Redundancy and Performance in Public Service Delivery Networks

Sean Nicholson‐Crotty Department of Political Science and Harry S Truman School of Public Affairs University of Missouri

Jill Nicholson‐Crotty Harry S Truman School of Public Affairs University of Missouri

Robert Shireman Department of Political Science University of Missouri

Scholars studying policy implementation in traditional hierarchical organizations have long been interested in the impact of redundancy on program performance. Specifically, they have asked if a duplication of effort among agencies performing similar or competitive functions is justified by an improvement in the quality of programmatic outcomes. Early work in this area suggested that redundancy increases program effectiveness by reducing the errors and omissions that might occur in single agency production. More recent work has been less sanguine, suggesting that redundancy may actually decrease performance as all agencies gravitate toward the standards of the least effective, or that the increased costs of redundancy are only justified by improved performance under a relatively limited set of circumstances.

During the same period that scholars have been debating the impact of redundancy in traditional policy implementation, we have learned that fewer and fewer programs are actually implemented within such structures. Research now suggests that many (most?) public programs and services are delivered through collaborative or networked arrangements between government, private, and third-sector organizations, rather than through singular (or redundant) hierarchical government agencies. Thanks to a large and growing body of research, we have also learned a great deal about the impact of these network structures on the performance of public organizations and programs, as well as the factors that condition that impact.

Interestingly, however, the impact of redundancy in network implementation has yet to be explored.1 This oversight is particularly surprising because redundancy is at least as likely (if not more likely) to occur in nonhierarchical and decentralized network settings, relative to a more traditional intra-governmental implementation scenario. It is also surprising because of the

1 Redundancy as we describe it is different than the concept of network redundancy described in the private sector management literature. That concept describes the degree to which an organization is a part of two or more networks with overlapping or redundant members or purposes (see Echols and Tsai 2005) and not the degree to which two organizations within a network are providing the same services to clients.

1 potential consequences of redundancy for network performance. As in traditional settings, duplication of effort in networks inevitably produces increased costs that can only be justified by a commensurate or super-commensurate increase in output quality. There are reasons to believe, however, that such performance gains are likely to occur only under certain conditions.

This manuscript explores the impact of redundancy on organizational performance in network settings and, more specifically, the network, organizational, and individual characteristics that moderate that impact. We test assertions regarding the relationship between redundancy and network performance in analyses of reentry/recidivism-prevention services delivered to clients by organizations in 15 community corrections networks around the state of

Missouri in 2009/2010.

Networks and Effectiveness

Over the past several decades, public management scholars have become increasingly focused on the implementation of public programs via interorganizational networks (see for example Agranoff and McGuire 1998; O’Toole 1997). An important dimension of this work has focused on the creation, structuring, size, and management of, as well as the interactions within, these networks (see for example Agranoff 1991; Graddy and Chen 2006; Silva and McGuire

2010; Alter and Hage 1993; Sydow 2004; Teisman and Klijn 2002). Another body of work has focused on the impact that networks have, relative to one another or traditional hierarchical arrangements, on the organizations within them and the programs they implement (see for example O’Toole and Meier 1999; Provan and Milward 1995). Because we are ultimately interested in questions of effectiveness, we will focus primarily on this latter body of work.

The predominant assertion in this literature has been that collaboration is generally better than isolated action and that coordination among service providers increases effectiveness through the

2 dispersion of information and expertise, the attainment of economies of scale in resource use, and the ―catching‖ of clients who might be missed or fail to take advantage of programs offered by a single provider (see for example Alter and Hague 1993; Provan and Milward 2001). The majority of work in this area has focused on organizations, or nodes, within networks when assessing performance. This work has suggested that, when holding inputs and other characteristics (including internal management activities) constant, interaction with more nodes in the network increases the performance of public organizations (see for example Meier and O’Toole 2001; Nicholson-Crotty and

O’Toole 2004; Rethemeyer and Hatmaker 2007). More recent work, in public management, has posited a nonlinear relationship between network activity and effectiveness, suggesting that staff resources and managerial quality moderate the impact of networking on organizational performance

(Hicklin et al. 2008).

Scholars have also suggested that the character of interaction between nodes, rather than simply the volume, determines the impact of networking on the outcomes for individual organizations. The private-sector management literature has long recognized for some time that ―embeddedness‖ within a network also correlates with firm performance (see Borgatti and Foster 2003; Moran 2005).

Embeddedness differs from network activity, or ―centrality,‖ in that it is the strength of connections with a subgroup of organizations in one part of the network, rather than the total number of connections that is assumed to determine outcomes for an individual node (Wasserman and Faust 1994). Strong ties and consistent interactions among a small group of organizations aids in the development of trust and reputation, which are ultimately attributes that quality organizations can leverage to receive more resources from the network. The public management literature has also explored the impact of embeddedness on organizational performance within networks. Provan and Sebastian (1998) identified the performance advantages of small group membership, similar to embeddedness, in the mental health networks the studied. Schalk et al. (2010) find higher levels of satisfaction among

3 the graduates of colleges that have strong ties to cohesive subgroups of other organizations within the network of Dutch teacher training colleges.

A smaller body of work has examined the performance of networks (or the service delivery nexus) as the unit of analysis (Knoke et al. 1996; Provan, Fish, and Sydow 2007). Provan and

Milward (1995) find that integration and centralization among network nodes, external control of network activities, and the stability of network partners all influence the perceived effectiveness of mental health networks among clients. Provan and Kenis (2008) also argue that network governance matters, but suggest that its impact on effectiveness is moderated by the fit between structure and other network characteristics. Specifically, they suggest that more decentralized forms—such as shared governance among nodes—is most effective in networks marked by low membership, high trust, and relatively little need for network level competencies. Alternatively, more centralized arrangements—with management by an external network authority at the far end of that continuum—is best suited to larger networks, where nodes have lower levels of experience/trust, and which must perform tasks that require higher levels of inter-node coordination.

Redundancy and Effectiveness

Scholars of implementation first addressed the issue of redundancy more than 50 years ago. The interest in the subject was driven by the recognition that the duplication of effort among agencies existed in myriad corners of the federal government and, more importantly perhaps, that such duplication was frequently encouraged through formalized interagency competition and review procedures. Downs’ (1967) argued that such competition was likely to produce increased organizational effectiveness when 1) both agencies were funded by the same budgetary authority and 2) when the agencies were sufficiently distant organizationally to ensure that they had distinct loyalties and did not temper their behavior for fear of retribution. Landau (1969)

4 similarly suggested that intentionally overlapping services could increase the performance of the duplicate agencies, primarily due to the increased reliability and quality that arises from having one agency check the outputs of another. Others suggested that redundancies might also increase efficiency and lower costs by reducing the level of errors or mistakes (Landau 1979).

Initial accounts of potential benefits of redundancy for reliability have been empirically tested and theoretically extended. Bendor (1985) formalized the relationship between redundancy and reliability and provided empirical tests in the area of metropolitan transportation, which largely confirmed the existence of a positive relationship. Others demonstrated empirical support for the benefits of redundant systems in air traffic control and naval operations (LaPorte and Consolini 1991; Rochlin, LaPorte, and Roberts 1987). Heinman (1993) extended the concept of redundancy by considering the different types of error that public organizations might try to avoid through redundant systems. Through an examination of the Challenger disaster, his work demonstrates that redundant systems are better at preventing ―type II‖ administrative errors, where an agency fails to take an action that is necessary, but are actually more likely to create

―type I‖ errors, where an agency chooses to act when it should not. Heinmann (1995) demonstrates that redundancy is a superior choice for increasing reliability when agencies must make nonprogrammatic decision, where variability and uncertainty are high. Alternatively, he suggests that serial strategies are preferable when decisions are repetitive and routinized.

Others have also argued that redundancy only increases effectiveness under certain conditions. Krause and Douglas (2003) suggest that gravitation toward performance of the least effective organization eliminates the advantages of redundancy in many parallel systems.

Drawing on the theoretical perspective of Adverse Reputational Herding, they argue that, ―when a parallel (redundant) agency performs at the same or lower level compared to an initial agency

5 did before the former bureau’s creation, the initial bureau will play to the level of its competition by either exhibiting no change or a reduction in task performance quality‖ (Krause and Douglas,

2003). Ting (2003) also illuminates a number of conditions under which redundancy may not increase effectiveness. In a game theoretical approach which allows for strategic action by agents, he demonstrates that that redundancy can help the principal achieve a policy goal, which is assumed to be effectiveness, when the that principal and the agents have divergent preferences.

The positive impacts of redundancy decrease markedly, however, when the principal and at least one of the agents share preferences regarding outcomes. Redundancy may also not be unnecessary if the principal can take away a single agent’s right to produce a good in the event of poor performance.

Redundancy and Network Effectiveness

The literatures on network effectiveness and redundancy both offer conditional expectations about the relationship between these concepts and effectiveness. In other words, both suggest a potential positive impact on organizational performance under the right conditions. Interestingly, however, there has been relatively little work attempting to integrate these approaches to program effectiveness. Landau (1991) did envision the possibility for redundancy in ―multiorganizational systems‖ 20 years ago, extolling the potential benefits of organizing systems of ―loosely coupled‖ horizontally linked organizations with duplicate jurisdictions and functions. He suggested that this type of system would be better at recognizing and correcting errors in the provision of public services, making the system better than any of the component parts. Landau’s (1991) account is suggestive and intriguing, but, to date, no one has built upon it to offer a more systematic examination of the impact redundancy in network implementation settings.

Doing so requires that we 1) define network redundancy and 2) attempt to reconcile the disparate literatures discussed above to understand conditions under which such redundancy should improve network effectiveness. We define network redundancy narrowly as a condition where a client receives a similar good or service from more than one organization within a network of nonhierarchically linked providers within six months, when they could be receiving that service from a single provider. There may be performance effects from simply having two or more organizations offering the same services to different clientele within the network, but restricting the definition to redundant services delivered to the same client better matches the concept of parallel processing used to define redundancy in the more traditional implementation literature. As a final definitional note, we are interested primarily in redundancy in consequential

(Graddy and Ferris 2006) or goal-oriented (Kilduff and Tsai 2003) networks, where organizations purposefully come together and coordinate some degree of their action in order to produce a particular outcome or accomplish a particular goal. Redundancies likely exist in what have been described as ad-hoc or ―serendipitous‖ networks (see Provan and Kenis 2008), but, because these often are not organized to produce a specific outcome it may be more difficult to identify a metric against which ―effectiveness‖ can be measured.

With network redundancy defined, we move now to integrating the disparate literatures on these topics. In order to do so, we take what we believe is a parsimonious and intuitive, if not particularly sophisticated, approach. If the expectations of the network and redundancy literatures regarding the factors that condition their impact on performance move in the same direction, we expect redundancy to have a positive impact in a network setting. Alternatively, if they move in opposite directions, we expect a null impact. In other words, if a characteristic increases the likelihood that redundancy will improve effectiveness and increases the likelihood

7 that a network arrangement increases performance, then we expect redundancy within a network to have a positive impact when that characteristic is present. Alternatively, if a characteristic increases the positive impact of networks, but decreases the advantage of parallel systems, then we expect those things to cancel one another out, producing a null impact for network redundancy. In those cases where either literature is conflicted on the impact of a particular variable on effectiveness, we do not offer an expectation about the direction or significant of the relationship network redundancy and effectiveness under those conditions. By using the individual receiving services from organizations in the network as the foundation for our definition of redundancy, it allows us to specify moderating characteristics at the individual, organizational, and network levels.

We begin with propositions regarding the moderating impact of network structure and activity on redundancy. The literature on networks suggests that those organized around a central node or authority tend to be more effective (Provan and Milward 1995), particularly when goal consensus and the need for coordination among nodes is relatively high (Provan and Kenis

2008). The benefits of these structures accrue because they result in greater integration and coordination of network activities (Graddy 2008). In a similar vein, the network literature consistently suggests that ―centrality,‖ or the frequency of contacts with other nodes, is an important predictor of success for individual organizations within a network (Nicholson-Crotty and O’Toole 2004; O’Toole and Meier 2004; Schlak et al. 2010). Centrality (as opposed to integration) increases performance by increasing the organization’s access to resources and expertise and improving its ability buffer key operations from environmental shocks.

The literature on redundancy in traditional implementation scenarios also addressed the issues of integration and communication among organizations, and it has evolved regarding the

8 expected impact of these factors. Downs (1967) suggested that coordination among units eliminated the benefits of redundancy. Alternatively, Bendor (1985) demonstrated formally that integration does not necessarily reduce the positive effects of parallel processing. Finally,

Landau (1991) provides some case evidence that coordination among ―loosely coupled,‖ redundant service providers is what allows that type of system to better identify and solve problems relative to hierarchical or serial production systems. Because it seems as though the expectations in the literatures on redundancy and networks move in the same direction, we offer the following propositions related to redundancy and network effectiveness:

Proposition 1: All else being equal, the impact of redundancy on network effectiveness will be more evident when a centralized authority works to integrate network activities.

Proposition 2: Redundancy will have a larger impact on effectiveness when an organization has a higher level of centrality or communication with other nodes in the network.

There are also places where the literatures on redundancy and networks overlap in terms of their expectations regarding effectiveness of organizations within networks. The first of these that we treat deals with the issues of organizational capacity and/or quality. The literature on network effectiveness originally argued that contact between network nodes was sufficient to increase performance relative to hierarchical implementation (See O’Toole and Meier 1999).

Recent work suggests that the benefits of network activity may not accrue equally to all organizations, and demonstrates that the staff resources of an organization and the quality of its manager moderate the relationship between network activity and performance (Hicklin et al.

2008).

The literature on redundancy also emphasizes the importance of high quality/capacity organizations. Indeed, work in this area consistently suggests that increasing the capabilities of

9 individual organizations is necessary to reduce error and improve reliability (see Landau 1969;

Heinman 1993). It simply argues that this approach is not sufficient to guarantee reliable outputs and that overlap and duplication are, therefore, warranted. Interestingly, even work challenging the desirability of redundancy indicates the need for high quality organizations. Krause and

Douglass (2003) argue that performance in redundant systems sometimes regresses to the level of the lowest performing organization. This suggests that such system will be most effective when both organizations have higher individual performance levels. Combining these arguments from the literatures on redundant systems and networks, which again seem to move in the same direction, we expect that:

Proposition 3: Redundancy will have a larger impact on effectiveness when the organizations providing redundant services have higher capacity or quality.

Finally, we believe that similar predictions in the literatures on networks and on redundancy suggest at least one individual level factor that may influence the impact of redundancy within networks. The work on networked implementation suggests that it is preferable to hierarchical arrangements in a variety of settings (Agranoff and McGuire 2008).

This seems to be particularly true, however, in the area of human service delivery, not only because the incentives of providers are more properly aligned in such settings (Provan and

Milward 2001), but also because these are ―wicked‖ problems that require a multifaceted approach (O’Toole 1997) and because they often serve the most ―vulnerable‖ clients (Aday

2001). This latter point reappears regularly in the literature with assertions that network implementation is better at catching those difficult clients who are most likely to fall through the cracks in traditional settings (Alter and Hague 1993; Provan and Milward 2001).

Interestingly, though it was never explicitly been interested in individual client outcomes, the literature on redundancy makes a similar argument regarding difficult problems. Initial work suggested that redundant systems were preferable for most problems faced by government

(Downs 1967). Subsequent work demonstrated formally and empirically, however, that redundancy was best suited to deal certain types of problems and less able to provide effectiveness gains in others. Heinman (1993) argues that Type II errors, where government failed to act when action was necessary, are particularly amenable to correction by parallel systems. This becomes more true the greater the necessity of action. So, combining the findings regarding problem type from the literatures on networks and redundancy, we expect that:

Proposition 4:Redundancy will have a larger impact on effectiveness for the most difficult clients.

Empirical Test

We test hypotheses drawn from these general propositions in data from community corrections networks in Missouri. Missouri maintains a relatively large incarcerated population relative to its population (Pew Trust 2008) and in 2009, there were more than 100,000 persons on parole or probation within the state (MODOC 2010). The challenge of reintegrating these persons into society is well documented. Within 3 years of their discharge from the system, approximately 67% of convicted felons will be rearrested. Forty-four percent will come in contact with the justice system within 1 year of release. Of course, these rates vary dramatically by offense severity and criminal history. A study by the U.S. Sentencing Commission found that, among those convicted in federal court, the most serious offenders had a 1-year rearrest rate of

55% , while the lowest risk individuals reoffended at approximately 14% (CRS 2008). Despite

11 this variation, few dispute that offender reentry is a significant problem, particularly for governments with punitive justice regimes.

In response to the scale and scope of the reentry problem, the state of Missouri has taken a progressive approach to reintegration. It was among the first 8 states chosen by the U.S.

Department of Justice to develop a demonstration community corrections system modeled on the

Transition from Prison to Community Initiative (TPCI) developed by the National Institute of

Corrections and Abt Associates (Parents and Barnett, 2004). Lawmakers, judges, and corrections administrators have taken a variety of steps creating this system. The first of these was the creation of Transition Accountability Plans (TAP) (Parents and Barnett, 2004). TAPs provide valuable information about offender needs to organizations that administer services post- release. Assessment is done through a series of different programs that are administered in the beginning of an offender’s time on probation, or at admission to prison and 6 months prior to release.

The second step was the creation of Missouri Reentry Process Teams to coordinate the activities of local public, private, and nonprofit organizations that might deliver reentry services.

This began with the formation state-level MRP Steering Team, which included representatives from the Department of Corrections, Department of Mental Health, Department of Revenue,

Department of Elementary and Secondary Education, Department of Social Services, Office of the State Court Administrator, Department of Economic Development, Department of Public

Safety, Department of Transportation and the Department of Health and Senior Services. This steering committee oversaw the creation of 34 local MRP teams that are responsible for coordinating reentry activities in 1 to 5 counties each. These teams work with parole officers and service providers to coordinate reentry activities with the goal of decreasing recidivism rates in

12 the community. As an example these Teams’ activities, the District 11 team, which is responsible for five counties, said in its annual report that it ―worked with the Central Ozarks Private

Industry Council Inc. (COPIC) in order to increase the effectiveness, efficiency and availability of client employment referrals and opportunities.‖ These teams resemble, though are not identical, to what scholars have identified as Network Administrative Organizations (See Provan,

Issett, and Milward 2004; Provan and Kenis 2008). They do not deliver services themselves, but instead focus on building overall network capacity. The act primarily as a coordinator of network activities, but may become more or less involved with individual programs as needed to produce network goals.

The third step that the state took produced the data for this project. The Community

Reentry Funding Initiative (CRFI) was launched in 2007 with the supervision fee collected by the Department of Corrections and the Division of Probation and Parole. The Initiative is designed to provide funding to innovative community organizations and programs to assist offenders as they reenter the community. The initiative focuses on programs that assist offenders with housing, transportation, employment, mental health and substance abuse, education, basic essentials, training, and a variety of other needs. The CRFI is intended to support local efforts that will reduce the risk presented by offenders to commit new crimes and return to prison. In

2009, CRFI provided funding to 36 reentry providers across the state. These organizations provide employment, housing, transportation and basic skills assistance, as well as cognitive behavioral counseling. These organizations were funded with the understanding that they would fit into community corrections networks already being coordinated (in most places) by a local

MRP team.

As a component of an evaluation conducted for the Missouri Department of Corrections and the National Institutes of Justice in 2010/2011, the Institute for Public Policy at the

University of Missouri collected data from the 36 funded organizations and the 4276 clients they served. Organizational data include information on staff, budgets, funding sources, clients served, and services offered, as well as communication with other reentry service providers, parole officers, and MRP teams. Client data include information on employment, housing, perceived needs, perceived support, and services received. We also collected information from the MDOC on clients and all other parolees and probationers in the state in 2010, which included demographics (race, sex, age, and marital status), risk assessment (from a standardized Field

Risk Reduction Instrument), offense type (violent, drug, etc…), sentence type (parole or probation), recommended supervision level, and reoffense.

In terms of network structure, our data collection allows us to see pieces (smaller and larger) of 15 community corrections networks around the state of Missouri. We know the basic structure of these networks based on the guidelines established by the state-level MRP team.

Typically, each network includes multiple service providers, the parole and probation office associated with the local district court, and the regional MPR team tasked with coordinating community corrections activities in the area. We can observe the degree of integration at the organizational level with data on MRP involvement in each organization’s delivery of services.

We can observe the activity of the 36 organizations we studied within these networks with data on their contacts with other service providers, municipal government agencies, community groups/members, and parole/probation officers. The greatest limitation of these data is that we do not observe all community reentry providers within each network. This limits the questions we can ask, but does not impair our ability to address the propositions discussed above, which

14 are all at the level of the organization or individual. Because we do not have data on every possible organization from which an individual could receive services, there may be redundancies that we cannot observe. These should, however, bias the results against our hypotheses and, therefore, be unproblematic for the analysis. Nonetheless, we urge readers to exercise the appropriate amount of caution given the imperfect nature of our data.

Dependent Variable

The dependent variable in all of our analyses is a dichotomous indicator of whether an individual was rearrested within one year of entering parole or probationary custody. We choose this measure because recidivism prevention is the primary stated objective of the Transition from

Prison to Community Initiative, the Missouri Reentry Project, and the Community Reentry

Funding Initiative. As such, it is the best measure of the effectiveness of the network of providers created and/or coordinated by these efforts. The overall reoffense rate in our data is .14, though that figure varies dramatically depending on the type of offender. The one-year recidivism rate is only .05 for persons with a recommended supervision level of 1. Alternatively, it is .33 among classified as Level 3, which indicates the most serious and/or repeat offenders.

Independent Variable

The primary independent variable in subsequent analyses measures whether an individual received redundant employment, housing, counseling, or basic skills services. There are several subcategories within each of these service types, including for example training, resume development, GED acquisition, and job search within Employment, or short-term shelter versus rental assistance within Housing. We do not directly observe which of these subcategories of service a client receives, but we do know the range of services provided by the organizations with which they are enrolled. In other words, we do not know whether Client A received resume

15 or training assistance from Organization X, but we do know that she could have received all 4 employment services from that organization.

The score on our measure of redundancy is determined by the services delivered by the organizations that an individual is enrolled with, as well as the date on which he or she began receiving services from them. If a person is enrolled contemporaneously with two organizations, redundancy is coded 1 if they receive services in the same category from both, when either one could have provided the service. As an example, if Client A simultaneously enrolls in employment services from Organization X and Y when X is a comprehensive provider (all employment services) and Y provides job training, we code that as redundant because they could have received all services from Organization X. Alternatively, if Client B simultaneously enrolls in employment services with Organization Y, which provides search assistance, and

Organization X, which provides training, that is coded as nonredundant because they could not have received all services from either of the organizations individually.

When individual enrolls in two programs noncontemporaneously (but within 6 months), the coding of redundancy depends most heavily on the services offered by the initial provider. If the first provider offers all services offered by the second provider, we code this as redundant.

Alternatively, if the second provider with which a client enrolls offers a different basket of subservices within a category, this is not coded as redundant. Using these decision rules, 477 individuals, or approximately 10% of those served, received redundant services in our sample.

As noted above, we code redundancy only for housing, employment, basic skills, and counseling services. Transportation services are not treated as redundant or not because numerous organizations assist clients with transportation to the services they provide. So, even if

16 an individual received transportation services from two providers it would not be redundant in the same way that receiving rental or job search assistance from two organizations is.

Moderating Variables

We have identified 4 factors that should moderate the relationship between redundancy and effectiveness in networks including integration, communication, capacity, and need. We measure integration, at least to the degree it affects our organizations, with a question to service providers regarding the ―involvement‖ that the local MRP team has with their program. Answers range from 1 for no involvement to 5 for very high involvement. To provide some substance to these numerical categories, one organization that marked a 1 said in an available comments field that ―We do not know our MRP team.‖ Alternatively, an organization that marked a 5 said, ―The local MRP team is very active and engaging. Attendance has remained high and many segments of our community are represented.‖ We assume that higher involvement from the MRP team is a good proxy for the level of integration and expect it to enhance the effect of redundancy on recidivism prevention.

We measure communication between the organizations we study and other network members (or centrality) by asking if they communicate regularly with other community organizations, members of the community, government officials, and the parole/probation office.

The measure ranges from 0 to 4, with higher values indicating more communication. We expect higher levels of communication to increase the impact of redundant service provision on effectiveness.

We measure capacity with the number of paid staff. Most of the organizations we study are 501(c)(3) service providers and there is evidence that staff resources correlate with capacity in these types of organizations (Eisinger 2002; Hall 1998). Nonprofit organizations can serve a

17 relatively large number of clients relying heavily on volunteer labor. Volunteers certainly provide valuable resources to the organizations they serve, but they are interchangeable with paid staff for only a limited proportion of tasks (Handy, Mook, and Quarter 2008). In part, this is because volunteers have significantly higher levels of turnover which affects continuity, institutional memory, and agency morale (Fischer & Schaffer, 1993). We expect redundancy to have a greater positive impact on effectiveness when staff capacity in the organizations providing redundant services is high.

Finally, we measure the need of individual clients—or the relative importance that the network act to help prevent their reoffense—with the supervision category. Based on the characteristics of current and past offenses, as well as the Field Risk Reduction Instrument score, which is comprehensive behavioral assessment designed explicitly to measure risk of recidivism, each offender in the Missouri system is assigned a Supervision Category between 1 and 3. The highest value represents those offenders who, by the state’s assessment, are at the highest risk of reoffense and need the most significant intervention to prevent that from occurring. We suggest that these offenders represent the greatest possibility for a Type II error in Heinman’s (1993) conceptualization, where government must act in order to produce a desired outcome. According to the author, these are the problems that redundancy is best suited to address. We expect that redundancy will have a greater positive impact on effectiveness as the supervision level of the individual offender increases.

Control Variables

Finally, subsequent models include a set of variables designed to control for alternative explanations for recidivism. The first of these is race. Nationally, Hispanics are slightly more likely to come in contact with the criminal justice system than Whites relative to their

18 population, while African Americans are significantly more likely to do so. More importantly for our purposes, the recidivism rate among African American offenders is also significantly higher

(CRS 2008). Evidence suggests that these differences become far less evident when controlling for other factors (see for example Florida Department of Corrections 2010), but we, nonetheless, include a dichotomous indicator of whether an individual is white or nonwhite. The second control variable measures sentence type. It is coded 0 for previously incarcerated offenders currently on parole and 1 for those who were sentenced to probation rather than a prison term.

Even after controlling for other factors, incarceration dramatically increases the risk of reoffense relative to those offenders who are allowed to remain in their communities. All models also include the supervision score discussed above as a control. In our theoretical story the variable moderates the impact of redundancy, but it is also the single best available predictor of recidivism (U.S. Sentencing Commission 1992).

Subsequent models also control for age, based on the recognition that the likelihood of criminal activity declines precipitously as an individual grows older (Florida Department of

Corrections 2010). We also control for offense type, based on evidence that certain types of offenders are most likely to come back in contact with the criminal justice system. Nationally, drug offenders have among the highest recidivism rates, because physical and mental addiction make it very difficult for these individuals to abstain from illegal behavior (New York State

Commission on Drugs and the Courts 2000). All models discussed below include a dichotomous indicator of whether or not an individual was convicted of a drug crime. Finally, we control for the ―dosage‖ of treatment received by offenders from organizations in the community corrections network. Prior to the data collection, organizations were asked to standardize their treatment measurement based on a metric that we developed and all providers agreed to. So, as examples,

19 all organizations reported 1 hour of job readiness classes as 1 unit of employment, or 1 day of rental assistance as 1 unit of housing, or 1 hour of substance abuse treatment/counseling as 1 unit of counseling. All models include a measure of the total treatment units.

Methods

The community corrections organizations that we study treated 4276 of the more than

100,000 persons on parole or probation in the state of Missouri in 2009/2010. Unfortunately we do not observe random selection of offenders into these organizations. In fact, sample selection is based on a mix of self-selection by offenders, suggestion/assignment by parole or probation officers, and recruitment by the organizations themselves. It is this decentralized selection process that we suggested at the outset of this paper could produce redundancy in network settings. Despite the seemingly stochastic nature of selection into these programs, however, we know that certain offender characteristics, such as age, race, sex, sentence type, and supervision level, predict selection into these programs. This complicates the analyses because many of these characteristics also predict the likelihood of reoffense. This selection effect biases the findings in most standard analytic techniques because any observed impact from a program or service is likely to be a function of the characteristics of those selected into the program. In other words, a program populated by older lower supervision offenders will correlate negatively with the likelihood of reoffense, but the result simply reflects the fact that older nonviolent offenders are less likely to reoffend.

The most appropriate way to deal with such bias is via a selection model and we employ a Heckman approach. Generally speaking, the Heckman procedure corrects for sample selection.

More specifically, the model estimates the two equations simultaneously and takes advantage of information from the individuals that select out of the sample in order to produce asymptotically

20 efficient and unbiased estimates for those that remain. In the case of reentry programs, the model uses information about individuals that are not treated by our grantees to more accurately estimate the probability of recidivism among those that are. In other words, it reduces the likelihood that differences in the characteristics between offenders treated and untreated offenders do not drive the findings. Because the dependent variable here is categorical, we employ a Heckman estimator that fits a maximum likelihood probit model with sample selection.

Because we are interested in the impact of organizational and individual factors on recidivism, as well as the interaction between these factors, we have an inherently multi-level question. Typically this would suggest the use of a Hierarchical Linear Model or some other multilevel estimator. Unfortunately, we are not aware of (and are not skilled enough to derive) a model that can deal with sample selection in the first stage and estimate an HLM in the second.

As an alternative, we estimate a model with both organizational and individual variables and cluster the standard errors at the organizational level. This does not provide all of the information that an HLM estimation would, like the proportion of variance explained by different levels, but it should produce unbiased results.

Findings

The results from Heckman probit models testing the propositions outlined above presented in Tables 1 through 4. Collinearity, as well as the difficulty of getting the selection models to converge when estimating multiple interactive parameters, compels us to test these in

4 separate models. We focus most of the discussion on the second stage results, presented in the bottom panel of the tables. Before moving to those we can briefly discuss the first stage models predicting selection out of our analytic sample, which are presented in the top panels. Because of missing data in the information collected from the MDOC, we have 85, 185 usable observations

21 in the total sample. The sample in the second stage is 4276, indicating 80, 955 censored observations. We predict censoring with race, age, sex sentence type, and supervision level. The model suggests that parolees and those with higher recommended supervision levels were more likely to select into, or be selected into, the programs funded by Community Reentry Funding

Initiative. In all 4 models, a significant Wald test suggests that the first and second stage equations are not independent of one another and, thus, that the selection model is appropriate.

The upper panel of Table 1 presents the second stage results from the model of the interaction between MRP team involvement and redundant service provision. Because we are measuring the impact of integration in both organizations that provide a redundant service, the model contains 2 measures of MRP involvement and 2 interaction terms, along with the indicator of redundancy. The null coefficients on MRP involvement suggest that, in nonredundant systems, increased integration also does not have an independent impact on the likelihood of reoffense. The insignificant coefficient on redundancy suggests that, when both measures of

MRP involvement were 0, it has no effect on the probability of recidivism, though it is important to remember that this condition is impossible because the lowest value of the MRP measure is 1.

The significant coefficient on the first interaction term indicates that redundancy becomes significant as the level of MRP involvement in the first organization’s program increases. The second interaction term is not significant either alone or jointly, indicating that, after controlling for integration in the first organization, the level of MRP involvement with the second does not moderate the impact of redundancy on recidivism.

In order to assess the direction of the impact of redundancy at higher levels of MRP involvement it is necessary to examine the predicted probability of recidivism at different values of the two variables and their interaction, with all other variables set to their means or modes.

We do so by calculating the change in predicted probability of going from nonredundant to redundant services in conditions of low and high MRP involvement. The results suggest that, when the level of MRP involvement is at the lowest level switching to redundant service provision from nonredundant decreases the probability of reoffense by .004. Alternatively, switching to redundant provision at the highest level of the MRP variable decreases recidivism by .008. This represents a substantial change, and given that the baseline probability of offense in this model is .072.

The control variables in this first model performed largely as expected. Parolees were more likely to reoffend relative to probationers, with former incarceration increasing the probability of recidivism by .04. Those with higher supervision scores also had a higher probability of recidivism, with the likelihood of reoffense increasing by .042 from the lowest to the highest category. Drug offenders were more likely than individuals that committed other crimes to come back in contact with the criminal justice system, but the substantive effect was inconsequential. Finally, total units of treatment correlated with a reduced probability of recidivism, suggesting that even after controlling for other factors, a higher dosage of the services offered in the community corrections network help to reduce the probability of reoffense. On the other hand, when other factors were controlled for, race of the offender did not have a significant impact on the likelihood of recidivism.

The upper panel in Table 2 contains the second stage model examining the moderating impact of network activity on the relationship between redundancy and recidivism. The significant coefficients on networking suggest that increased activity correlates negatively with the likelihood of reoffense when an offender is not receiving redundant services. The null coefficient on redundancy suggests that, in the absence of any network activity it does not have a

23 discernable impact recidivism. One of the interactions between networking and redundancy is significant suggesting that redundancy begins to have an impact on recidivism as network activity in at least one of the organizations providing redundant services increases.

Calculating the change in predicted probabilities suggests that the moderating impact of network activity is quite substantial. With at least one organization that engages in no network activity, redundancy actually appears to increase the likelihood of recidivism, though the change is not statistically significant. Alternatively, if that organization engages in the highest level of activity, redundancy decreases the probability of reoffending by a very large .038. Not surprisingly, the control variables performed identically, with parolees, those that received fewer treatment units, those with a higher risk score, and drug offenders having the highest probability of recidivism.

The upper panel in Table 3 presents the findings from the analysis of staff resources and redundancy in networks. In this case, both interaction terms are significant, suggesting that greater staff resources in the second organization providing redundant services moderate the impact of that redundancy even after controlling for the moderating impact of staff in the first organization. The positive and significant coefficient on the measure of redundancy indicates that it would actually increase the likelihood of reoffense if both organizations delivering services had 0 paid staff. Of course, that condition does not exist in our sample, but even if we calculate the change in the predicted probability of recidivism with both staff levels set to 10 employees, switching from nonredundant to redundant services increases that probability by

.004. Alternatively, if each organization has 50 full and part time staff, moving from singular provision to redundancy decreases the likelihood of recidivism by that same amount.

Finally, the results from the second stage model of supervision level are presented in the upper panel of Table 4. The significant interaction term suggests that redundancy is moderated by supervision, but the direction of the effect was different that we expected. At the lowest level of supervision, indicating offenders with the lowest risk of reoffense, a change from nonredundant to redundant services decrease the likelihood of recidivism by .006. However, at the highest level, receiving services from two rather than one provider increases probability that an offender will come back into contact with the criminal justice system.

Discussion

As noted above, the result regarding supervision level is contrary to our expectations. Our assertion here was that the benefits of redundancy should be particularly evident for the highest risk offenders, where the necessity of government action is highest. The findings suggest, however, that it is these offenders who are actually harmed by redundant services. The direction of this result holds, though not always the significance, if we use alternative measures for

―need.‖ More research is needed to better understand this result, but it may arise because of the opportunity costs that redundancy imposes. Receiving the same service from two providers helps to increase reliability, but it may also mean that an individual is forgoing another service that might benefit them. It appears as if the more serious offenders benefit from exposure the varied basket of services available in the community corrections network, rather than from the more reliable provision of one of those services.

The remainder of the results provide support for the propositions outlined earlier in the paper. Integration, captured at the organizational level by the involvement of the local MRP team in program delivery, increases the positive impact of redundancy. We suggested that this should be the case because integration has been shown to have a positive impact on network

25 performance (Graddy 2008) and because the redundancy literature has come to support some communication and integration among loosely coupled providers in multiorganizational networks (Landau 1991).

The benefits of integration in the case we study are particularly unsurprising because of the nature of the networks we examine in this study. The majority of these networks are in nonurbanized portions of the state and, typically, cover offenders in several towns across more than one county. This produces a condition that should increase the benefits of more centralized integration. Despite their distance from one another, offenders regularly receive services from providers in different communities. Because of those distances, however, the need for a coordinating entity that has a sense of the network as a whole, including which services are being offered by which providers and on what schedule, is particularly high. Under these conditions, we believe that redundancy may have greater benefits in more integrated networks because offenders (or their POs) that are unsatisfied with a service being received from one organization are more likely to be aware of programs in neighboring areas that can provide them with a slightly different version of that service. Even in cases where clients ―stumble‖ into redundant services, they may be more effective in integrated networks because the MRP team has worked to make them complimentary.

The findings also provide strong support for our assertion that greater network activity by an organization increases the benefits of redundancy. At the lowest levels of networking, redundancy actually correlates positively with the probability of recidivism, but as such activity increases it becomes negatively associated with reoffense. This result likely arises because more active organizations have a better sense of not only what other organizations in the network are doing, but also what community members and local government officials believe are the biggest

26 challenges facing offenders trying to gain employment, housing, or the skills and temperament necessary to stay out of prison. If redundancies in multiorganizational networks are generally better suited to identifying and solving service delivery problems relative to serial production systems (Landau 1991), this should be particularly true when those organizations communicate more regularly with others in the network about the nature of and solution to those problems.

Finally, our proposition that organizational capacity should increase the positive impact of redundancy was also supported by the analysis. Network scholars have begun to argue that capacity may moderate the positive impact of network activity and redundancy scholars have always suggested that parallel processing among high skill agencies was the best approach.

Similarly, we find that redundant service provision by organizations that do not invest heavily in human resources actually produces more negative outcomes for clients, while redundancy in organizations with higher staff capacity reduces the likelihood that an individual will reoffend

(relative to provision by a single organization). In many ways, this result is intuitive simply because having two organizations produce the same service when both have limited capacity to provide it is unlikely to produce measurable performance gains. Indeed, in our model, it is not just one, but rather both, redundant providers that must have above average staff capacity before the benefits of redundancy begin to accrue.

Conclusion

We began this essay with the assertion that the characteristics of policy implementation networks make redundancy and duplication among providers relatively likely and that it is important to understand the impact of that redundancy on network effectiveness. We explored that impact in an assessment of community corrections networks and demonstrated that redundant service provision improves outcomes for clients when integration, network activity,

27 and staff capacity are high. We believe the findings suggest a need for the literature on networks to seriously consider the topic of redundancy. Numerous questions persist about the impact of redundancy on effectiveness and answering these is important for scholars who study performance in network systems, but also for the policy makers who design them and the organizations that deliver public goods and services within them. There are also myriad questions for those that study the structure of and interaction within networks including: the extent of redundancy in service delivery networks, the variation that might exist based on the service being delivered or the size and structure of the network and the degree to which redundancies appear by design, to name just a few.

While we believe the results of our analyses do warrant a closer examination of the role of redundancy in networks, we also caution readers about drawing definite conclusions because of the many shortcomings in this study. First and foremost, these data were collected for a slightly different purpose than a study of network effectiveness and, therefore, do not always provide perfect measures of the concepts we are interested in. This is also a study of only a single service type delivered via a single type of network structure. Future research will determine if the results we report here are robust to more precise measurement, different service types, and more variability in network structure and governance.

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Table 1 Interactive Effect of MRP Involvement and Redundancy on Recidivism

Coef. s.e. Stage 2: Reoffense Race -0.129 0.135 Sentence Type -1.140 **** 0.173 Supervision 0.285 **** 0.061 Age 0.000 0.004 Redundancy -0.117 0.161 MPR org. 1 0.020 0.023 MRP org. 2 -0.007 0.022 Redun X MRP org. 1 -0.070 ** 0.037 Redun X MRP org. 2 0.077 0.048 Drug Offense 0.046 * 0.025 Total Units 0.000 ** 0.000 Intercept -0.708 0.665

Stage 1: Selection Race 0.187 0.128 Sex -0.096 0.099 Sentence Type -0.198 **** 0.040 Age 0.005 0.004 Supervision 0.268 **** 0.056 Intercept -1.941 0.579

Log pseudolikelihood = -17037.58 Wald test of indep. eqns., chi2(1) = 40.77 Prob > chi2 = 0.0000 N = 85231 Uncensored = 4276

Standard Errors Clustered on Organization. * p<.1, ** p<.05, *** p<.01, **** p<.000

Table 2 Interactive Effect of Network Activity and Redundancy on Recidivism

Coef. s.e. Stage 2: Reoffense Race -0.127 0.128 Sentence Type -1.151 **** 0.173 Supervision 0.287 **** 0.061 Age 0.000 0.004 Redundancy 0.013 0.041 Nework org. 1 -0.052 ** 0.026 Network org. 2 -4.072 **** 0.282 Redun X Network org. 1 0.019 0.030 Redun X Network org. 2 3.986 **** 0.281 Drug Offense 0.049 * 0.026 Total Units 0.000 ** 0.000 Intercept -0.604 0.680

Stage 1: Selection Race 0.187 0.128 Sex -0.098 0.098 Sentence Type -0.198 **** 0.040 Age 0.005 0.004 Supervision 0.268 **** 0.056 Intercept -1.939 0.579

Log pseudolikelihood = -17035.38 Wald test of indep. eqns. chi2(1) = 52.18 Prob > chi2 = 0.0000 N = 85231 Uncensored = 4276

Standard Errors Clustered on Organization. * p<.1, ** p<.05, *** p<.01, **** p<.000

Table 3 Interactive Effect of Staff Resources and Redundancy on Recidivism

Coef. s.e. Stage 2: Reoffense Race -0.127 0.137 Sentence Type -1.143 **** 0.173 Supervision 0.286 **** 0.062 Age 0.000 0.004 Redundancy 0.083 ** 0.040 Staff org. 1 0.000 0.000 Staff org. 2 0.000 0.000 Redun X Staff org. 1 0.000 **** 0.000 Redun X Staff org. 2 -0.002 ** 0.001 Drug Offense 0.045 ** 0.024 Total Units 0.000 * 0.000 Intercept -0.666 0.670

Stage 1: Selection Race 0.187 0.128 Sex -0.099 0.100 Sentence Type -0.198 **** 0.040 Age 0.005 0.004 Supervision 0.268 **** 0.056 Intercept -1.937 0.581

Log pseudolikelihood = -17036.32 Wald test of indep. eqns.chi2(1) = 45.54 Prob > chi2 = 0.0000 N = 85231 Uncensored = 4276

Standard Errors Clustered on Organization. * p<.1, ** p<.05, *** p<.01, **** p<.000

Table 4 Interactive Effect of Supervision Level and Redundancy on Recidivism

Coef. s.e. Stage 2: Reoffense Race -0.130 0.135 Sentence Type -1.147 **** 0.174 Supervision 0.276 **** 0.062 Age 0.000 0.004 Redundancy -0.200 0.136 Redun X Supervision -0.091 * 0.053 Drug Offense 0.048 ** 0.025 Total Units 0.000 * 0.000 Intercept -0.625 0.684

Stage 1: Selection Race 0.187 0.128 Sex -0.097 0.097 Sentence Type -0.198 **** 0.040 Age 0.005 0.004 Supervision 0.268 **** 0.056 Intercept -1.940 0.579

Log pseudolikelihood = -17038.27 Wald test of indep. eqns. chi2(1) = 40.55 Prob > chi2 = 0.0000 N = 85231 Uncensored = 4276

Standard Errors Clustered on Organization. * p<.1, ** p<.05, *** p<.01, **** p<.000