclusion of other elements. In practice, narrow policy adoption also could involve
recognition-based groups, beyond the ofﬁce of the federal prosecutor, but limited to those
most intimately involved in the apprehension and prosecution of gun offenders, namely
state and local prosecutors and law enforcement officials. The ability and willingness of
local and state prosecutors and law enforcement to support protocols to apprehend and
prosecute gun offenders is hypothesized to be inﬂuenced by all the factors in the model,
but bounded by the level of gun crime and enhanced by the level of political and ﬁscal
capital the policy would provide. Therefore, all elements of the model are theorized to
inﬂuence the acceptability of prosecuting gun offenders, but the needs and product sector
and the resources and survival sector are hypothesized to be most critical.

Hypothesis 3. Patterns of policy adoption will be found that reﬂect symbolic groups,
recognition-based decision-making groups, and strategic groups.

Theories on the ways in which groups deal with the risk and costs of participating

in collaborative enterprises reﬂect three patterns of group interaction: symbolic,

103

recognition-based groups, and strategic groups (Comfort, 2002, 2005). While the
dynamics of groups will differ, and these patterns are proposed to fall along a continuum,
if the hypothesis be correct, the expectation would be examples of extremes, or pure
forms, of these groups could be distinguished. Given the nature of PSN, the three
patterns reﬂecting risk reduction group'dynamics are hypothesized to be symbolic,
recognition-based, and strategic.

Despite no district reporting a failure to adopt PSN, it is hypothesized that a
pattern of symbolic participation will be found. Symbolic groups, in their purest or most
extreme form, would be those that do not implement any element of the policy. Another
means of reducing the cost and risk of investing in incentivized, cross-entity
collaboration is to limit active participation to a recognition-based group. In other words,
group members that share very similar core goals, lexicon, and protocols can
communicate and operate at a lower cost than groups that must expend resources on
ﬁnding or developing common goals, common protocols, and common communication
systems. In theory, narrowly—focused or recognition-based groups could involve any sub-
system or closely linked sub-systems, but in practice for PSN the most likely scenario is a
federal prosecution-focused group. Since PSN is based out of the Office of the U. S.
Attorney (U SAC) and since the USAO is accountable for reporting results, the most
likely scenario for a limited and targeted group would be one centered on the protocols
and processes of federal prosecution. Finally, those groups with the capacity to share
information, transform that information so that is useful to all members, and to drive

decision with information are hypothesized to take on a strategic or ﬁrll implementation

structure.

104

Sources of Data

As previously noted, Project Safe Neighborhoods information is available from a
variety of sources. The Attorney General of the United States requires formal reports on
various aspects of the program to be ﬁled by each United States Attorney’s Office
(U SAO) twice a year. In addition, contracts with research partners were required as part
of the PSN grant, and research partners were required to provide information to a national
research team, based at Michigan State University (MSU). While much of the
information that is routinely collected is useful and has been integrated into the measures
of policy adoption, gaps in information and limited variation in some measures required

an additional survey to be conducted.

Research Partner Survey

A survey instrument was drafted and constructed as an electronic form. The
survey focused on strategic planning within the district’s PSN task force and included
closed-ended, numerically-coded questions using modiﬁed Likert scales, yes or no
responses, and open-ended ﬁelds for comments and clariﬁcations. The survey form
appears in Appendix A. The research partner or lead researcher of the research team
under contract with the district’s U. S. Attorney‘s Office (USAO) was identiﬁed. The
names and email addresses for all research partners were identiﬁed and entered into a
database. The electronic form and instructions were emailed to the research partner of
record. To promote candor, research partners were assured that their responses would not
be speciﬁcally identiﬁed with their district. Surveys were returned via email or were
faxed. Three formal email communications were used to follow-up with research partners

who did not respond within the requested timeframe. During that timeﬁ'ame, faulty

105

contact information was corrected and messages resent.ll If responses were still not
forthcoming, phone calls were made and responses elicited. Of the 90 federal judicial
districts (excluding the territories), seventy-nine researchers, or 87.9 percent, completed

the survey.

Formal Reports from the USAO to the US. Attorney General

Formal reports are mandated to be ﬁled twice a year by each United States
Attomey’s Office to the Attorney General of the United States. Officials of the US.
Department of Justice determine the format of the reports and the material that is to be
reported during that period. While some remain consistent, questions may vary ﬁom
reporting period to reporting period. These questions include lists requiring the
responder to check all that apply, modiﬁed Likert scales, and narrative sections. The

questions that were used to construct scales are listed in Appendix B.

Secondary Data Sources

Secondary data sources were used to construct independent variables on external
factors and are discussed speciﬁcally under the section on independent variable

construction.

Data Reports to the PSN National Research Team
District research partners are to submit information to the national research team
on a variety of gun crime measures in the district. To be completely in compliance with

data submissions, researchers submit monthly reports on federal and local gun crime

 

1' Some email addresses were originally miscoded. one research partner had passed away and several had
changed positions, so the lists were updated with those researchers performing the task at the time of the

survey.

106

prosecutions, arrests and seizures for gun possession, arrests and reports of gun crime and
crimes involving the use of a weapon, calls for service on gun-related incidents, and

illegal ﬁrearm sales and interdictions. Researchers coded data submissions according to

the criteria listed in Figure 15.

Figure 15. PSN National Research Team Data Quality Rating Scale

Very good Submitted most or all of the requested variables in monthly form

Submitted most or all of the variables in monthly form and usually some

GOOd additional ones in place of missing variables
Poor Submitted some data in monthly form but it is severely limited (generally
only URC data)
Submitted limited variables in annual form or submitted data that was hard
Very poor .
to interpret
None No data submitted

Assumptions and Limitations

While ordinary least squares (OLS) multiple regression analysis is a robust tool
and well suited to the ﬁamework of this study, there is a ﬂaw in the application of this
tool to this population. Federal judicial districts are nested in states. Linear model
analysis assumes linearity, normality, homoscedasticity, and independence. In the case
of federal judicial districts, we cannot assume that the last two assumptions are true. The
inﬂuence of the state in which the district is nested may mean that the amount of variance
that policy adoption exhibits across the independent variables may be inﬂuenced by that
nesting. This nesting also threatens the assumption of independent observations (Allen,
1997). Ideally, this problem would be addressed by using hierarchical linear modeling

(HLM). However, only twenty-four states have multiple districts, and the greatest

107

nesting occurs in California, New York, and Texas in which the highest number of nested
districts is four. This nesting pattern is insufﬁcient to meet the requirements of HLM
(Raudenbush & Bryk, 2002). Another option to control for the effect of nesting is to
insert dummy variables to control for the inﬂuence of each state (Allen, 1997). For this
population, twenty-four such dummy variables were required. Survey responses limited
the number of cases to seventy-nine. Regression models were run with and without the
inclusion of these variables, and the affect of the full model and restricted model were
analyzed. The inclusion of the state dummy variables tripled the number of independent
variables in the model from twelve to thirty-six, increasing the degrees of freedom to an
untenable level. As illustrated in Appendix C, only two dummy variables rose to
signiﬁcance. The model was run including dummy variables for these two states. One
state was signiﬁcantly associated with higher levels and one with lower levels of
adoption. In comparing the models with and without the dummy variables for these two
states, the results remained consistent as the same variables rose to signiﬁcance under
either condition. The state effects are discussed in Appendix C, and the simple model
without dummy variables was employed in the study.

Additional diagnostics were conducted to assess the possibility of collinearity, or
the problem that independent variables will be highly correlated, thereby inﬂating
standard errors and increasing the likelihood that a false null hypothesis will be rejected
(a Type H error). Analyses were conducted to identify relationships among independent

variables that could lead to problems with collinearity in the model (see Tables C2 and

1,08

C3, in Appendix C). 12 Some signiﬁcant associations did exist among variables. The
level of violent crime was signiﬁcantly associated with the number of federal gun cases
brought by federal prosecutors, with population density, with the number of prisoners per
capita, and with the level of the allocation (which, as this is based on a modiﬁed per
capita funding formula) is highly related to the number of residents in the district. Crime
was negatively related to the per capita spending on an inmate per year. The number of
gun cases brought by federal prosecutors also was signiﬁcantly related to population
density and to the allocation. Cases were negatively related to the restrictiveness of gun
laws. Levels of racial threat were higher in districts that were more punitive and had
more Republican leadership. Gun laws were more restrictive in higher population
districts. More punitive districts spent less on inmates. The presence of a Weed and
Seed program was signiﬁcantly associated with high crime and high allocation districts.
These relationships indicate potential problems with collinearity could be present in the
model. Therefore, further diagnostics were performed.

The variance inﬂation factor (VIP) and its inverse, tolerance (TOL) were
computed (Appendix C). Tolerance statistics for the restricted model ranged between .56
and .92, which, according to Fox (1991) is well within the acceptance range. Linear
dependence was examined using a condition index. Proportions of variance among the
independent variables, found in Appendix C, indicated that none of the factors shared
proportions of variance above .50 that threatened a violation of linearity. Standardized

residuals and Cook’s Distance (D) statistics were computed and reviewed. As suggested

 

‘2 A correlation matrix was constructed for scaled variables, an analyses of variance (one-way ANOVA)
were conducted to gauge relationships between categorical and scaled variables, and Chi Square analysis
compared categorical variables.

109

by Berk (2004), the studentized residuals and the Cook’s D values were examined, and
no problematic cases were identiﬁed or deleted. 1
Summary and Introduction to Variable Construction

This chapter provides an overview of the theory driving the external level of the
model. Along with testing the hypotheses related to competing external factors model
and the sectors within the model, if is hoped that this investigation will help to hone
questions to be posed and answered by further research into the context of the agency and
task group levels of the ﬁll model. In addition, the construction of measures of success
for these complex, incentivized policies poses particular challenges, which this research
effort strives to overcome. Because the construction of elements of policy adoption pose
particularly interesting and important challenges for this research, the construction and

the description of variables are covered in some detail in the following chapter.

110

CHAPTER SEVEN:
VARIABLE CONSTRUCTION AND DESCRIPTIVES

Policy Adoption Elements and Measures

PSN is not a unitary policy in any sense. True adoption of PSN entails the
adoption of core policy elements framed by the Department of Justice. Those elements
can be characterized as strategic planning that integrates research into decision-making,
collaboration among federal, state and local groups to craft and implement programs,
media outreach, training, and enhanced federal prosecution of gun offenders. Therefore,

to begin to capture the extent to which PSN actually is adopted by the districts, separate

measures would Figure 16. Diagram of Policy Adoption and Policy Adoption

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

need to be Elements
developed for
Collaborative
each of these core Implementation
elements.
However, formal Research .
Integration Into l____. Policy Adoption
reports from the Strategic
Planmng
United States
Attorney 5’ Enhanced
Federal
Ofﬁces (USAO) Prosecution
included
documentation

ﬁom all districts that the media partner for each district had adopted a media outreach

strategy, and that training had been provided. Since no variation was found in these two

111

policy elements, they effectively became constants and hence were not included in the
policy adoption models. The remaining three elements of policy, illustrated in Figure 16,

became the component elements of PSN policy adoption.

Collaborative Implementation

The policy element of collaborative implementation (collaboration) was
constructed ﬁom the formal reports submitted by the USAO. All districts reported cross-
agency collaboration so that direct responses provided no variation. Therefore a measure
was constructed using a listing of programs reported to be implemented as part of the
PSN process. In the report form the USAO, the United States Attorney was to place a
check next to each strategy the PSN task force had implemented. To determine which
groups were active in the collaboration, the programs were categorized by group. An
example would be whether law enforcement agencies were an active part of PSN. If the
report indicted that any law enforcement-focused intervention, such as directed patrol or
street-level enforcement, were implemented as part of PSN, that district would be given
one point for engaging law enforcement. Unlike the direct questions about collaboration,
there was variation in these measures. The responses that were used to construct the
measure are described below and listed in Appendix B.

The following responses were coded by category, with a point given if any
intervention pertaining to the category were checked, and zero points given if none of the
interventions were checked. The exception in this scoring was for the engagement of the
community. Unlike the other categories, for which there were one or two possible
responses, community involvement had ten possible interventions listed. Over all the

districts, the mode value for community engagement was two. Therefore, to discriminate

112

between those districts with no or little engagement of community members from those
with more active engagement, districts with responses above the mode value were given
one point and those below the mode value received zero points for this category.
The maximum possible score for collaborative implementation was seven and the
lowest possible score was zero. The questions and the scoring for this are as follows.
1. Federal Prosecution: check box indicating yes or no.
2. Enhanced Local and State Prosecution: check box indicating yes or no.
3. Law Enforcement: check boxes for directed patrol or street-level enforcement.
4. Parole and Probation: check boxes for offender notiﬁcation meeting or offender
home visits.
5. Community Programs: districts reporting more than the mode value were scored
as one (see Appendix B).
6. Supply-Side Intervention: check box indicating yes or no.

7. Gang or criminal organization interventions: check box indicating yes or no.

Research Integration into a Strategic Planning Process

To construct a measure of the integration of research into the strategic planning,
three sources of information were used: the research partner survey, the formal reports by
the United States Attorneys, and the data submitted to the PSN national research team.
The following questions were used from the research partner survey (see Appendix A).
Question one, part B asked the degree to which data were translated into decision-
making. Answers were given on a four-point scale indicating that data were not
translated (score of 1) to data had been used to create programs or strategies (score of 4).

Question three, parts A and B were yes or no questions, asking ﬁrst whether the research

113

team was able to conduct an analysis of gun crime in the district and, if so, whether the
task force used the information to shape policy. The district scored a one if an analysis
had been conducted, and the information from that analysis had been utilized. The
district scored a zero if either were not true. Question six asked about the overall impact
of PSN in terms of the use of problem-solving processes and was scored on a ﬁve point
scale from no impact (score of 1) to PSN creating an environment in which data analysis
drives decision making (score of 5). From the formal reports ﬁom the USAO to the US.
Attorney General, a three-point scale graded the helpﬁrlness of data and research. A
score of one reﬂected the response “not really,” a score of two reﬂected “somewhat,” and
a score of three indicated “very helpﬁrl.” The last measure used to construct this variable
was the quality of the information submitted to the PSN national research team. Because
of the overall poor quality of data reporting, a limited scale was used. Each year the PSN
research team reported the data received as falling into ﬁve categories reﬂecting its
overall quality ﬁom none, very poor, poor, good, or very good. A binary scale was used
to rate data quality. If the mode value for data in the PSN period (2001 to 2004) were
good to very good, the district was given a one, if not the district received a zero. This

construction created a scale with possible values between three and fourteen.

Enhanced Federal Prosecution

Two measures of enhanced prosecution were used to create a factor score for the
measure of enhanced federal prosecution. The number of federal cases for gun violence
was compared for the year prior to PSN implementation (2000) and four years into the

policy adoption process (2004).13 The numeric increase in cases captures some

 

‘3 The cases used were those ﬁled under 18 U.S.C. 922 and 924.

114

 

information about the increase in effort in the district, but fails to place that number in the
context of the population of the district. Therefore, the numeric change and the change in
the per capita prosecution rate for the districts were reduced to a factor score using
principle component analysis and an oblique rotation. One factor was extracted. The
factor loading was .66, and the one extracted component explained 82.9 percent of the

variance.

Overall Policy Adoption Variable
To create an overall implementations score, the scores from the three policy

elements were converted to a standardized form and added.14

Describing the Policy Adoption Elements and Overall Score

For the policy elements of collaborative implementation and prosecution,
measures were available for all ninety districts (Table 1). For research integration, and
therefore for the overall adoption score, seventy-nine districts had valid measures.
Collaborative implementation scores indicate that no district reported a total absence of
program elements. Measures of central tendency and dispersion for the elements of
policy adoption and the overall level of policy adoption are listed in Table 1. The
average across districts indicated collaboration in 4.33 areas out of a possible seven.
Research integration measures indicated that some districts reported no integration while
others reported the maximum levels. The measure of federal prosecution is a factor

score, and the overall policy adoption score combined standardized values.

 

‘4 Scores were converted to z scores.

115

Table 1. Measure of Central Tendency and Dispersion for Dependent Variables

 

 

Standard

N Mean Minimum Maxium Deviation
Collaborative Implementation 90 4.33 1.00 7.00 1.78
Research Integration 79 8.73 4.00 14.00 3.20
Federal Prosecution 90 0.03 -l .73 4.44 1.00
Overall Adoption 79 0.07 -3.99 6.79 2.08

Since the sample size is less than one hundred, the Shapiro-Wil W statistic was
used as a test for normality. The null hypothesis that the distribution was not normal
could be rejected (at p<.01) for all distributions except research integration. A review of
that test, and normal probability plots and box plots generated using the Univariate
procedure and tests of normality in SAS indicate a fairly normal distributions for the
overall measure, federal prosecution, and collaborative implementation. The null
hypothesis for a non-normal distribution for the research integration measure could be
rejected, at the 0.06 level of signiﬁcance. Review of the normal probability plots and box

plots indicate a research integration distribution that skews right.

Construction and Description of Competing External Factors

In accordance with the model, variables were constructed to reﬂect concepts
within each of the four sectors. As the independent factors are related to the forces
proposed to enhance or inhibit policy adoption, the information is taken from the year
2000, the year immediately preceding implementation, as this reﬂects the conditions that
faced decision-makers as the policy was introduced. Unless otherwise noted, the
measures are for the year 2000 and for federal judicial districts. For scaled independent
variables, measures of central tendency and dispersion are illustrated in Table 2. I

Frequencies for dummy variables are found in Table 3.

116

 

 

Table 2. Measure of Central Tendency and Dispersion for Scaled Independent
Variables
N Mean Min Max SD
Need and Product Sector
Crime 89 490.63 43. 58 1507.36 253.85
Federal Gun Cases 90 2.88 0.5 23.82 7.5
Structure and Infrastructure Sector
Information Technology 89 7.08 2.00 11.00 1.90
Population Density 90 362.32 1.26 9358.98 1 145.13
Human Factors Sector
Racial Threat 90 0.34 -0. 09 0.76 0. 16
GunLaws 90 9.19 -10.00 76.00 18.77
Punitiveness 89 405.99 114.61 775.09 151.01
Resources and Survival Sector
Resources 90 24,413 9,131 72,517 10,043
Allocation 90 584,841 285,000 1,300,000 345,347
Politics 87 0.85 0 2 0.64
Needs and Product Sector

The stated overarching goal of the PSN policy is the reduction of gun crime.

Therefore, a measure that appears to best capture the level of need in the district is the

level of such crime in the federal judicial district. Uniform Crime Report (UCR) data

were used. Since gun crime is not a speciﬁc category within the reporting structure, the

level of reported crime in four violent crime categories was used as a surrogate measure.

Information on the number of homicides, robberies, aggravated assaults, and rapes were

gathered and organized by judicial district (FBI, 2000). Rates of reported crime were

computed as the rate per one-hundred thousand in the population for each district

(USCB, 2000). Crime and census data from 2000 were used to indicate the level of need

prior to the initiation of the incentivized policy. As noted in Table 2, these rates ranged

from a low of just under 44 crimes per 100,000 in Eastern Oklahoma and 85 per 100,000

in North Dakota to highs hovering around 1,500 in the District of Columbia (1,465) and
Northern District of Illinois (1,512). The mean rate was 490, a rate close to that in the
Southern District of California (489), and the Middle District of North Carolina (496).15
A second variable used to capture the degree of need for gun crime intervention
was the number of federal gun cases per 100,000 people in the district in 2000, the year
prior to PSN initiation. Information on the number of cases brought was submitted to the
PSN research team and was available for all ninety districts. '6 The rate for federal gun
crime cases ranged from 0.05 to 23.82. The mean was 2.88. An example of a district
falling around the mean is the Northern District of Texas. The largest city is this district

is Dallas, and 176 federal gun crime cases were prosecuted during the year 2000.17

Structure and Infrastructure Sector

This sector deals with the structural factors that may inﬂuence policy adoption.
The three concepts to be captured within this sector were the general level of competence
with technology in the public sector, past history with programs with like elements to
PSN, and the district’s population density. Four variables were constructed to reﬂect
these concepts.

The general level of technological competence in the district is a scale constructed
from Barrett, Greene and Mariani’s rating of public sector competency with computer

technology (200]). While rankings could have been as low as 0, the minimum rank was

 

‘5 This variable uses a state-level measure. As noted in the discussion of assumptions and limitations in the
previous chapter, in twenty-four instances states have more than one district As discussed in Appendix C,
the limitations on this research, including the size of the population, and the pattern of nesting did not allow
for hierarchical linear modeling or the use of dummy variables to control for state level inﬂuences.

'6 The cases used were those ﬁled under 18 U.S.C. 922 and 924.

118

2, the mean score was 7.08 and the highest was 11 (Table 2). As part of the Government
Performance Project, more than one thousand state oﬁicials were surveyed. To assess the
state of information technology (IT), the ofﬁcials were asked to provide information on
the following areas: enterprise-wide technology systems, central coordination of
information, multi-year information technology planning processes, ease of procurement
of information technology, training for IT staff and end users, requirements for cost-
beneﬁt analysis or return on investment (ROI) for IT projects, use of IT by citizens, and
ease of use to conduct state business (Barrett & Greene, 1999; Barrett et al., 2001).

The history of implementing similar programs within the district was captured in
two dummy variables. 18 The ﬁrst was a self-report by US. Attorneys in the semi-annual
reports of the presence of Weed and Seed, a program that shares common elements with
PSN. The Weed and Seed program encourages community and governmental

collaboration to address crime and neighborhood decay through combined community

Table 3. Frequencies for Independent Variables for Similar Program Implementation

 

Frequency Percent

 

SASCI
Present 10 11.11%
Absent 80 88.89%
Missing 0 0.00%
Weed and Seed
Present 66 73.33%
Absent 2] 23.33%
Missing 3 3 .3 3%

 

'7 While these factors are signiﬁcantly correlated (see Table C2, in Appendix C), the number of cases
regressed on the crime rate indicates that only about 11 percent of the variation in the number of cases can
be explained by the variation in the level of crime.

18 In both cases, a score of one indicates the presence of the program and a zero indicates its absence.

119

development and law enforcement efforts (CCDO, 2006). The presence of Weed and
Seed programs was reported in nearly three-quarters of the districts (Table 3). The
second dummy variable (value of one) indicates that a Strategic Approaches to
Community Safety Initiative (SACSI) program had been ﬁrnded within the district.
SASCI is a policy initiative with many elements in common with PSN. The Office of the
United States Attorney (USAO) is the lead agency in this research—driven, problem
solving effort aimed at reduction of criminal activities (Coleman, Holton, Robinson, &
Stewart, 1999). As noted in Table 3, compared to the wide adoption of Weed and Seed,
only ten out of the ninety districts, or 11.1 percent, had received ﬁnding to adopt SASCI.
In the structure sector, theory suggests that policy adoption may be related to
population density (Feld, 1973; McGarrell & Duffee, 1995). It is theorized that in very
low density areas, where people and the criminal justice agencies that serve them are
widely spread, it would be more difficult to specify a target area and more expensive, and
complicated to organize the task force and routinely share information. In addition, with
no economics of scale implementing programs across a wide area might be cost-
inefﬁcient. Information to construct this variable was taken from the US. Census (2000).
Bivarate analyses (see Tables C2 and C3, in Appendix C) indicate only one
signiﬁcant relationship among the structure and infrastructure factors. Having a better
information technology infrastructure is signiﬁcantly related (p=.05) to high population

density in the district.

Human Factors Sector
Public attitudes translated into some measurable public action were the focus of

this sector. One concept to be translated into a variable was perceived racial threat. The

120

 

measure of racial threat was constructed by comparing the percentage of white inmates to
the percent of white residents. The possible scores ranged from negative one to one. The
extreme scores of negative one and one are possible mathematically, but improbable in
practice. What the scale represents is the level of minority over-representation in secure
conﬁnement. If the district had no white population at all, but 100 percent of inmates
were white, that would be a score of negative one, in effect reverse racial threat. If the
percentage of whites in the general population was exactly equal to the percentage of
white in the inmate population, the score would be zero, indicating no difference in
incarceration rates between black and whites. Ifthe general population in the district
were 100 percent white and all the inmates were black, the score would be one, indicating
the highest possible level of racial threat. While many factors are proposed to explain the
differences in incarceration rates between whites and nonwhites, this measure is used to
capture the relative difference in over-representation among jurisdictions (Leiber, 2003).
The greater the difference between the white prison percentage and the white population
percentage, the greater the score on this measure of racial threat.

To illustrate the measure, the District of Utah’s score on racial threat is very close
to zero. The district population in Utah is roughly 86 percent white. The inmate
population mirrors that percentage almost perfectly. Therefore, minority over-
representation in the prison population as a measure of racial threat is approximately
zero. In contrast, in the District of South Carolina the population is about two-thirds
white (66 percent), but only 28 percent of the inmate population is white. Therefore, the

racial threat measure for this district is 38 percent. In the District of Connecticut, white

121

residents make up roughly 80 percent of the population but only 23 percent of the prison
population, resulting in a racial threat score of 57 percent. 9

The second variable was a scale indicating the degree to which gun sales,
ownership, and carrying is regulated when compared to federal regulations. A score of
zero indicates congruence with federal statutes, positive scores indicate the net number of
provisions that have been codiﬁed that increase restriction on gun sales and gun
ownership above the federal laws, and a negative score would indicate the net number of
federal restrictions that are not reﬂected in the state statutes (ATF, 2000; OSI, 2000).
Scores ranged from negative ten to 76 (Table 2). The most restrictive districts were those
in the District of Hawaii (71) and the District of Massachusetts (76) and the least
restrictive was the District of Maine (-10). An example of the differences in the level of
regulation is that Maine has no restriction on sellers of ammunition while Massachusetts
requires those wishing to sell ammunition to formally apply and pass a criminal
background check. In Maine, ex—oifenders may apply for gun permits if they remain
crime-ﬂee for ﬁve years post discharge. In Massachusetts, gun ownership is prohibited
for all ex-felony offenders and also for ex-offenders who were convicted for speciﬁed
misdemeanors. In addition, Massachusetts has speciﬁc language prohibiting gun
purchases for some other non-criminal classiﬁcations such as drug addiction or habitual
drunkenness, with a doctor’s certiﬁcation of recovery required for the application to be
approved (ATF, 2000).

A third human factors variable, punitiveness, was constructed from census data
and from information on prison day-counts collected by the American Correctional

Association (ACA, 2000). A rate of incarceration per 100,000 was computed as a

122

measure of the level of punitiveness. Punitiveness was lowest in Minnesota, Maine, and
North Dakota, with fewer than 150 inmates for every 100,000 residents to more than 650‘
inmates for every 100,000 residents in the states of Texas, Louisiana, and Delaware.

One of the limitations of this, and the other human factor variables, relates to the
fact discussed earlier that these are state-level, not district-level variables. Since districts ‘
do not function as government units for the purposes of imposing gun laws or
incapacitating offenders, the overarching public acts and actions, such as regulating
ﬁrearms or and imprisoning offenders, reﬂect state-level, rather than district speciﬁc
measures. 19

Bivarate analyses of the relationship between variables in this sector (see Table
C2, in Appendix C), indicates that higher racial threat is signiﬁcantly correlated with
higher punitiveness, but not with more or less restrictive gun laws. Gun laws and

punitiveness are not signiﬁcantly correlated.

Resources and Survival Sector

Three concepts represented by variables in the survival sector were: ﬁscal
resources provided by PSN, the level of resources currently available to the criminal
justice sector, and potential political gains or losses related to PSN participation.

The resources provided by PSN variable is represented by the amount allocated to
the district during one funding cycle under the PSN program (Table 2). Funding for PSN
is allocated on a per capita basis, based on district population. However, the allocation
has both a ﬂoor and a ceiling amount. The lowest population districts, such as the

District of Wyoming with a population under 500,000, receive a base amount regardless

123

 

of population. This held true for twenty-four districts with populations below 1.36
million.20 On the other end of the spectrum, seven districts, with populations above
seven million residents, received the maximum, capped amount.21 The district with the
highest population is the Middle District of Florida with more than 9.2 million residents
and the lowest population district is Wyoming with less than 500,000 residents. The
mean funding amount across all districts was $584,840.57. This reﬂects the allocation to
(a district with roughly three million residents, such as the Eastern District of North
Carolina.

The second variable in the survival sector is a surrogate measure for ﬁnancial
support for criminal justice. It is the expenditure per inmate per year, based on surveys
conducted by the American Correctional Association. Information ﬁ'om those surveys on
correctional budgets and the average daily population (ADP) in the prisons was used to
compute a per inmate per year expenditure (ACA, 2000; USCB, 2000). States with the
lowest investment per inmate (the districts in Alabama) expend roughly nine thousand
dollars per year per inmate while the highest resourced districts, such as Oregon, will
spend eight times that amount. The mean expenditure is about twenty-four thousand
dollars per inmate per year, reﬂecting districts such as Kansas, New Jersey, and the

districts of Florida. As with the variables in the human factors sector that rely on

 

‘9 As noted in the assumptions and limitations sector in the previous chapter, the ability to address the
nesting of districts in states was constrained by limitations in the population size.

20 The lowest allocations districts were Alabama Middle. Alabama Southern, Alaska, Delaware, the District
of Columbia, Georgia Southern, Hawaii, Illinois Southern, Iowa Northern, Louisiana Middle, Maine.
Mississippi Northern, Montana, New Hampshire, North Dakota, Oklahoma Eastern, Oklahoma Northern,
Rhode Island, South Dakota, Vermont, Washington Eastern, West Virginia Northern, West Virginia
Southern, and Wyoming.

2‘ The six high population districts receiving the highest and capped amount were California CentlaL
California Northern, Florida Middle, Illinois Northern, New Jersey, New York Eastern, and Texas
Southern

124

correctional system information, this variable too suffers from the limitation of being
measured at the state, not the district level.

The potential political gain or loss from participation was represented by the
congruence of political party afﬁliation (Republican) with the President in the year 2000,
who proposed and championed the policy. Since a core mission of PSN is to engender
collaboration among federal, state, and local leaders and programs, variables were created
to represent the political atfrliation of key leaders at two levels of governance, city and
state. A scaled variable was constructed to represent either no congruence at the local or
state level, one leader in congruence, or both of the same party as the president
championing the policy, in this case a Republican. For state-level leadership the task is
straight-forward. Local leadership is much more difficult to difficult to represent. A
surrogate measure for the political alignment of local officials was used, the political
party afﬁliation for the mayor of the largest city, which was also, with the exception of
one district, the city with the most violent crime.22 The reasoning for this selection was
that the support for or opposition to the PSN initiative by the mayor of the largest city
and the city in which violent crime was most likely to occur would have some inﬂuence
over policy adoption in a key area of the district. In terms of Republican-afﬁliated
leaders, in contrast to state leaders, 60 percent of mayors were not Republican. As some
cities consider mayor to be a non-partisan office, the non-Republicans include
Democrats, other party afﬁliations, and non-partisan mayors. In 12 percent of the

districts, both the state and local leadership were aligned with the President’s party.

 

22 The only district for which the largest city was not the city with the highest violent crime rate was
Virginia The mayor for the city with the highest crime rate was used.

125

Bivariate analyses (see Table C2, in Appendix C), indicates that none of the

variables in this sector are signiﬁcantly related.

Summary

A major challenge to the study of policy adoption in criminal justice, and the
adoption of multi-goal, federally-incentivized policies in other ﬁelds, is the difficulty
deﬁning what constitutes success and how such success can be scaled. In the next
chapter, a variety of analyses will be conducted to attempt to use these variables, not just
to ﬁnd relationships between the external competing factors and success, but also to
attempt to differentiate the ways in which external factors may inhibit or enhance the key

elements that together constitute adoption.

126

CHAPTER EIGHT:
ANALYSIS AND FINDINGS

With the construction of the model and the construction of measures of policy
adoption, the external competing factors model and the hypotheses outlined in Chapter 7
were tested. The examination proceeded from a test of the overall model to tests of the
components and elements of the model.

Hypothesis 1. The external competing factors model will predict the level of policy
adoption.

The ﬁrst hypothesis addressed the overall research question, which is the extent to
which the external competing factors model predicts the level of policy adoption. Prior
to conducting a regression analyses, a bivariate analysis was conducted to investigate the
relationships between the independent variables in the model and the overall level of
policy adoption.23 The results, presented in Table 4, indicate that, that all of the variables
in both the needs and products sector are signiﬁcantly and positively correlated. Higher
levels of violent crime are correlated with higher levels of policy adoption (p<.01), and
higher per capita federal cases ﬁled for gun offenses are signiﬁcantly correlated with
higher levels of policy adoption (p = .01). Therefore, prior to controlling for the other
factors and other sectors of the model, the need for the policy proposed is associated with
higher levels of policy adoption.

Similarly, all of the factors in the infrastructure sector were signiﬁcantly
correlated with the level of policy adoption, and in the predicted direction (Table 4).

Higher population density in the district was associated with higher levels of policy —

 

23 Correlations were computed between scaled variables and an ANOVA for the relationship between
categorical and scaled variables.

127

adoption (p<.01). A better information infrastructure was associated with higher levels of
policy adoption (p<.01). Districts with prior experience with a Weed and Seed Program
had higher levels of policy adoption (p<.01), as did districts with prior experience with a
SACSI program (p<.01). These bivariate correlations lend support for the potential

importance of this sector.

Table 4. Bivariate Analyses of Policy Adoption and the Independent Variables

 

 

 

 

 

 

 

 

Bivariate Correlations Policy AN OVA Results
Adoption
Need and Products Sector
Crime Pearson Corr. 0.37
Sig. (2-tailed) 0.00 **
N 78
Cases Pearson Corr. 0.28
Sig. (2-tailed) 0.01 *
N 79
Structure and Infrastructure Sector N Mean sig.
Population Density Pearson Corr. 0.33 Weed and Seed 0. 00 W
Sig. (2-tailed) 0.00 ** Present 59 0.60
N 79 Absent 17 -1. 14
Information Technology Pearson Corr. 0.35 SACSI 0.00 **
Sig. (2-tailed) 0.00 ** Present 9 2.17
N 78 Absent 70 -O.20
Human Factors Sector
Racial Threat Pearson Corr. 0.08
Sig. (2-tailed) 0.47
N 79
Gun Laws Pearson Corr. 0.09
Sig. (2-tailed) 0.42
N 79
Punitiveness Pearson Corr. 0.00
Sig. (2-tailed) 0.98
N 78
Resources and Survival Sector
Correctional Pearson Corr. -0.01
Resources Sig. (2-tailed) 0.90
N 77
Allocation Pearson Corr. 0.12
Sig. (2-tailed) 0.28
N 79
Politics Pearson Corr. 0.09
Sig. (2-tailed) 0.41
N 78
*"' p< .0]
* p< .05

128

 

While bivarate correlations indicated signiﬁcant relationships between the factors
in both the needs and products sector and the structure and infrastructure sectors, no
variables in either the human factors sector or the resources and survival sector were
signiﬁcantly correlated with policy adoption levels (Table 4.)

To test these associations while controlling for the other variables in the model, a
multiple regression analysis was conducted.24 The composite dependent variable for
policy adoption was regressed on the external competing factors model. Table 5 presents
the results of that regression. The model of external factors predicted 50 percent of the
variation in policy adoption. In terms of the inﬂuence of various sectors of the model and
factors within those sectors, the signiﬁcance of their contributions to the model varied
widely and are discussed under the sub-hypotheses dealing with each sector.

Hypothesis 1A. Within the needs and product sector of the model, the greater the need
for the product associated with policy adoption, the greater the level of
implementation.

It was hypothesized that the more a policy under consideration focused on
producing a product or service that met a district’s need, and the greater the level of that ‘
need, the greater the perceived beneﬁt and the less the perceived risk of investing in
policy adoption would be. In this case, the level of gun crime per capita and the number
of federal gun crime cases per capita that were brought in the year prior to PSN initiation
were employed as indicators of the level of need for gun crime interventions. However,
though the bivariate analysis indicated that both were signiﬁcantly correlated with policy
adoption, once the other factors in the model were controlled for, neither signiﬁcantly

contributed to the model (Table 5). Therefore, in the needs and product sector, the

 

2" This analysis used an Ordinary Least Square (OLS) Regression, produced using SAS software.

129

hypothesis that the level of need would signiﬁcantly predict of the level of policy

 

adoption was not supported.
Table 5. Policy Adoption Regressed on the External Competing Factors Model
R2 " .50
N= 72
intercept = -2.20 F value = 5.04 Pr < .000
Variable Coeff. SE Sig. Beta
Needs and Products Sector
Crime 0.00 0.00 0.17 0.15
Cases -0.01 0.14 0.93 -0.01
Structure and Infrastructure Sector
Information Technology 0.35 0.10 0.00 ** 0.33
Weed and Seed 2.09 0.49 0.00 ** 0.45
SACSI 1.90 0.59 0.00 ** 0.32
Density 0.01 0.00 0.69 -0.05
Human Factors Sector
Gun Law 0.01 0.01 0.57 0.06
Racial Threat -O.71 1.34 0.61 -0.06
Punitiveness 0.00 0.00 0.08 -0.21
Resources and Survival Sector
Correctional Resources -4.89 0.00 0.03 * -0.26
Allocation -2.50 0.00 0.34 010
Politics 0.08 0.32 0.80 0.02
** p< .01
* p< .05

Hypothesis 1 B. Within the structure and infrastructure sector of the model, the greater
the congruence of the competencies and the congruence of the
structures within the adopting jurisdiction with those needed for policy
implementation, the greater the level of policy implementation.

In the inﬁastructure sector, all factors except population density contributed

signiﬁcantly to the model. The level of information technology (,6 = .33) and prior
experience with a Weed and Seed (,6 = .45) or SACSI program (,6 = .32) were all

signiﬁcant. This supports that portion of the hypothesis that higher policy adoption is

130

associated with technological competence and with prior experience implementing
collaborative criminal justice policies. However, the portion of the hypothesis that low
population density would make it more difﬁcult to structure a policy target was not

supported.

Hypothesis 1 C Within the human factors sector, the greater the congruence of public
attitudes with the mission of the proposed policy, the higher the levels
of policy adoption.

For the human factors sector, it was theorized that the attitudes of the public and
the positions of public interest groups could facilitate policy adoption or inhibit it. PSN
is a policy led by the federal prosecutor and with a core element of enhanced prosecution
of gun offenders who violate federal law. Therefore, it is theorized that districts in which
gun laws are less restrictive than federal laws would have constituent groups that would
view PSN as counter to local attitudes. The results of the regression analysis do not
support this contention.

Similarly, while PSN does support prevention programs, federal prosecution and
the collaboration of law enforcement and correctional agents (probation and parole) to
assure arrest and prosecution are core elements of the policy. Therefore, a state with
greater support for punishment, operationalized as the per capita imprisonment rate, is
theorized to positively inﬂuence support for PSN. However, as with gun laws, this was
not supported (Table 5).

The third variable, racial threat, was similarly postulated to be negatively related
to support for PSN. If the majority (white) population felt a high level of threat from the
minority (non-white) population, it is proposed that this threatened population might

resist any federal program that they viewed as limiting their right to buy and bear arms to

131

 

 

protect themselves from the perceived threat. This third variable, as with the previous

two, did not signiﬁcantly contribute to the model.

Hypothesis 1D. Within the resources and survival sector of the model, the greater the
degree to which the policy increases the resources and survival
capacity of the adopters, the greater the level of policy adoption.

There was limited support for this hypothesis. In the resources and survival
sector, political party alignment and the amount of the allocation did not contribute
signiﬁcantly to the model. However, the measure of current ﬁscal effort did”. Those

districts with the lowest levels of per inmate ﬁrnding adopted PSN at higher levels

(,6’ = —.26). This supports the contention that lower ﬁscal support for corrections may be

related to enhanced PSN policy adoption as a means enhance the survival prospects
elected or politically appointed agency leaders. Increasing federal prosecution of gun
offenders could shift the cost of incarceration away from local and state coffers to the
federal system. Collaborating with social support or other prevention and social
intervention entities also could hold the promise of shifting costs away from, or
preventing the expenditure, of criminal justice system ﬁrnding. In either case, decreasing
the strain and/or increasing the resources for the state and local criminal justice systems

could lower the actual and perceived risks and enhance policy adoption.

 

25 The word “eﬂ’ort” is used as it is in budgeting to denote the current amount of ﬁscal or other resources
put to a particular purpose. This captures the concept behind the construction of the variable which is to
ﬁnd a means to indicate the degree to which the jurisdiction is both willing and able to support the criminal
justice system. Lack of ﬁscal effort indicates a low level of resources available to, in this case, correctional
systems.

132

Mapping Competing External Factors

To continue the investigation of the patterns of external competing factors that
could inﬂuence the level of policy adoption, a modiﬁed version of Quinn’s (1991) sector
map was constructed. This mapping technique has two major purposes. The mapping of
variables or factors within each of the quadrants helps to visualize the inﬂuence of factors
and sectors in order to spur ﬁlrther analysis and to focus the researcher on potentially
interesting relationships among factors or sectors that warrant further investigation.
Secondly, to help inform decision-makers and practitioners, the maps provide a more
accessible, though simpliﬁed, visual representation of the relationships among variables

captured more richly in a regression analysis.

Table 6. Lower Bound of Quartiles for Independent Variables

 

 

Lower Bound of Quartile l 2 3 4
Needs and Products Sector
Crime 43.58 318.33 466.81 626.09
Cases 0.50 1.27 2.68 3.73
Structure and Infrastructure Sector
Information Technology 2.00 6.50 7.00 7.25
Weed and Seed 0.00 0.25 0.50 0.75
SACSI 0.00 0.25 0.50 0.75
Density 1.13 53.06 96.95 230.98
Human Factors Sector
Gun Law -10.00 —4.00 2.00 17.25
Racial Threat -0.09 0.26 0.35 0.44
Punitiveness 114.61 313.55 386.06 482.45

Resources and Survival Sector
Correctional Resources 9,130.97 17,514.61 23,794.56 29,244.19
Allocation 285,000.00 285,001.00 432,015.00 868,276.50
Politics 0.00 0.50 1.00 1.50

133

Figure 17.

Independent Variable Means of the
Highest and Lowest Quartile of Adopters

External Competing Factor Maps Comparing the

 

 

 

 

 

 

 

 

 

 

 

Race

 

 

 

 

 

 

 

 

 

Human Factors Resources and Human Factors Resources and
Sector Survival Sector Sector Survival Sector
Punitive Resources Punitive Resources

 

  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Crime
Densit
SAC SI
Weed &
Structure and Needs and Structure and Needs and
Inﬁ'astructure Products Inﬁ'astructure Products
SCCtOl' Sector swmr Sector
Lowest Adopters Highest Adopters

 

 

 

 

 

To create the maps, four sectors were created and the variables, or competing
factors, were placed within the sectors. Using the data from all the districts, four levels
were constructed and represented by concentric circles on the map. For each independent
variable, cases were grouped into quartiles. An upper bound was found for each quartile,
and that became the scale for each level on the map. For example, for violent crimes per
100,000 in the population, one-quarter of the districts felt below 318.3, so values between
the minimum of 43.6 and 318.3 were scored as a level one on the map (Table 6).

Dummy variables cannot be similarly scaled, so instead the levels were simply divided

134

into four equal values.” Again, following the Quinn protocol to examine and contrast the
inﬂuence of competing factors on “effective” and “ineffective” organizations, districts in
the highest quadrant of policy adoption success were aggregated and mapped and
compared to those in the lowest quadrant (Quinn, 1991, p. 95). For each group, the high
and low adopters, the group mean for each of the independent variables was computed
and mapped. As noted in Figure 17, the maps visually illustrate the results of the

regression analysis and highlight the inﬂuence of the structure and infrastructure sector’s

inﬂuence on policy adoption.

Summary of Findings for the Broad Test of the Model

While the overall model predicted half of the variation in the level of policy
adoption, some sectors contributed little. The human factors sector, perhaps because the
external human factors, measured by legal and administrative actions at the state level, do
not truly reﬂect district factors. Alternative concepts, measures, and interpretations will
be discussed in the ﬁnal chapter. In the resources and survival sector, one variable, the
level of current correctional resources, did rise to signiﬁcance. While no variable in the
needs and products sector contributed signiﬁcantly to the model, the bivariate
correlations indicated a relationship, prior to the inclusion of other factors. The maps
reinforce the ﬁndings, again indicating the preeminence of the structure and infrastructure
sector and the importance of prior program experience and public agency adoption of
information technologies as the two factors that prime PSN adoption. Harkening back to
the literature on policy adoption, research ﬁndings suggest that structural factors may

create a tipping point for adoption. Examples of policy adOption failure in both health

 

26 For the politics variable, only three values were possible. Like the dummy variables. values were divided

135

care and criminal justice systems was found in entities in which public attitudes and
professional standards supported the adoption, a need existed, and resources were
provided. In those studies, traditional protocols and organizational support structures,
such as accounting and data systems, were incongruent with the new policy or so poorly
developed that the entity could not support change (Cunningham et al., 2000; Morley et
al., 2003). To explore the idea of the structure and infrastructure sector as having a

priming effect on the other sectors in the model, ﬁrrther analyses were conducted.

Subgroup Analysis: High and Low Technology Groups

Since aspects of the infrastructure sector appear to be signiﬁcantly associated with
the degree to which PSN is adopted, this sector was explored ﬁlrther. Districts were
divided into two groups, low and high information technology groups.27 The differences
between measures of central tendency and dispersion policy adoption in all districts and
in the high and low technology subgroups are illustrated in Table 7. Since original scores
for information technology were discrete numbers, the groups could not be divided into
equal subgroups (Table 7). While this limitation exists, t-tests indicated that there is no
signiﬁcant difference between groups on the independent variables at the .05 level.

However, population density did differ, if the signiﬁcance level is relaxed to .10.

 

into equal parts, and means for the groups charted accordingly.

27 To conﬁrm that that these subgroups did differ on policy adoption, a one-way analysis of variance
(ANOVA) was conducted, and the results indicate that the subgroups (high and low information
technology) differed signiﬁcantly (p<.01), with high technology associated with higher adoption

136

 

Table 7. Descriptives of High and Low Information Technology Subgroups

Measures of Central Tendency and Dispersion

 

 

 

 

 

 

Sector Low Technology

Variable N Mean Minimum Maximum SD
Needs and Products Sector

Crime 54 482.33 . 43.56 1,465.36 243.24

Cases 55 2.59 0.50 0.07 1.65
Structure and Infrastructure Sector

Density 55 175.22 1.13 1,023.66 231.04 l

Racial threat 55 0.33 -0.01 0.66 0.05
Human Factors Sector

Gun Laws 55 7.95 -10.00 76.00 21.99

Punitiveness 55 434.40 133.18 746.50 165.28

 

 

Resources and Survival Sector
Allocation 55 572,016.44 285,000.00 1,300,000.00 572,016.44
Resources 55 23,624.51 9,130.97 72,517.35 1,133.52

 

 

 

 

 

 

 

Politics 54 0.87 0.00 2.00 0.58
Sector High Technology

Variable N Mean Minimum Maximum SD
Needs and Products Sector

Crime 34 473.9 184.57 911.28 212.33

Cases 34 2.74 0.57 6 1.52
Structure and Infrastructure Sector

Density 34 ' 400.36 10.03 5,558.07 967.83

Racial threat 34 0.36 -0.09 0.76 0.08
Human Factors Sector

Gun Laws 34 11.41 -6 43 12.24

Punitiveness 34 360.02 114.61 775.1 112.19

 

Resources and Survival Sector
Allocation 34 614,404.32 285,000.00 1,300,000.00 309,308.79

 

 

 

 

Resources 33 25,726.79 14,104.57 54,614.13 7431.9
Politics 33 0.82 0.00 2.00 0.73
Frequencies for Dummy Variables
Sector Low Technology High Technology
N Freq. Percent N Freq. Percent
Structure and Infrastructure Sector
Weed and Swd 55 34
present 42 0.76 23 0.68
absent 12 0.22 9 0.26
missing 1 0.02 2 0.06
SASCI 55 34
present 5 0.09 5 0. 15
absent 50 0.91 29 0.85
missing 0 0.00 0 0.00

 

 

137

Regression analyses, regressing policy adoption on the external factors model
sans the information technology variable, were conducted on the low information
technology and the high information technology subgroups. Given the small numbers in
the subgroups and the small number of districts with SACSI programs (ten out of ninety
districts), the dummy variables for SACSI and Weed and Seed were reduced to a single

variable, indicating experience with, or lack of experience with, a similar program.28

High Information Technology Subgroup

When this model was applied to the thirty-four districts categorized as the high
information technology subgroup, the model predicted 80 percent of the variation in the
level of policy adoption. As indicated in Table 8, for those districts with a good
information technology infrastructure, the model is more predictive than it is when
applied to the population of all districts.

Within the needs and products sector, the level of violent crime contributes
greatly to the model (,8 = .71 ). This is in contrast to the ﬁndings when all districts were
analyzed. In that case, no variable in the needs and product sector signiﬁcantly
contributed to the model. In the structure and infrastructure sector, the programs
variable, which combined the Weed and Seed or SACSI dummy variables, remains
signiﬁcant ( ,6 = .37 ). This result begins to lend some credence to the possibility of a
tipping point or priming effect of a structure that is congruent and capable of supporting
change.

However, within the structure and infrastructure sector, one factor rose to

signiﬁcance, but in a direction counter to that predicted by the model. Population density

 

28 This reduced the degrees of freedom in the model from eleven to ten.

138

becomes a signiﬁcant contributor to the model (B = —.64 ). This result indicates that for
this subgroup, lower population density districts would have higher levels of adoption.

Diagnostics on the regression failed to show the effects of outliers or any other issue that
provided insight into this unexpected relationship. Therefore, further exploration of this

result was conducted and is explained following this discussion of the subgroup analysis.

Table 8. Subgroup Analysis: High Information Technology Group with Adoption
Regressed on the Simpliﬁed External Factors Model

 

 

R2 = .80
N= 25
intercept = -7.84
Fvalue= 5.91 Pr>F=0.000
Sector Variable Coeff. SE Sig. Beta
Needs and Products Sector
Crime 0.01 0.00 0.00 ** 0.71
Cases 0.36 0.26 0.18 0.25
Structure and Infrastructure Sector
Programs 1.26 0.59 0.05 * 0.37
Density 0.00 0.00 0.01 * -0.64
Human Factors Sector
Gun Law 0.07 0.03 0.08 0.34
Racial Threat -3.54 1.97 0.09 —0.31
Punitiveness 0.00 0.00 0.28 0.22
Resources and Survival Sector
Correctional Resources 0.00 0.00 0.20 0.30
Allocation 0.00 0.00 0.45 -0. 15
Politics 1.48 0.47 0.01 * 0.47
** p< .01
* p< .05

In the human factors sector, no variables contributed signiﬁcantly to the model (at
the p < .05 level). However, districts with more restrictive gun policies approached

positive signiﬁcance (p = .08), indicating that in those areas where there is support for

139

more restrictions on guns and legal penalties prescribed for gun law violators, PSN may
be less risky to adopt. Racial threat also approached signiﬁcance (p = .09) and in the
expected direction. Those districts in which racial threat levels were higher experienced
lower levels of policy adoption, providing some support to the theory that the dominant
group might view heightened federal efforts to control the sale and ownership of guns as
well as the aggressive prosecution of gun violators as inﬁinging on their right to arm
when a racial threat is perceived.

In the resources and survival sector, political alignment did signiﬁcantly

contribute to the model (,8 = .47 ), while the other two factors did not. This result also

provides some support for the idea that a good infrastructure may be a necessary, though
insufﬁcient condition for policy adoption, a priming effect. However, the failure of the
correctional resources factor to rise to signiﬁcance, as it did when all districts were

evaluated, appears in conﬂict with that priming effect.

Low Technology Subgroup

The model was less predictive for the low information technology subgroup than
it was for the high information subgroup or for all the districts. For the low technology
subgroup, the model predicted 47 percent of the variation in policy adoption, as opposed
to 50 percent for all the districts and 80 percent for the high information technology
subgroup (Table 9). For this subgroup, none of the variables in the needs and products
sector and no variables in the human factors sector were, or even approached,
signiﬁcance.

In the infrastructure sector, the presence of a similar program provided the highest

contribution to the model ( ﬂ = .59, p < .000 ). This is the one variable that was signiﬁcant

140

in all three analyses: the full population, the high information technology subgroup, and

the low information technology subgroup. Population density, the other factor in this

sector, was not signiﬁcant. In the human factors sector, no variables were signiﬁcant. In

the resources and survival sector, low investment in correctional resources was associated

 

 

Table. 9. Subgroup Analysis: Low Information Technology Group with Adoption
Regressed on the Simpliﬁed External Factors Model
R2 = .47
N= 46
intercept = 1.20
F value = 5.91 Pr > F = 0.005
Sector Variable Coeff. SE Sig; Beta
Needs and Products Sector
Crime 0.00 0.00 0.47 -0.12
Cases -0.04 0.16 0.81 -0.04
Structure and Infrastructure Sector
Programs 1.78 0.43 0.00 ** 0.56
Density 0.00 0.00 0.22 0.20
Human Factors Sector
Gun Law 0.00 0.01 0.69 -0.06
Racial Threat 2.05 1.81 0.26 0.17
Punitiveness 0.00 0.00 0.23 -0.19
Resources and Survival Sector
Correctional Resources 0.00 0.00 0.02 * -0.36
Allocation 0.00 0.00 0.30 0.17
Politics 0.02 0.37 0.95 0.01
** p< .01
* p< .05

with higher adoption. This reﬂected the results in the full model, but the correctional

resources factor did not signiﬁcantly contribute to the model for the high information

technology subgroup. The possible reasons that low investment in correctional systems

141

may be associated with adoption success in low technology, but not high technology

subgroups, will be explored in the ﬁnal chapter.

Comparison of the High and Low Information Technology Subgroups

A comparison of the results of the regression analyses on the low and high

information technology subgroups is illustrated in Figure 18. Not only does the factor of

information technology have an effect on adoption (Table 5), but for those districts with a

higher level of information technology, the model becomes more predictive. Within the

context of districts with a better information infrastructure, factors relating to all the other

sectors reach or approach signiﬁcance. In contrast, for districts with a less well-

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 18. Comparison of the External Competing Factors Model
for Low and High Information Technology Subgroups
Human Factors Resources and Human Factors Resources and
Sector Survival Sector Sector Survival Sector
Race

 

  
 

Resources
(p <05)

 

 

 

 

 

 

 

 

 

 

 

 

  
 

Politics
0) <05)

 

 

 

 

 

   

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Crime
Programs (P ('01)
(p < .01)
Structure and Needs and Structure and Needs and
Infrastructure Products Infrastructure Products
Sector Sector Sector Sector
Low Information Technology High Information Technology

 

 

142

developed information infrastructure, policy adoption levels will be lower overall (Table
5), and the model is less predictive. It is interesting to note that in this low information
infrastructure subgroup, experience with programs is the most signiﬁcant contributor to
the model. This reinforces the concept of the importance of structural and infrastructure
components and competence to facilitate or prime policy adoption. This ﬁnding suggests
that for those districts with a poor information technology infrastructure, prior experience
with a similar program may be the key to overcoming barriers to implementation.

The resources and survival sector provides an interesting comparison. For low
information technology districts, the lack of correctional resources signiﬁcantly
contributes to the model, while in high technology districts political alignment is
important. Why low ﬁscal effort and the potential to shift costs away from local and state
coffers should be linked to success in low technology districts, while enhanced political
survival by supporting programs aligned with the party of local elected ofﬁcials should
be associated with success in high information technology districts is not immediately
obvious. This is discussed ﬁlrther in the ﬁnal chapter.

Overall, the structure and infrastructure sector appears to play a critical role in the

success or failure of PSN adoption.

Further Investigation of the Effect of Population Density

Before leaving the subgroup analysis, there remains to be addressed the
unexpected direction and signiﬁcant inﬂuence of population density for the high
information technology subgroup. Effectively, this ﬁnding would indicate that for those
districts with a better information infrastructure, low population density is associated with

policy adoption success. This is counter the hypothesis related to that sector.

143

 

One possible explanation is that population density fails to capture some
important aspect of the structure of the district. To follow that line of reasoning, districts
were assessed categories, based on United States Census Bureau’s deﬁnitions (USCB,
2000).” The criteria for rural was an overall population density of less than 1,000
residents per square mile. Urban cities are deﬁned as those with a population above
50,000. Combining those two deﬁnitions, three categories of districts were deﬁned.

The ﬁrst category is the rural district. These districts have a population density
below 1,000 and no city with a population of 50,000 or more. Five districts, of the
ninety, fall into that category. An example would ‘be the Northern District of West
Virginia. The population of the district is approximately 800,000 with about 64 residents
per square mile and the largest city, Wheeling, has a population just over 30,000.

The largest group is comprised of the seventy-nine districts that do not have an
overall population density of more than 1,000 residents per square mile, but do have one
or more cities with a population about 50,000. This category will be called mid-urban.

The third category is the truly urban district. Six districts have a population
density above 1,000 residents per square mile and a city or cities with a population above
50,000. An example is the Eastern District of New York. The district has almost eight
million residents, and a population density of 1,417 people per square mile. The largest
city (or Borough in this case) is Brooklyn, with a population of nearly 2.5 million.

As was noted earlier, at the .10 level of signiﬁcance, high and low information
technology groups differed on the independent variable of population density. Low

information technology levels were associated with low population density and high

 

39 The census categories are based on cities or metropolitan areas, not on regions 'or districts. so the density
and population sizes were used as a means to translate an industry standard and apply it to a federal judicial

144

 

information technology with high population density. To understand how the categories
of rural, mid-urban, and urban might explain the negative association between population
density and policy adoption in the high information technology subgroup, the
relationships between the rural-urban factor, information technology, and adoption were
explored. Since the rural and urban groups are very small, statistically signiﬁcant results
are difﬁcult to obtain. However, simple cross tabulations indicate relationships of
practical signiﬁcance.

First, of the ﬁve rural districts, all ﬁve were in the low information technology
subgroup. Of the urban districts, only one did not fall in the high technology subgroup.
Second, the rural districts varied in the level of policy adoption, with three in the lowest
third of policy adoption, two in the middle third, and one in the highest third. In contrast,
all the urban districts were in the middle range of policy adoption.30 This leads to the
possibility that the structure of very high density, high population districts may create a
special environment for policy adoption, preventing low or symbolic implementation but
with some barriers to ﬁll] implementation.

To investigate the inﬂuence of the model if the extreme cases of rural and urban
are treated as special cases, regression analyses were run, omitting the rural and urban
districts. While overall the predictive capacity of the model did not change nor did the
factors signiﬁcantly contributing to the model, in the case of the high technology
subgroup, the signiﬁcance of population density disappeared. In the ﬁnal chapter, the

possibility of the effect of very high density, high population structures will be discussed.

 

district
3° One urban district did not have a value for information technology and hence, could not be compared on
this variable.

145

 

Summary for Hypothesis One

The model has been shown to have predictive power. External factors in the
structure and inﬁ'astructure sector appear to have the greatest inﬂuence on policy uptake.
In the overall test of the model, having experience with similar programs, and having a
better information inﬁ'astructure are associated with higher levels of policy adoption.
Attitudes and human factors, at least as conceptualized and measured in the model, did
not signiﬁcantly inﬂuence policy adoption. Perhaps the most intuitive factor, the level of
need, also was not associated with the level of adoption. The amount of ﬁrnding
provided as an incentive and the possible political gain for adopting a policy championed
by those of the same political party were not signiﬁcantly associated with policy adoption
levels. However, the lack of current ﬁscal effort, measured by low funding for
corrections, does appears to be associated with a greater level of adoption. While this
study conceptualizes this factor as part of the resource sector, it also could be viewed as
part of the needs sector. If correctional resources are lacking, the need to shift the cost of
incarcerating gun offenders from the state to the federal level could be seen as a
motivating force for policy adoption. Taking in total, the broad analysis indicates that
policy adoption is more successﬁrl in districts with more experience with PSN-like
programs, with a better public information technology infrastructure, and if current
spending on criminal justice (speciﬁcally the correctional system) is low.

The subgroup analysis provides some of the most interesting prospects for the
employment of the model. Subgroup analysis lends support for a priming effect for the
structure and infrastructure sector. Low information technology districts are less

successful overall. Capacity, in the form of another infrastructure variable of prior

146

experience, gives these districts the best chance of success. For these districts, low
current resources invested in the criminal justice system appears to motivate success.
After separating the districts into high and low information technology competencies, the
subgroup analyses indicates for districts with a good information infrastructure, other
factors in the model then become signiﬁcant. If that portion of the infrastructure is in
place to support policy adoption, experience with similar programs is associated with
greater success.31 Once that capacity exists, human factors begin to emerge as possible
predictors. Tougher gun laws and lower racial threat were associated with more success,
if the standard for signiﬁcance were relaxed to p <. 10. Political capital, earned for
championing programs linked to the elected ofﬁcials’ political party also rose to
signiﬁcance for this higher infrastructure group. While lower population density was
associated with higher success, this relationship disappears if extremely high-density
districts are treated as a special case. The policy implications of this will be discussed in

more detail in the following and ﬁnal chapter.

Hypothesis 2. The external competing factors model will differentially affect the
elements of policy adoption.

The theory informing the external factors model would predict that the elements
of policy would be differentially affected by external factors due to differing risks
associated with each of the policy elements. The risk of each element would depend on
the ability to perform tasks associated with the various policy elements and the potential

gains or losses associated with the adoption of the various elements. A bivariate analysis

 

3‘ A similar subgroup analysis was attempted for the variables (rupturing prior experience with similar
programs (Weed and Seed and SACSI) but in both cases, subgroups were wildly unbalanced, with one

147

on the elements of policy adoption indicate that while the integration of research into
strategic planning is signiﬁcantly correlated with collaborative implementation and
enhanced federal prosecution, collaborative implementation and enhanced federal

prosecution are not signiﬁcantly related (Table 10).

Table 10. Correlations Matrix for Policy Adoption Elements

 

 

 

Research & Collaborative
Planning Implementation
Collaborative Implementation Pearson Corr. 0.34 **
Sig. (2-tailed) 0.00
N 79
Prosecution Pearson Corr. .25 * .04
Sig. (2-tailed) .03 .70
N 79 90
** p<.01
* p<.05

To begin to untangle the different inﬂuences on each element of policy adoption,
separate regression analyses were conducted using each of the three policy elements as
the dependent variable. The results, illustrated in Table 11, lend support to Hypothesis 2,
which proposes that the speciﬁc mission and competencies associated with each of the
policy elements create different perceptions of risks and beneﬁts and are associated with
different levels of policy adoption. The comparisons of the predictive ability of the
model and the variables that contribute to the model’s explanatory power reﬂect
differences between the adoption variable and each of the three elements used to form
that composite measure.

The results of each regression analysis will be examined individually. This comparison

(Table 11) illustrates the model predicted more than half of the variation in collaborative

 

subgroup so small that regression analysis could not be conducted. For the SACSI program, only 10 of the
ninety districts had a SACSI program. For Weed and Seed, 15 districts had data available for an analysis.

148

 

implementation, roughly a third of the variation in research integration into strategic
planning, and one-fourth of the variation in enhanced federal prosecution. Also
supporting the hypothesis that variables will differ in their inﬂuence on various elements

of policy adoption is the comparison across regresSion analyses which shows that no

 

 

 

 

 

 

Table 11. Comparison of Standardized Coefﬁcients (Beta) for Regression Analyses
of Policy Adoption and the Three Adoption Elements
Adoption Collaborative Research Enhanced
Implementation Integgation Prosecution

Sector Variable Beta Beta Beta Beta
Needs and Products Sector

Crime 0.15 0.11 -0.04 0.29 *

Cases -0.01 0.06 0.00 -0. 10
Structure and Infrastructure Sector

Information Technology 0.33 ** 0.02 0.38 ** 0.31 *

Weed and Seed 0.45 " 0.47 ** 0.31 * 0.10

SACSI 0.32 ** 0.22 * 0.23 * 0.19

Density -0.05 0.05 0.03 -0. 18
Human Factors Sector

Gun Law 0.06 0.13 0.12 -0.04

Racial Threat -0.06 0.13 0.04 -0.28 *

Punitiveness -0.21 -0.21 -0. l3 -0. 10
Resources and Survival Sector

Correctional Resources -0.26 * -0.27 * -0.14 -0.09

Allocation -0.10 0.26 * -0.10 024

Politics 0.02 -0.03 0.03 0.07

R2 0.50 0.51 0.34 0.25

** p< .01

* [K .05

single variable was signiﬁcant across all of the three policy elements.

Hypothesis 2A. Adoption of collaborative programs should be associated with all
sectors and factors within the model

Collaborative implementation of programs requires multiple agencies to consider

PSN participation a positive, rather than risky undertaking. Given that, it is theorized that

149

V

all sectors and variables within the external factors model should all inﬂuence the level of
adoption of this policy element. The regression analysis deﬁning collaborative
implementation as the outcome or dependent variable lends some support for this
hypothesis. The model explains just over half the variation in collaborative
implementation of programs. However, the variables in the needs and products sector did

not rise to signiﬁcance. True to the hypothesis, the experience with similar programs did

Table 12. Collaborative Implementation Regressed on the External Competing

 

 

Factors Model
R2 = .51
N= 81
intercept = 2.93
F value = .07 Pr > F < .0001
Variable Coeﬂ‘. SE Sig. Beta
Needs and Products Sector
Crime 0.00 0.00 0.29 0.11
Cases 0.07 0.11 0.52 0.06
Structure and Infrastructure Sector
Information Technology 0.02 0.08 0.79 0.02
Weed and Seed 1.94 0.39 0.00 ** 0.47
SACSI 1.16 0.47 0.02 * 0.22
Density 0.00 0.00 0.64 0.05
Human Factors Sector
Gun Law 0.00 0.01 0.89 0.13
Racial Threat 1.47 1.07 0.20 0.13
Punitiveness 0.00 0.00 0.06 -0.21
Resources and Survival Sector
Correctional Resources -4.56 0.00 0.01 * -0.27
Allocation 1.32 0.00 0.02 * 0.26
Politics -0.09 0.26 0.74 -0.03
** p< .01
* p< .05

150

signiﬁcantly contribute, although the other variables in the structure and infrastructure
sector did not (Table 12). Human relations or attitudinal variables did not contribute to
the model. In the resources and survival sector, the lack of current ﬁscal effort and the
amount of PSN resources did contribute signiﬁcantly to the model. Political congruence
did not.

Therefore, the ability for districts to form collaborative groups, able to implement
programs across agencies, does not appear to depend on the level of need for the policy
product. The ability to engage multiple agencies is linked to prior experience with
similar endeavors. It also appears to be fueled by the level of resources and the potential
ﬁscal beneﬁt to the district and district agencies and the lack of ﬁscal effort and resources
currently available. This history of collaborative efforts and the potential ﬁscal gain
appear to be more inﬂuential than attitudes or the actual, crime-driven level of need for
the speciﬁc policy. One interesting result is that the level of information technology in
the district does not contribute signiﬁcantly to collaborative implementation, while it
does in overall adoption and in the two other policy elements. One possible explanation
is that this collaboration does not necessarily have to be strategic or research-based.
Collaborative strategies also could be based on working relationships within existing
networks and based on experience, rather than on strategic plans.

Hypothesis 2B. Adoption of research integration into strategic planning should be most
inﬂuenced by the structure and inﬁastructure sector within the model

To integrate research into strategic planning, having both a technical
infrastructure and a culture that supports research-driven decision-making would be

hypothesized to enhance this policy element. However, applying research to gun

151

Table 13. Research Integration Regressed on the External Competing Factors

 

 

Model
R2 = .34
N= 72
intercept = 4.56
F value = 2.56 Pr > F = 0.008
Variable Coeff. SE Sig. Beta
Needs and Products Sector
Crime 0.00 0.00 0.78 -0.04
Cases -0.01 0.26 0.98 0.00
Structure and Infrastructure Sector
Information Technology 0.65 0.19 0.00 ** 0.38
Weed and Seed 2.34 0.93 0.02 * 0.31
SACSI 2.27 1.11 0.05 * 0.23
Density 0.00 0.00 0.08 0.03
Human Factors Sector
Gun Law 0.02 0.02 0.36 0.12
Racial Threat 0.75 2.55 0.77 0.04
Punitiveness 0.00 0.00 0.32 -0. 13
Resources and Survival Sector
Correctional Resources -4.16 ..0.00 - 0.31 -0. 14
Allocation -8.95 0.00 0.48 -0. 10
Politics 0.15 0.60 0.81 0.03
** p< .01
* p< .05

violence issues would involve competing inﬂuences from all the sectors and factors in the
model, and it would be hypothesized that all the factors in the model would assert some
inﬂuence. When research integration is regressed on the factors in the model, the
hypothesis is partially supported (Table 13). With the exception of population density,

all the factors in the structure and infrastructure sector contribute signiﬁcantly to the

model. This lends credence to the postulated relationship between a culture and

152

infrastructure supporting research-driven decision-making. However, none of the

remaining variables in the model was signiﬁcant.

Hypothesis 2C Adoption of enhanced federal prosecution should be most strongly
inﬂuenced by the factors in the needs and products sector and those
in the resources and survival sector.

Increasing federal prosecution of gun offenders can be accomplished with little in the

way of collaboration across agencies in a locality. If resources were sufﬁcient and were

 

 

Table 14. Enhanced Federal Prosecution Regressed on the External Competing
Factors Model
R2 "' .25
N= 81
intercept = -0.30
Fvalue= 1.89 Pr>F=0.0513
Variable Coeff. SE Sig. Beta
Needs and Products Sector
Crime 0.00 0.00 0.03 * 0.29
Cases -0.06 0.07 0.43 -0. 10
Structure and Infrastructure Sector
Information Technology 0.14 0.05 0.01 * 0.31
Weed and Seed 0.20 0.24 0.41 0.10
SACSI 0.51 0.29 0.09 0.19
Density 0.00 0.00 0.17 -0. 18
Human Factors Sector '
Gun Law 0.00 0.01 0.75 -0.04
Racial Threat -1.51 0.66 0.03 * -O.28
Punitiveness 0.00 0.00 0.46 -0. 10
Resources and Survival Sector
Correctional Resources ~7.67 0.00 0.49 -0.09
Allocation -6.02 0.00 0.09 -0.24
Politics 0.10 0.16 0.54 0.07
** p< .01
* p< .05

153

targeted toward the hiring and training of federal prosecutors, and providing there was
sufficient gun crime to allow for an increase in such prosecutions, this policy element
could be achieved. As will be discussed in the analysis of Hypothesis 3, this policy
element is the one that is hypothesized to be the most likely candidate for recognition-
based, or narrow, adoption and adoption to the exclusion of other elements. In practice,
narrow policy adoption also could involve recognition-based groups, limited to
prosecutors and law enforcement. The ability and willingness of local and state
prosecutors and law enforcement to support protocols to apprehend and prosecute gun
offenders is hypothesized to be affected by all the factors in the model. Therefore, all
elements of the model are theorized to inﬂuence the acceptability of prosecuting gun
offenders, but the need for such prosecution and the resources needed and provided are
hypothesized to be most critical.

When enhanced prosecution is regressed on the model, the hypothesis is
supported only in part. One variable in the needs and products sector, crime, does
signiﬁcantly contribute to the model. This does support the premise that in order to
enhance federal prosecution, some need to do so must be present. However, none of the
resource variables signiﬁcantly contributes to the model (Table 14).

Enhanced prosecution is associated with one of the attitudinal factors in the
human factors sector, indicating that enhanced federal prosecution is more likely in
districts in which the perceived racial threat is low. This is the only analysis in which this
variable rose to signiﬁcance. In the overall model, it was proposed that PSN adoption
would be facilitated in those districts in which racial threat was low. It was hypothesized

if dominant groups (white in this case) constructed or perceived the non-dominant (non-

154

white) group as criminal, that the dominant group would oppose federal government
efforts to restrict sales, ownership, and use of ﬁrearms, a means to prevent or control that
threat. For example, in a jurisdiction in which minorities were not incarcerated at a rate
beyond their population percentage, one could argue that this is some evidence that
nonwhites are not symbolically constructed as a criminal group. If a jurisdiction
incarcerates nonwhites at a percentage far above their population percentage, an
argument can be made that a dynamic has been created in which the nonwhite group has
been constructed as a criminal group. In this case, the model predicts that this
construction of a dominant versus a criminal group will increase the public perception of
risk. This risk will lead the dominant group to oppose any restriction on their ability to
defend themselves.

While this reasoning ﬁts the model, other views of the relationship between racial
threat and PSN adoption are possible and worthy of investigation and discussion. IfPSN
is perceived not as a policy to restrict gun owners’ rights, but rather as a way to target and
punish gun offenders, it could be argued that racial threat would increase support for PSN

adoption. This and other possible views of the human factors variables will be further

discussed in the ﬁnal chapter.

Summary of the Findings for Hypothesis Two

Policy elements, as proposed in hypothesis two, are differentially inﬂuenced by
factors in the model. Important for the construction and for the interpretation of
measures of success in policy adoption, the differences indicate that, even though
elements of a policy may be logically and legally related to the incentivized whole,

jurisdictions’ willingness or ability may vary across elements. The model has the most

predictive power for the element of collaborative implementation, which requires districts
to actively engage multiple agents and agents in the district in programs. The critical
sectors for this element of adoption are structure and infrastructure and resources and
survival. In practical terms, this means that districts succeed at bringing together
multiple people and instituting some collaborative efforts if they have done something
similar in the past. Such experience also was positively associated with higher levels of
integration of research into strategic planning. However, this relationship did not hold for
enhanced federal prosecution.

Success in collecting and integrating information into a planning process to
address gun crime was inﬂuenced by the strength of the existing public information
infrastructure. This also was true for increasing federal cases against gun criminals, but
the information infrastructure did not contribute signiﬁcantly to the model when applied
to collaborative implementation.

In terms of resources, success improves as the ﬁscal incentives from the federal
government grow, and if the current spending on related programs is low. The keys
appear to be experience and the provision of resources where resources are needed.
Resources played no signiﬁcant role for either enhanced prosecution or research
integration. Only for the element of enhanced prosecution did a human factor variable
come into play. Lower racial threat was associated with higher levels of prosecution.

Overall, the lack of a bivariate correlation between enhanced prosecution and
collaborative implementation, as well as the fact that no one variable played a signiﬁcant

role across all three regression analyses, lends support to the hypothesis that each element

156

of policy adoption is subject to the inﬂuence of the competing factors that are associated

with the potential cost and beneﬁt of adoption of that element.

Hypothesis 3. Patterns of policy adoption will be found that reflect symbolic
groups, recognition-based decision-making groups, and strategic
groups.

Theory on ways in which groups deal with the risk and costs of participating in
collaborative enterprises reﬂect three patterns of group interaction: symbolic,
recognition-based groups, and strategic groups. For Project Safe Neighborhoods, formal
reports indicate that no districts have failed to adopt PSN, at least to some degree.
Despite this, it is hypothesized that a pattern of symbolic participation will be found.
Another means of reducing the cost and risk of investing in incentivized, cross-entity
collaboration is to limit active participation to a recognition-based group. In other words,
a group in which members share very similar core goals, lexicon, and protocols can
communicate and operate at a lower cost than groups that must expend resources on
ﬁnding or developing common goals, common protocols, and common communication
systems. While the dynamics of groups will differ, and these patterns are proposed to fall
along a continuum, if the hypothesis is correct, the expectation would be that examples of
extremes, or pure forms, of these groups could be distinguished from the overall pattern
and ﬁ'om one another.

Given the nature of PSN, the three patterns reﬂecting risk reduction group
dynamics are hypothesized to be as follows. Symbolic groups, in their purest or most
extreme form, would be those that do not implement any element of the policy.
Conversely, the strategic groups would be those that implement all policy elements ﬁrlly.

In theory, narrowly focused or recognition-based groups could involve any sub-system or

157

closely linked sub-systems, but in practice for PSN, the most likely scenario is a federal
prosecution-focused group. Since PSN is based out of the Ofﬁce of the United States
Attorney (USAO) and the USAO is accountable for reporting results, the most likely
scenario for a limited and targeted group would be one centered on the protocols and
processes of federal prosecution.

To determine if examples of these extreme forms could be found and compared,
the districts were grouped into thirds on each of the three policy element scores. If
symbolic adoption occurred in PSN, some districts Should score low across all three
elements. If full and strategic adoption occurred, some districts should score in the high
category across all three elements. If recognition-based decision-making groups arose,
focused on federal prosecution, some districts should reﬂect a pattern of high enhanced
federal prosecution but low scores on research integration and collaborative
implementation. In addition, since the analysis supported the hypothesis that enhanced
federal prosecution was inﬂuenced by the level of crime, a fourth pattern was added to
explore the inﬂuence of the level of crime on policy adoption. This pattern mirrored ﬁrll
adoption minus the prosecution component. Districts in this group were in the highest
third for research integration and collaborative implementation, but the lowest third for
prosecution. Presumably, these groups would have risk perceptions similar to those
scoring high in all categories, but differing in some context that affects their ability or
willingness to enhance federal prosecution.

To test this hypothesis, the districts were sorted into these categories and then
sorted into patterns. As noted in Table 15, pure forms of each of these hypothesized

groups were distinguishable for twenty-two districts, roughly one-quarter of all districts.

158

Table 15. Numbers of Districts in Each of the Pattern Categories

 

Pattern

All Low

High Prosecution, Low Research, and Low Collaboration
Low Prosecution, High Research and High Collaboration
All High

Oxmuiuiz

Those districts scoring high in all categories will be referred to as “strategic groups,”
those scoring high in research integration and collaborative implementation, but low in
enhanced prosecution as “strategic minus,” those scoring high in prosecution but low on
the other two elements as “recognition-based,” and those scoring low on all elements as
“symbolic.” Going back to the original deﬁnition of risk as “weighing the cost of the
strategy against the potential beneﬁts” (Crocker, 2003, p. 13) helps to put these results in
context. Using the mapping procedure outlined earlier in this chapter, the means of each

group were calculated and displayed on the maps in Figure 19.

The Hypothesized Functioning of Risk-Pattern Groups

Given that groups are proposed to ﬁrnction in response to perceived risk, or to the
potential costs and beneﬁts of participation, each of these risk- pattern groups would be
expected to exhibit functioning patterns to respond to risk conditions. One of the costs of
participating in collaborative, data-driven policies, such as PSN, is the cost of
information sharing. Research on decision-making under conditions of risk and
uncertainty indicates that, to be effective, an information sharing strategy requires an
initial investment that can be costly (Kamarck, 2005). Therefore, in terms of risk, the
costs associated with implementing PSN should be reduced for districts in which such an
inﬁastructure is in place. This ability to take in, translate, share, and employ data

strategically has been modeled in two related ways for risk-pattem groups. Comfort uses

159

the idea of a bowtie model, in which a system is set up with a hub, or knot in the bowtie,
that is the connection point where data is received, translated, and distributed in forms
useﬁrl to all participants (Comfort, 2002, 2005). Weber et al. (2003) use the model of
horizontal and vertical competencies, meaning the ability to ability to both share
information among members and put that data to use to target the goal of the policy.
Research indicates that the presence of a problem to be solved, relevant information to be

gathered, and peOple empowered and ﬁrnded to address the issue, is not associated with

 

strategic policy adoption unless those competencies are present (Weber et al., 2003).
Given that two-fold premise, one would expect that high ﬁrnctioning in all elements of
the policy would require horizontal and vertical competency. This can be thought of as
an infrastructure in which investments have already been made in the information
technology infrastructure and in the training of human resources to value and employ
data in decision-making. In the external factors model, this is reﬂected in the structure
and infrastructure factors of prior experience with similar programs and the level of
information technology.

Comparing these variables for the groups lends support for the models of vertical
and horizontal competency and the need for prior investment in data sharing and use. All
of the districts in the strategic group had prior experience with a similar program, and the
mean for the group on information technology was in the highest quartile, indicating a
prior investment in competencies that should lower the costs and the risk of PSN
adoption. The same pattern is present for the strategic minus group. All have experience
with similar programs, and the mean for information technology is in the highest quartile.

Contrast this with the maps for the symbolic and recognition-based groups. Symbolic

160

groups had a mean technology score in the lowest quartile, and only one of the ﬁve
districts had prior experience with a SASCI or Weed and Seed program. Similarly, the
recognition-based group had a mean score in the lowest quartile, and none of these

districts had past experience with a similar program.-

Figure 19. Competing Factors Maps of Policy Adoption by Group Type

 

Resources

 

 

 

 

 

 

 

Tech.

Dens'

     

Programs Programs

 

 

 

 

 

l Recognition-Based ] l Strategic Minus l

 

 

 

 

 

161

This map provides a good visual illustration of a risk reduction response in the
context of group decision making. The maps support the contention that the “risk of
adoption is related to the costs of information gathering and sharing plus the costs of
equipping people to use that data and participate in strategic planning. Lack of either
horizontal or vertical competencies increases the risk, due to the costs that would be
incurred in developing these competencies and in the potential costs associated with
failure if those competencies were not developed. This mapping supports the premise
that groups can limit this risk in two basic ways, either adopt the policy only symbolically
or engage in recognition-based decision-making. The symbolic group saves all the costs
of information sharing by decoupling goals from practice and not investing in or
attempting meaningful planning. The recognition-base group limits the costs by limiting
the group interaction to those who already share a common knowledge base and
protocols for planning. In this case, a prosecution-only strategy allows these recognition-
based groups to focus on only limited data, constrain the problem set to the goal of
enhanced federal prosecution, and interact only with members with whom and in a way
in which they are comfortable and familiar.

A second component that Should affect the manner in which groups react to risk is
the need for the product offered in the policy. High need for a gun crime policy, such as
PSN, should weigh the cost-beneﬁt calculation toward adoption. The map of the
strategic group appears to bear this out. The mean for these districts fell in the high
quartile for both crime and federal gun cases ﬁled prior to PSN’s initiation. For the
Strategic minus group, the results also seem consistent. Given that this group

successﬁrlly has implemented research-driven collaborative programs, but did not

162

increase federal prosecutions, appears to be consistent with a lower need than is present
in the strategic group.

Comparing overall violent crime rates among all groups, but the strategic group,
those rates do not differ practically or statistically. However, the level of federal
prosecution prior to PSN initiation raises some interesting issues regarding the
interpretation of variables. Higher per capita ﬁlings are found in the strategic and the
recognition-based groups. Lower per capita ﬁlings are found in the strategic minus and
the symbolic group.

This variable, federal case ﬁlings, may be reﬂecting two concepts and could be
thought of as belonging to two different sectors. The rate of federal prosecution could
reﬂect both the level of need, gun offenses in the district that meet the requirements for
federal prosecution, and prior experience, a well-established means of working with local
and state law enforcement and prosecutors to identify and refer cases to federal
prosecutors. Thus, the variable may relate to both the needs and products sector and the
structure and inﬁastructure sector. The risk-pattems in the maps ﬁt this model well.
Strategic groups can integrate information and programs to enhance prosecution into their
overall plan, since there is sufﬁcient need, and Since the cost of such programs is reduced
as investments in their development has already occurred. Strategic minus groups are
less likely to integrate enhanced federal prosecution programs into their plans because
either the need does not exist, or investment in and experience with such programs has
not yet occurred. Either the potential beneﬁt is perceived as small or the deﬁcits in the
infrastructure raise the cost of adoption. For symbolic groups, the lack of need and/or the

lack of experience raise the cost of ad0pting enhanced federal prosecution programs and

163

 

lower the potential payoff, thus increasing risk and decreasing policy adoption. For the
recognition-based group, this prior experience, coupled with need, creates a risk
environment in which the payoff is reversed from that in the symbolic group. Here need
is high, and the infrastructure to plan and implement this narrow strategy in already in
place. Potential beneﬁts with little additional investment would be expected.

The maps of these extreme cases support the theory underlying the risks
associated with population density. Strategic and strategic minus groups have denser
populations, while symbolic and recognition-based groups have lower population density.
It is theorized that it would be costly for low population density districts to determine a
target area, as the population is widely spread. In addition, since agents and agencies also
are widely spread, investment in collaborative planning meetings would be higher. Since
target areas, populations, and participating agencies could be widely separated
geographically, the cost versus the beneﬁt of planning and implementing cross-agency,
cross-jurisdictional programs might be might make such efforts cost-inefficient.

In terms of how patterns to limit risk are affected by human factors and by
resources, the maps provide little additional information. While lower current resources
for the correctional systems was associated with higher policy adoption in the overall
population, the relation of this factor to adoption in these maps of extremes is far less
clear. The relationship of some of these sectors to risk may be better understood at other
levels of the model, either in terms of risks within participating agencies or risks within
the task force structure.

The identiﬁcation and mapping of risk patterns also helps inform research aimed

at testing other levels of the model. In addition to mapping measures of central tendency

164

for the pattern groups, understanding how competing factors may inﬂuence these patterns
is also aided by adding a mapping of the spread of mean values among the group
members. Figure 20 provides maps for the recognition-based group. In the upper left-
hand corner is the mapping of the group means. The value for each variable for each
district in this category is then mapped separately.

The maps of each district in the recognition-based group provides conﬁrmation of
the assumption, from examining the group mean, that low information technology and
little prior experience with Similar programs increased collaborative and strategic
adoption costs for these districts. Uniformly, across all ﬁve districts, information
technology was in the lowest quartile and none of the districts had prior experience with a
SAC S1 or Weed and Seed Program. In addition, there was little variation in the federal
case variable, with three districts in the highest quartile and two in the third quartile.

For the purposes of further investigating the inﬂuences of other variables in the
competing factors model, the maps steer the researcher toward investigating the
inﬂuences of existing correctional resources. Two districts are in the highest spending

quartile, while three are in the lowest.

Summary

The model of external competing factors holds promise for further research and
for informing policy whose aim is to address pressing problems in the criminal justice
arena. The overall model predicts half of the variation in policy adoption, a useful tool to
begin to ferret out jurisdictions primed for policy success, or failure. That priming
appears to be inﬂuenced by the presence of factors in the structure and infrastructure

category related to horizontal and vertical competencies. These factors appear to relate to

165

riskepattems that make adoption more likely in districts in which the investments in these
competencies already have been made. This idea of printing based on the costs
associated with competencies is supported by the subgroup analysis in which the model

becomes more robust when applied to those districts in which components of this

infrastructure already exist.

166

Figure 20. Competing Factor Maps of the Recognition-Based Group and Bach
Group Member

Resources

 

 

 

 

 

 

 

 

 

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CHAPTER NINE:
CONCLUSIONS

Is PSN Being Adopted?

Revisiting the theories and research introduced in Chapter Two, PSN as a
federally incentivized-policy has been, according to official reports, universally adopted.
However, construction of the measures of success used in this research indicate that, as
institutional theory suggests, there may be differences between reports and reality. While
the incentivized policy, including all major elements of the policy, was adopted in some
districts, other districts implemented some aspects and not others. The degree to which
policy elements were adopted varied, and some districts failed to adopt any elements of
PSN. Viewed in the context of institutional theory, this result is not unexpected. For
agencies in the criminal justice sector, where results are often difﬁcult to ﬂame and
measure, agencies proﬁt from the legitimacy gained from adopting a nationally-touted
program. The factors that predict differences in the level of adoption, from decoupled
and symbolic, through full and strategic can be at least partially explained by the

framework of risk in the competing factors model.

Horizontal and Vertical Competencies and Costs

It is questionable if we have a task force in our district.
(Survey response from symbolic adoption district)

Despite reports of task force meetings and planning processes, the reality
reported by those surveyed, and hopeﬁrlly captured in the policy adoption measures, was
that membership and planning processes in task forces sometimes did not include much
in the way of meaningful activities. For members of symbolic groups, such as the

member quoted above, that generally meant that meetings never took place.

168

In contrast, members in some districts reported sophisticated, data-driven problem.

solving processes.

The research showed that a large percentage of homicides are gang-

motivated, so the next step was conducting gang audits, identifying

person-targets, and focusing enforcement on the violent few...

(Survey response from a strategic district)

The results of the various analyses in this study lend support to the risk
framework as a way to better understand why some districts would be willing and able to
adopt strategic planning and implementation processes, while others do not even attempt
to hold task force meetings. The most compelling ﬁndings were related to horizontal and
vertical competencies, the ability to both interact with others and share information to
solve a problem, and the ability to translate information and interaction into action that
achieves the goal. This study supports the idea that a jurisdiction in which task force
members have experience with group interaction and work in a culture in which
information systems and processes are more routinized and acculturated will be more
likely to have the horizontal and vertical competencies that enhance policy adoption
success. The absence of such competencies was associated with low levels of policy
adoption and with symbolic policy adoption. The analysis of subgroups of those in high
information and low information technology districts reinforces the notion that both
human experience with and the technological support for collaboration are necessary, if
not sufﬁcient, predictors of enhanced policy adoption success.

Turning ﬁrst to the issue of an existing information infrastructure that can support

data collection and use, districts with better infrastructures appear to be primed for policy

adoption. Those with poor infrastructure tend to start at a disadvantage. The information

169

technology measure was not a speciﬁc measure related to police or court information
systems, but rather a measure of the overall capacity and culture of information
technology in the public sector.

One research partner who was surVeyed noted that the task force in his district
was willing to use information to help identify problems and guide strategies, but basic
data collection, inﬁ'astructure, and interoperability problems among member agencies
created barriers. “Those barriers are essentially the result of capabilities and resources
rather than the lack of desire to cooperate.” Framing this in terms of risk, the lack of an
information infrastructure is not an easy or inexpensive barrier to overcome when
attempting to adopt a policy that hinges on research-driven planning. The results of the
analyses reinforce the concept of the importance of this infrastructure component.
Because districts with better information infrastructures experienced higher levels of
policy adoption success, this supports the idea that such deﬁcits create risk. When such
systems are not in place, the investment that would be required to support data-driven
change may far outweigh the ﬁscal support provided by the incentive and may outweigh
the perceived beneﬁt of the level of gun crime control offered by PSN.

I think that without an existing relationship between research and action

partners, evolution of an effective relationship takes a long time.

(Survey response from a low adoption district)

The second aspect of creating a state of horizontal and vertical
competency involves the human element. As the above survey response notes,
districts with little experience in collaborative and strategic planning efforts
struggled to adopt the strategy. This was, according to those surveyed, not due to

a lack of interest, but due to lack of understanding of how the process should

170

 

 

 

work, what roles members Should play, how information should be shared and
used, and how multiple goals and objectives could be blended into the PSN plan.

When prior investments had prepared and equipped management
information systems and human participants for adoption planning and
collaborative implementation processes, it is theorized that the costs of adoption
were reduced, leading to the ﬁndings of higher level of policy adoption and
adoption across all core elements. When these infrastructure components were
missing, districts adopted risk-patterns designed to lower adoption costs. These
risk-reducing patterns of interaction resulted in either symbolic adoption or
narrowly focused strategies. A researcher discreetly described the symbolic
nature of the task force in his district by saying, “It is questionable if there is a
task force in our district.” Such complete decoupling of goals from actions results
in symbolic adoption which certainly reduces information and planning costs.
Recognition-base decision making also reduces costs, but through a different
pattern of interactions. Recognition-based group interactions were also found to
be used by PSN districts as a risk-reduction strategy.

There is little formal activity in the form of a task force... Interaction

between the US. Attorney’s Office and the police department was

concerned primarily with developing a clear process for forwarding gun

cases to the US. Attorney’s Ofﬁce.

(Survey response from recognition-based district)

The survey response quoted above is a good example of cost reduction through

forming a recognition-based group. The mapping and evaluation of extreme districts

indicated that for districts with very low horizontal and vertical competency factors, a

narrow form of policy adoption was possible. By focusing only on inclusion of people,

171

 

ideas, problems, information, and protocols centered around a narrowly focused area
(federal prosecution), the need to invest in broader data systems or invest in building
competencies in broad-based, collaborative planning can be eliminated. Instead,
resources can be focused and work done by a group that already shared information,
language, and goals. This reduces the risk of adoption, but also limits the potential
beneﬁts as the problem and the solutions are deﬁned without beneﬁt of a wider group or
research on possible alternatives.

The PSN Task Force has not bought into the “strategic planning” aspect of

PSN. My sense is that they believe prosecuting a handful of cases each

month at the federal level is in fact accomplishing the ultimate goals.

(Survey response from a recognition-based district)

Another way of conceptualizing the beneﬁts of a recognition-based group that
focuses on expediting federal prosecution is to consider this a form of competency.
While horizontal and vertical competencies are generally envisioned as elements of
strategic problem solving, recognition-based groups could be thought of as non-strategic,
but able and low-cost planning groups. The investigation and mapping of extremes
indicated that districts which engaged in this form of decision-making averaged in the
highest quartile of per capita federal gun prosecutions in the year prior to PSN initiation.
While this variable was framed in this study to represent need, it also can be
conceptualized as a measure of practical experience with information and people to
develop and execute protocols designed speciﬁcally to meet the goal of enhanced federal

prosecution. With these competencies already developed, even a modest infusion of

funding could make an expansion of such processes very inexpensive.

172

Density and Decisions

While population density did not rise to signiﬁcance in the regression analysis of
policy adoption, the investigation and mapping of extremes still suggest that density may
be related to policy adoption, though not in the manner and measures offered in this
study. Narrative responses from very low population districts indicate the difﬁculties and
costs associated with any planning effort across sparsely population areas.

An unexpected ﬁnding in the subgroup analysis for high and low information
technology districts was that population density was negatively related to success.
Further investigation led to the discovery of a possible tipping point in density. For
districts in the mid-range of population, with a large city but an overall population
density less than 1,000 residents per square mile, policy adoption results were not related
to density. The signiﬁcant difference in adoption appears to occur at the extremes. Very
low density districts were generally unsuccessful adopters, as would be predicted by the
model. Exceedingly rural districts, according to the responses by the research partners,
often had difﬁculty identifying a target for the program. Further research into the agency
and task force levels of the model will explore the relationship between low population
density and policy structure. Narratives from the research partners and reviews of the
existing information would suggest that rural district task forces were more likely to
develop a district-wide target, rather than a target aimed at a speciﬁed locale. This
diffuse target may confound efforts to provide speciﬁc, viable, and measurable programs.
Conversely, the districts with large cities and very high overall population density were
generally not in the high adopter category as was predicted. Looking back to the research

on policy adoption of pre-release centers in larger and smaller correctional systems, the

 

effect of size differed when success was differently deﬁned. Larger entities were more
likely to adopt a center, but were not Signiﬁcantly associated with implementing a larger
number of beds per capita (McGarrell & Duffee, 1995). This suggests that entities that 7
must deal with a large number of people, goals, and competing programs may have the
resources to absorb and the need to adopt policy at some level. In contrast to very low- I
density districts that suffer from no economies of scale, it is possible high population
districts, like large agencies, may have such a range of needs, competing goals and
programs, and complex human relations that full adoption in that context may be too
complex and too expensive. Further analysis of the factors inﬂuencing adoption at the
agency and task force level will be needed to explore to what extent very high density
districts or very low density districts have organizational, administrative, geographic, or

political issues that limit the ability of a task force to be highly successful.

Human Factors and Political Forces
Legal barriers were eventually overcome, but the perceived risk to
agencies was a huge issue... We were going to match the public list of elk
and deer (riﬂe) licenses to a list of current felony probation and parolees.
The [state] Department of Corrections gave us permission, as did the

Game and Fish Department... but the governor heard about it and jumped

on the Fish and Game ofﬁcials.
(Survey response, low adoption district)

While human factors and attitudes proved poor predictors of policy adoption
success, responses ﬁom the surveys raise the possibility that public attitudes and political
positions may play a role in policy adoption. The response from the research partner
quoted above indicated that public attitudes in this low adoption state were not supportive

of federal government programs that were perceived as infringing on the rights of citizens

174

to bear arms, even if those citizens are felons on parole. One of the limitations of this
broad a study of human factors is that it becomes difﬁcult to discover how policies, such
as PSN, are framed in the public and the political decision-makers’ minds. Is PSN a
program that protects good people ﬁom bad and perhaps racially threatening groups by
federally prosecuting them and incapacitating them in federal prison for long periods? Is
PSN a federal incentive to get states to take guns away citizens or make buying or selling
those guns more difficult? It is hoped that further studies, informed by this research, will
allow in-depth analysis of districts. That analysis can better deﬁne the manner in which
the policy is ﬁamed and perceived and seek out measures that capture the nuances of the

push and pull of competing human forces on the adoption of this policy.

My bunch (and it is really nothing more than a hunch) is that of lot of this

[adoption failure] has to do with local politics, a Republican US. Attorney

in a highly Democratic city.

(Survey response from a low adoption district)

Subgroup analysis for districts with a good information infrastructure lent support
to the idea that the political context of the district could inﬂuence the level of policy
adoption. Another limitation of this broad analysis, and an analysis that does not place
the researcher in the policy context, is that politics and the potential political capital to be
gained or lost is difficult to measure without a deeper understanding of the political
players. The measure used in this study was very limited, as it looked only at the
political party of the governor and the mayor. Local politics is much more complex and a
true measure would require understanding where political strength is truly exercised. In

one jurisdiction, county leaders may be peripheral, in others county ofﬁcials may be more

powerﬁrl that a mayor serving in a city manager form of government. Tenure in the

175

 

position, the strength of alliance with a political party, as well as local political feuds and

tensions can all play into the context of political beneﬁt or risk.

Resources and Cost-Shifting

Organization theory would predict that if organizations can adapt to the
circumstances in the external environment to assure a positive resource ﬂow, the better
the survival chances of the entity. It was theorized that the amount of resources ﬂowing
into the district as an incentive would inﬂuence policy adoption. Ifthe premise stated
earlier is true that the inﬁastructure factors serve as a primer or barrier to implementation,
then, once this investment is paid for, the level of resources should positively inﬂuence
adoption. However, this analysis provides an indication that it is not just the amount of
money infused into the district that may enhance the adoption of this strategy, but the
manner in which that money and that policy reduce a current risk. Districts in which the
ﬁrnding for the correctional systems was low were more likely to ad0pt PSN than districts
in which such ﬁrnding was high. While other interpretations of the inﬂuence of this
variable are possible, the model proposes that low correctional ﬁrnding indicates a
survival risk and provides an incentive to adopt PSN to lower risk. The possibility of
increased federal prosecution translates into an increased possibility that serious
offenders will be charged, convicted, and incarcerated in the federal system. Any shiﬁing
of offenders from state and local jurisdictions to the federal system means a state and
local cost saving on court and correctional costs. For serious offenders this can translate
into signiﬁcant savings. Aside ﬁom simple ﬁnancial calculations, low investment in

criminal justice systems can result in negative survival consequences for elected ofﬁcials

176

in under-funded jurisdictions, including increased liabilities associated with under-staffed

police departments, courts, and correctional institutions.

The Need for PSN

While perhaps the factor that one would most intuitively associate with policy
adoption of a gun crime control program, the level of violent crime and the per capita
federal prosecution of gun offenders, did not signiﬁcantly contribute to the model of
policy adoption tested with linear regression. As with the ﬁscal incentives, however, for
districts with a good informational infrastructure, the level of need, measured as per
capita reported violent crime, did contribute signiﬁcantly to the model. This suggest that
risk is related to the potential beneﬁt that is promised by policy adoption, but that the
costs of creating the infrastructure capable of producing such beneﬁts may be a
substantial enough cost barrier to prohibit adoption even when there is a need.

As discussed earlier, the classiﬁcation of the federal cases brought per capita,
used in this study as a measure of need, may reﬂect something more. Aggressive or
limited prosecution may be bounded by crime rates, but also may reﬂect the competency
and history of the federal attorneys to engage local and state prosecutors and law
enforcement in processes leading to federal charges. In some sense, this variable may be
related to the prior program experience variables, measuring implementation of Weed
and Seed or SACSI programs. However, in this case, the experience is with a narrowly-

focused group on a narrowly-deﬁned mission.

177

Policy Implications

The ﬁndings of this study indicate the need for policy makers to seriously
consider the level of vertical and horizontal competencies present in jurisdictions prior to
channeling ﬁrnding for policy adoption that requires broad-based collaborative groups.
Using only the external level of the model, the results indicate that the best return on
investment can be expected if districts have an information inﬁastructure and a history
that supports collaborative problem solving. For districts lacking these competencies, a
better long-terrn return on investment could be expected if grant monies were directed as
ameliorating information systems and interoperability issues, and if programs were
funded to provide technical assistance in the areas of data sharing and strategic planning.
Narratives from the research partner surveys indicate that task forces in districts that
failed in all but symbolic adoption did not know how to begin a cross-ﬁrnction planning
process and would have proﬁted by training in the basics of cross-agency collaboration.

While it seems intuitive to areas in which the need for the policy appears, by some
objective measure, to be most needed, the ﬁndings in this study indicate that that premise
may be most predictive of success with simpler policy processes. The level of violent
crime in the district did inﬂuence high adoption of enhanced federal prosecution, but
failed to contribute signiﬁcantly to collaboration, research, or overall success with PSN.
For framers of policy, the current trend toward multi-agency, multi-level policy adoption
may require rethinking conditions that prime adoption. This study raises the possibility
that for these more complicated criminal justice policy initiatives, policy makers should
be aware of the level of competency in the district as well as the level of need. If a

single-agency solution is proposed, those competencies may be less important.

178

 

Perhaps just as importantly, policy makers need to understand how jurisdictions
limit the risk of policy adoption. Districts with poor competencies and, therefore,
requiring large infusions of resources to create the structures and infrastructures required
to support the adoption, may well resort to symbolic adoption or to a narrowly focused
strategy that controls their costs. Some might argue that such a narrowly focused strategy
may be a good policy choice. If infrastructure competencies do not exist, decision
makers would be well advised to consciously make a choice to either invest in needed
infrastructure components and defer adoption, or choose to support a simpler policy that
involves less need for collaboration and strategic planning and does not require either the
federal government or the state or local entities to bear the costs of enhancing the

inﬁastructure.

Questions Related to Other Levels of the Model

Task force turnover has been an issue. . . no strategy has been agreed on.

(Survey response from a low adoption district)

Further research into the ﬁrnctioning of the model at the agency and the task force
level may be more successﬁll at building an understanding of a host of factors left
unexplored at this level of the model. As the researcher quoted above noted, the work of
the task force in his district was limited, not by the factors conceptualized and measured
for this study, but factors related to the stability of task force membership. In addition to
the factors that are endemic to member agencies or the task force itself, the role of human
factors may become more clear when put in the context of the risks perceptions of those
playing speciﬁc roles. For example, local attitudes on gun control may be of more

concern to the elected prosecutor of the largest county in the district but create less

179

political risk for the federal prosecutor. In this study, whether related to problems with
translating concepts into measures, or with the concepts themselves, attitudinal factors
were not strongly associated with policy adoption or failure. The inclusion of risk
perceptions by agency decision-makers may begin to unravel possible connections
between public attitudes and policy adoption success.

Perhaps moSt intriguing are the questions related to the patterns of adoption that
were found by sorting out extreme cases and reﬂecting strategies groups employ to
reduce risk. Good candidates for case studies investigating the other levels of the model
could be drawn from the four adoption patterns illustrated in Figure 19. These extreme
cases could provide interesting laboratories to tease out the inﬂuences of agency andtask
force level factors. Social network analysis to determine the direction and density of
interactions could be most useful in better deﬁning these patterns of risk-decision making
groups and provide insight into the strategies employed by groups to limit the cost of

information sharing and the costs of policy adoption.

Limitations

This study, limited by the full coverage design, the relatively small population
under study, and the exploration of only the external factors on the model provides some
insights into the question of what factors support or inhibit policy adoption success.
While the measures of success were given careﬁrl thought and required a good deal of
industry to create, success is still a difﬁcult concept to ﬁrlly deﬁne and defend in the
context of collaborative, multi-goal policies. Ofﬁcial reports and survey responses

cannot be assumed to reﬂect an unbiased and unblemished view of reality.

180

The Quinn Heuristic

Using the concepts of sectors and the visual displays of competing factors
developed by Quinn (1991) has provided beneﬁts for this study and may provide
additional beneﬁts for research and practice. The model provides a useﬁrl discipline to
group variables in ways that allow policy adoption research to build evidence based on
the inﬂuence of sectors, while allowing variables to be tailored to the speciﬁc factors
inﬂuencing perceptions of risk related to that policy.

For translation to policy makers and practitioners, the maps provide illustrations,
that though simpliﬁed, of information that otherwise might be communicated in forms

that would be less accessible for those not familiar with or schooled in statistical analysis.

In Conclusion

Despite the limitations of the study, this attempt to ferret out conditions under
which policy adoption of the type of broad-based federally incentivized policies begins to
provide some clues to the external factors that enhance adoption. The answers resulting
from this study have implications for policy and could aid decision-makers determining
whether districts are primed for adoption or would be better served by targeting funds and
assistance to programs to build needed competencies. This knowledge would be helpﬁrl
in both crafting policy and funding strategies. From the standpoint of a research agenda,
the questions raised by the study are as, or more, important that those that are answered.
This study raises research questions that can and will be answered by expanding research
into the agency and task force levels of the model to continue to hone our understanding
of the competing factors at all levels of decision-making that lead to policy failure or

policy success.

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APPENDICES

182

 

APPENDIX A: Research Partner Survey Instrument

Project Safe Neighborhoods
National Research Team
At Michigan State University
Survey for the Research Partner
Revised Version (09-15-05)

Instructions:

Thank you for taking the time to ﬁll out this survey.

This document is a form that allows you to respond by clicking on the boxes or typing in
responses. Please enter the requested information in the form given by feel free to add comments

to help clarify your responses.

Research Partner Information

Name: Date Survey Completed: /2005
Phone:

District Name:

 

Research Partner Survey

1. To what extent has the strategic problem solving process been integrated into the PSN
task force?
For each section, please indicate the one statement that is closest to your perception of the
manner in which the PSN task force is using information to drive decisions. Please add
comments to better explain your answers. '

Part A. Using data to identify problems.
El The task force is interested in looking at data to identify problems and solve
problems.
1:] The task force is interested in data and analysis for areas they have already identiﬁed
as problems.
1:] The task force is interested in data to conﬁrm the value of current practices.
E] The task force is not interested in using data and analysis to drive planning.
Part B. Translating data into decisions.
[:1 The task force has used research and data to create programs or strategies.
[:1 The task force has used research and data to expand existing programs or strategies.
[:1 The task force has used research and data to justify or publicize existing programs.
D The task force has not connected research and data to programs or strategies.
Part C. Evaluating results
[3 The task force has already received an evaluation report(s) on PSN efforts.
[:1 Evaluations of PSN programs are underway but not yet complete.
[:1 Evaluations of PSN programs are in the planning or ramp-up mode.
[:1 Evaluations of PSN programs have not gotten beyond the discussion phase.
CIThe task force has not requested or supported the development of evaluations.

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Comments:

2. To what extent are you (and other research team members) integrated into the task
force?

Please indicate the'statement that best describes your perception of your relationship with the task

force and then provide any comments that will help explain your answer (choose one).

[:1 I/we function as a member of the task force, participating openly and regularly with task force

members.

1:] I/we function as a resource person, providing information routinely but not actively

participating in all phases of discussion and planning.

[:1 Research is peripheral to the task force proceSs.

Comments:

3. Was the research team able to conduct analyses of the local gun crime problem?
CI yes
[:1 no
D don’t know
a. Ifyes, did the task force use the ﬁndings to shape gun crime reduction strategies?
yes
1:] no

1:] don’t know

Cements:

4. In what areas has research provided a tangible result?
Check all that apply and give an example for those areas with tangible results.

Check all that apply Give an example
1:] Problem identiﬁcation

[:1 Program development/expansion
[:1 Program evaluation

[:1 Program revision/modiﬁcation

[:1 Resource allocations/Shifts

5. Do you think that task force members will be more likely to use research and research
partners after their PSN experience?
Cl yes
1:] no

[:1 don’t know

Comments:

184

 

 

6. In your view, the overall impact of PSN in terms of the use of problem-solving processes is
best described by which of the following statements (choose one).Please comment to

clarify your opinion.
[:1 PSN has created an environment in which data analysis drives decision making.

[:1 PSN has increased the use of research in decision-making.

[:I PSN has increased the use of research to evaluate existing strategies, but not to drive all

decisions.

[:1 PSN has increased the ability of task force members to collect data, but analysis and
evaluation processes are not integrated into decision-making.
[:I PSN has not changed the way in which decisions are made.

Comments:

7. Please rate each of these areas to the extent they created barriers to research-driven

problem solving. Please select one of the three responses.
a.

b.

C.

Legal barriers

problem

Administrative barriers

problem

Perceived risk to agency

problem

Turf issues

problem

Information is not collected
problem

Information in incompatible form
problem

Lack of technology/technical support
problem

Lack of interest in research
problem

Lack of action by members“
problem

Lack of funds

problem

Lack of personnel

problem
Administrative/organizational issues
problem

[:1 No problem
1:] No problem
D No problem
[:1 No problem
[:1 No problem
[:1 No problem
1:] No problem
El No problem
[:lNo problem
[:1 No problem
[:I No problem

[:1 No problem

Feel free to add comments.
El Some problem 1:] Major

El Some problem [:1 Major
El Some problem D Major
1:] Some problem 1:] Major
[:1 Some problem [:1 Major
C] Some problem [:1 Major
E] Some problem [:1 Major
1:] Some problem [:1 Major
1:] Some problem [:1 Major
1:] Some problem [:1 Major
[I Some problem 1:] Major

El Some problem 1:] Major

* members failing to follow through with data collection. working on data issues in
their agencies, etc.

Comments:

185

8. Has the PSN task force provided any additional funding for research beyond the initial
BJA grant?

[:1 no
D don’t know

El .ves

Comments:

9. From your perspective, how supportive and engaged has the US. Attorney been in PSN
(choose one)?

[I The US. Attorney has been both supportive and engaged in PSN.
D The US. Attorney has been supportive of PSN.
E] The US. Attorney does not appear to be supportive or engaged in PSN.
10. Comments:
11. How su portive has the PSN project coordinator been of your research efforts?
[:5 very supportive D somewhat supportive [I not
supportive
Comments:
12. Overall, has the PSN research experience been ositive or negative?
D positive E] somewhat positive If] somewhat negative [:I negative
Comments:
13. Please provide any additional comments about your experience in PSN that you would

like to share.

Comments:

186

 

 

APPENDIX B: PROJECT SAFE NEIGHBORHOODS SEMI-ANNU AL REPORT
TO THE ATTORNEY GENERAL
(07-2005)

The following questions were used in the construction of measures for this study.

15. Has data analysis helped the task force focus on gun violence?
[:1 Very much

[:1 Somewhat

E] Not really

17.31. Please identify enforcement/deterrence focused strategies your task force has
implemented:

Increased federal prosecution of ﬁrearms-related cases

Increased state and local prosecution of ﬁrearms-related cases

Deployment of street-level ﬁrearms enforcement unit

Offender-notiﬁcation meetings

Probation/parole enforcement home visits

Directed police patrol in high crime areas

Supply-side interventions

Investigations of criminal organizational/gang violence

DDDDDDDD

Please identify prevention strategies focused strategies your task force has
implemented.

Clergy outreach to offenders
Employment programs

Substance abuse programs

Education programs

Vocational training programs
Neighborhood development programs
Youth street worker outreach
School-based prevention

Hospital trauma center outreach
Other

...:
>1
g

DDUDDDDDDD

187

 

 

APPENDIX C: DIAGNOSTICS

This appendix section contains the tests used to both compare permutations of the
model and the regression diagnostics on the model that was employed in the study. In the
regression analysis including all 24 state, dummy variables, only two state variables rose
to signiﬁcance, being in California was associated with lower adoption (p<.01) and being
a district within North Carolina was associated with higher adoption levels (p<.05).

Comparing regression analysis between the simple model and the model adding the two

state variables, the variables that rose to signiﬁcance did not differ (Table C1).

Bivarate correlations indicate that being a California district is signiﬁcantly

associated with higher

allocations and stricter gun

laws. Being a North Simple State
Carolina diStI'iCt was nOt Variable Beta Sig. Beta Sig.
Needs and Products Sector
signiﬁcantly associated with Crime 0.15 0.15
. Cases -0.01 -0.05
any 0f the independent Structure and Infrastructure Sector
, Information Technology 0.33 ** 0.31 **
variables. As the full model Weed and Seed 0.45 H 0.37 H
is tested, ﬁirther clues into SACSI 0'32 H 0'28 H
Densrty -0. 05 -O. 14
the inﬂuence of these state- Human Factors Sector
Gun Law 0.06 0.23
level factors will be Racial Threat -0.06 -O.14
. Punitiveness -0.21 -O. l 3
explored. For ”“5 study, the Resources and Survival Sector
, , Correctional Resources -O.26 * -O.24 *
Simple model Will be Allocation -0 10 O 05
utilized. Politics 0.02 0.05
State Variables
Califomia -0.34 **
North Carolina 0.29 *

Table C1. Comparison of Standardized Estimated

(Beta) for the Simple Model and the Model
Including Two State Dummy Variables

 

188

 

um

 

 

8. a 58588 ..
88. a Became ..

 

189

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9.8 88 88 88 .. ~88 _ 88 _ 888 625.6 .88
88 88- 88 88 88 8 88 8 888 .50 89.58 88888
8 8 8 8 8 8 8 8 z
88 $8 .. 88 88 888 28 .. 888 .. S8 Gums-3 .88
S 8 88 $8 888 2 8 2 8 8.8. 88 e8 8858 88822
8 8 8 8 8 8 8 z
.. 888 888 88 88 88 88 .. 88 8585.3 .85
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88 8 8 8 8 8 z
888 .. 88 $8 88 88 .... 888 388-8 .88
a: 8- 88 888- 88- 88 88 .50 8858 88822888
8 88 8 8 8 z
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190

Table C4. Chi Square Analysis between Dummy Independent Variables

DF Value Prob.

 

 

 

 

Chi-Square 1 1.23 0.37
Frequency Weed and Seed
Percent O 1 Total
SACSI O 20 57 77
22.99 65.52 88.51
1 1 9 10
1.15 10.34 11.49
Total 21 66 87
24.14 75.86 100.00

 

 

 

 

 

Missing = 3

Table C5. Regression Diagnostics: Tolerance and Variance Inﬂation
Factor Statistics for the Restricted Model

 

 

Variable TOL VIF

Crime 0.71 1.41
Cases 0.71 1.40
Information Technology 0.92 1.09
Weed and Seed 0.74 1.35
SACSI 0.86 1.16
Density 0.66 1 .52
Gun Law 0.66 1.52
Racial Threat 0.74 1.36
Punitiveness 0. 56 l .80
Correctional Resources 0.60 1.65
Allocation 0.57 1.77
Politics 0.83 l .20

191

 

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