Structured to Partner: School District Collaboration with Nonprofit Organizations in Disaster Response

8/9/2019 Structured to Partner: School District Collaboration with Nonprofit Organizations in Disaster Response

1/35

Structured to Partner

0

TITLE PAGE:

Structured to Partner: School DistrictCollaboration with Nonprofit

Organizations in Disaster Response

Scott E. Robinson*

Bush School of Government and Public Service

And

Angela Bies

Bush School of Government and Public Service

* Corresponding author: [email protected]


2/35


1

Structured to Partner: School District Collaboration with Nonprofit

Organizations in Disaster Response

Abstract:

Emergency preparedness and response is moving from a specialized circle of

emergency management professionals and select nonprofit organizations (such as

the Red Cross and other national relief organizations) to include a broader varietyof organizations not traditionally fulfilling emergency management roles,

including schools. It is not clear who among these new potential members of

emergency preparedness networks collaborates with whom. We present the

results of a survey of Texas public schools and test how structural characteristicsare related to collaboration with nonprofits and relief organizations following a

local, visible disaster, that of the 2005 Gulf Coast Hurricanes. Our results showthat the propensity to collaborate is related to the size of the districts and its

degree of centralization, even while controlling for a districts general

collaborative tendency.

Keywords: Collaborative Public Management, Nonprofit Management, Emergency Management


3/35


2

Introduction

Numerous and frequently poignant stories emerged after Hurricane Katrina in the fall of

2005. Most remember the horror stories of the crowds of people neglected at the Superdome.

Many remember the images of entire neighborhoods being washed away as the flood waters

overtook the levees in New Orleans or wiped away as the storm scoured coastal communities.

The result of these perceptions resulted in a negative assessment of how the government

(defined broadly) handled the disaster. Not all of the stories, however, were so negative. Amid

the stories of government neglect and incompetence (which sadly, often overshadowed stories of

the heroism and competence of many civil servants), many contrasting stories emerged about the

successful efforts of private companies and nonprofit organizations to provide relief. These

private and nonprofit organizations emerged as important secondary disaster organizations

organizations who provided services to those affected by a disaster but whose mission was not

primarily disaster related. Examples of such secondary disaster organizations include local

churches that provided shelter or food to people in their community who lost their homes;

schools that enrolled the children of evacuees; or animal protection organizations that provided

assistance to evacuees with pets. These stories led to a shift in public perception about the role

and capacity of government to address future disasters adequately, and led many to call for an

increased role for private and nonprofit organizations, as secondary disaster organizations, in

governmental planning for emergency response.

The emergence of private and nonprofit actors as key players in disaster response has

raised the profile and perceived utility of secondary disaster organizations in emergency

management. It is now clear that emergency preparedness must include a role for local business


4/35


3

organizations, local welfare agencies, local education institutions, local religious institutions, and

many others. It is increasingly clear that effective disaster response calls for the incorporation of

organizations for which disasters are not their primary mission. In this paper, we address two

aspects of the non-governmental components of emergency management. Using a survey of

Texas public school districts, we investigate how school districts (not primarily disaster

organizations) connect to nonprofit organizations (including many relief organizations and non-

disaster nonprofits). In the first part of the paper, we discuss the existing literature on

collaboration and emergency response. From this literature, we adapt general hypotheses related

to collaborative propensity between public organizations and nonprofit organizations. The

hypotheses focus on the role of organizational structure to create capacity for collaboration. We

then describe a survey that we conducted of Texas school districts to assess their collaborative

practices in emergency response and its results. Finally, we discuss the most important questions

remaining about the collaboration between public and nonprofit organizations.

Collaboration and Disaster Response

Collaboration has been the subject of great interest in policy and practice discussions

(e.g. Choi, 2008; Gazley & Brudney, 2007; Graddy & Chen, 2006) and become a popular subject

of speculation and research in public administration (Foster & Meinhard, 2002; McGuire, 2006;

OToole, 1997; Waugh & Streib, 2006). Despite such interest, collaboration is defined variously

and with continuing imprecision. In general, collaboration is considered in terms of organizing

structures, in which collaboration exists on a continuum from informality to formality, with a

central concern resting on the issue of at what point a cooperative relationship transforms into a

collaborative one. Across this continuum, for example, collaboration might span from structures


5/35


4

that are informal and episodic, such as collaboration on a task force by business, citizen, and

nonprofit organizations to address an acute public health concern, to highly formalized contracts

between governmental agencies and social service providers.

Collaboration has also been defined in terms of process aspects around a problem about

which parties that see different aspects of a problem can constructively explore their differences

and search for solutions that go beyond their own limited visions of what is possible (Gray,

1989, p. 5). It is clear, however, collaboration suggests something less than authoritative

coordination and something more than tacit cooperation (Foster, 2002, p. 19). Collaboration for

our purposes is defined as a formal organization providing assistance in an emergent fashion to

address a disaster response need, whether as part of an existing collaboration or in the context of

a new relationship or venture.

In terms of cross-sector collaboration, the definition focuses on the parties to the

collaboration. Bryson, Crosby and Stone define cross-sector collaboration as partnerships

involving government, business, nonprofits and philanthropies, communities, and/or the public

as a whole (2006, p. 44). The specific policy domains in which people have investigated

collaborative public management have ranged from environmental regulation (Koontz &

Thomas, 2006), social service delivery (Provan & Milward, 1995), and the focus of this paper

emergency management (Waugh & Streib, 2006; Simo & Bies, 2007). In each of these areas,

writers have been optimistic that collaborative practices will improve outcomes and efficiency,

as well as increase the democratic inclusion of stakeholders. However, there are still many

standing questions in relation to policy networks and collaborative public management

(Robinson, 2006).


6/35


5

One gap in our knowledge that is particularly salient to collaboration and emergency

response is the proclivity for secondary disaster organizations to engage in collaborative public

management practices, and more specifically, the proclivity for collaboration with nonprofit

partners. Why is it that some secondary disaster organizations connect with other organizations

in responding to or planning for disasters while others do not?

In the sections that follow, we explore the dynamics of collaboration with special

attention to issues related to collaboration in disaster response contexts. We review the

controversies over the meaning of collaboration as well as the forces that are hypothesized to

predispose some organizations to collaborate. The section concludes with general hypotheses

reflecting our literature-grounded expectations of the role of organizational structure in the

decision whether to collaborate.

Choosing To Collaborate

A key area requiring investigation is how partners choose to link into collaborative

networks. For the most part, research (especially the large N quantitative literature) has focused

on the characteristics of collaborations in operation. The issue of how participants decide to

participate is central to the democratic potential of collaborative networks. Fung (2006) warned

of the illusory nature of some forms of participation. Even large numbers of participants in

administrative processes may not be particularly democratic if the participants are similar and

fail to represent the diversity of the effected population. A key mistake, Fung noted, was to

assume that self-selected participation systems would likely result in diverse participants. While

the solutions to the potentially skewed distribution of participants are not obvious or inexpensive,


7/35


8/35


7

Collaboration in Emergency Management and Disaster Response

Scholars and practitioners in emergency management have been among those most active

in promoting the importance of collaborative public management. By their very nature,

emergency management taxes the capacity of any organization or community. As has been

evident in the aftermath of recent emergencies ranging from terrorist attacks to natural disasters,

no one organizationor even municipality or statecan deal with large-scale emergencies

alone. It is nearly impossible, then, to effectively prepare for or respond to emergencies without

extensive collaboration.

Louise Comfort has been a pioneer in researching collaborative public management in the

area of emergency response. Her work on complexity theory and the management of emergency

response networks has made it clear to researchers and practitioners alike that emergency

response requires collaborative networks and that these networks raise a number of new

questions about the governance of these arrangements (Comfort,1994, 2007). The most basic

question raised in these studies was who is it that participates in disaster response?

Recent research into large scale disaster response has noted the prominent role of

nonprofit organizations in providing response and relief service. For example, research on the

response to Hurricane Katrina revealed the importance of nonprofit organizations in community

response to disaster. Robinson, Berrett, and Stone (2006) described the emergence of many new

partnerships between nonprofit organizations and local government officials in the Dallas/Fort

Worth area, as well as among networks of nonprofit organizations. This early research showed

how the picture of emergency response following the hurricanes of 2005 would have been

woefully incomplete without including the nonprofit collaborations. Similarly, Simo and Bies


9/35


8

(2007) documented the diversity of nonprofit collaborations along the Gulf Coast generally

during the same response period.

Mirroring the general literature on collaboration, the research into nonprofit

collaborations in disaster response have documented that there are many such collaborations.

What is lacking is a theory of why some collaborative relationships emerge while many other

potential collaboration fail to materialize. Why do some organizations have a diverse set of

collaborative partners while others collaborate with few (or no) external partners? We seek to

address this question. Before we present our research, we want to take a moment to discuss

collaboration from the side of the nonprofit organization.

Collaborations with Nonprofits

Nonprofits, like public agencies, are increasingly engaged in collaborative activity to

achieve public purposes. Such collaboration routinely cuts across across government, business,

and nonprofit sectors (Milne, Iyer, & Gooding-Williams, 1996), especially as nonprofit-public

sector collaborations become a prominent feature of public service delivery in the United States

and abroad (Gidron, Kramer, & Salamon, 1992).

In their research on government-nonprofit collaborations,Gazley and Brudney (2007)

argued that a desire for resource stability facilitates collaboration. The authors found that the

motivation by nonprofits to collaborate is driven by an organizations need to secure scarce

resources, with funding for nonprofits and its expertise and capacity for government as

predictors (p. 340). Government and nonprofit respondents provided remarkably similar

responses to questions about the goals of the collaboration explaining collaborations as

valuable in jointly addressing problems, improving community access to a service, improving


10/35


9

community relations, and promoting shared goals (p. 398). Their research also suggested that

nonprofits differ in some respects from government in terms of motivations to partner, with

nonprofits (relative to government) more likely to emphasize using collaborations to build

relationships to presumably help them gain resources (p. 398).

Gazley and Brudney also explored factors that explain non-collaboration. The authors

found that concerns about internal capacity and mission, rather than external factors such as

statutory pressure, appear to provide the strongest rationale for entering or avoiding public-

private partnerships (p. 412). Jang and Feiock (2007) also suggested that the costs of

collaboration and subsequent influence of nonprofits financial stakeholders are associated with

collaborative behavior.

Similarly, recent research on nonprofit-school partnerships provides a nuanced view of

institutional and environmental factors on collaboration, and indicated that organizational

characteristics and local policy environments may have a significant effect on the form and

intensity of collaborative efforts (Sowa, 2008, p. 317). In her study of interagency collaborations

to deliver early childhood services between school districts and community-based organizations,

Sowa (2008) emphasized the importance of examining the type of resources shared between

organizations in a collaborative relationship, and beyond this, the commitment of collaborative

partners, because varying degrees of commitment may produce significant variation in the

implementation of these collaborative service delivery mechanisms (p. 317). To the extent that

nonprofit organizations operate on different resource bases than organizations in other sectors,

Sowas argument suggests that there may be inherent difficulties in cross-sector collaboration.


11/35


10

This research illustrates the interest in better understanding the complexities of

collaboration that crosses sector boundaries between government and nonprofit organizations.

Given the prominence of these collaborations and the complexities involved in crossing sector

boundaries, it is important to test propositions about why some government organizations choose

to collaborate with nonprofits while others do not. The next section develops general

propositions about when organizations are likely to seek collaborative partners grounded in the

theory of inter-organizational relations.

Theoretical Expectations

Based on the existing literature reviewed above, we have expectations about the sorts of

factors that would increase the probability that a public agency would seek to collaborate with a

nonprofit organization. It would be prohibitively complex to assess all of the possible factors that

can influence the propensity to collaborate with nonprofits. We have chosen to focus on how

organizational structure affects the propensity to collaborate. We will control for other potential

factors in the empirical test, but our theoretical focus will be on structure in this section.

First, we expect larger organizations to possess the slack resources and the opportunities

for structural specialization to facilitate collaboration of any type. As a general effect, this should

affect organizations propensities to collaborate with nonprofit organizations. This expectation is

defended in detail in Thompsons classic text Organizations in Action (2003[1967]).

Organizations generally seek to protect their core operations from turbulence in the environment.

Managers dont want the people who perform functions close to the core mission of the

organization to worry about the supply of resources or other potentially uncertain components of

the environment. As a result, they create specialized units to monitor and interact with the


12/35


11

uncertain environment. Only larger organizations, though, have the economies of scale to devote

resources to these sorts of specialized units. Larger organizations can have separate press

relations units, analysis departments, or the like. All of these units serve to keep people

performing the core operations of the organization in their daily tasks.

Recent empirical work confirms some of theoretical explanations for nonprofit

collaboration reviewed above. Specifically, resource-dependence and environmental uncertainty

explanations for collaboration have been supported in the nonprofit literature (e.g., Gazley &

Brudney, 2007; Guo & Acar, 2005; Foster & Meinhard, 2002; Singer & Yankey, 1991). The

diversity of the evidence convinces us that this phenomenon is general across sectors. As a

result, we offer the following proposition.

Proposition One: Larger organizations are more likely to collaborate than smaller ones.

In addition to organizational size, the arrangement of resources within an organizations

structure is also likely to be important. Using the same Thompsonian logic as above, one expects

to see greater investment in boundary spanning operations in organizations where resources are

sufficiently centralized to realize economies of scale. If resources are decentralized, each unit

divides the potential base for such boundary spanning organization. Where these resources are

instead centralized, it is more likely that an organization can overcome the threshold start-up

costs needed to create a boundary spanning relationship. As a result we also propose the

following:

Proposition Two: Organizations with a greater degree of centralization are more likely to

collaborate than organizations with a lower degree of centralization.


13/35


12

We outline our approach to testing these propositions and the results of our tests in the next

section.

METHODS AND RESULTS

These propositions call for extensive data on the collaborative behavior of organizations

and a testable model of these behaviors. In this section, we review the data collection strategy

and our approach to operationalization, a nested regression model with which we simultaneously

test hypotheses derived from propositions one and two, and the results of the analysis.

Data Collection

As reviewed in a previous section, scholars have studied collaborative behaviors in a

variety of policy arenas. We chose to test these propositions within the context of emergency

management and disaster response. Specifically, this choice allows us to test the propositions

related to collaboration with nonprofit organizations in ways that would not have been possible

in other policy arenas. We chose to survey Texas public school superintendents about their

collaborative behaviors for a variety of reasons related to the strength of this sampling frame and

the saliency of the context.

First, this sampling frame is well studied and provides us a background against which to

compare our survey methodologyreducing the probability of results that are the result of

unpredicted characteristics of an untested sampling frame. Second, the sampling frame is one

where repeated surveys have resulted in relatively high response rates. This reduced the chance

that the survey would result in a data set that was too small for statistical analysis. Third,


14/35


13

previous research on the sample frame allows us to leverage years of previous research to test

propositions about structural and financial characteristics of the agencies. Fourth, the subject area

of collaboration (emergency management and disaster response) was salient, allowing for survey

measurement at a time when the respondents were most likely to be deliberately choosing

collaborative strategiesrather than operating under unconscious forces of structural inertia.

We conducted the survey in the immediate aftermath of Hurricanes Katrina and Rita

(about 60 days after Katrina). One should not discount the importance of this salience. Salience

makes the survey a better test of conscious strategies, but may misrepresent more typical

strategiesbut this is a question for future investigation. While this salience does create an easy

test of collaboration it tests for collaborative behaviors when these behaviors are most likely

it is appropriate to use an easy test strategy when studying relatively unstudied behaviors (like

the decision of public agencies to collaborate with nonprofits in emergency management).

Finally, the sampling frame includes an array of organizations that varied on all of our variables

(large and small districts, districts with recent or distant emergency experience, etc.).

The survey went to all public school district superintendents in Texas with two follow-up

waves. The final response rate was approximately 50%. Comparisons of the respondents to

known characteristics of the non-respondents suggest that the respondents were slightly (though

not statistically significantly) more affluent than non-respondents and significantly larger on

average (though of a relatively small magnitude). With these comparisons, we are confidents that

the results of the survey provided reasonable approximations of the population values.

Operationalization


15/35


16/35


15

months of 2005. We have greater faith in patterns observed across tests using multiple

operationalizations of collaboration.

Each proposition also includes an independent variable. The first proposition is related to

organizational size. We use state data on each school district to measure each districts size. For

each district, we included the logged total number of full time equivalent employees as the

measure of size. This measure is highly correlated (at around .997, depending on the year) with

other popular measures of district size such as total enrollment. We logged the value to reduce

what was a significant skew. This measure of size represents organizational size and not the

geographical area served by the district. This limitation will be important as we discuss the future

directions for research in the area.

The second proposition deals with the structural centralization within the district. We

operationalized this concept in two ways. First, we included a measure of the percentage of the

district budget dedicated to central administration. High values indicate a leaders general

preference for centralized offices rather than campus-based capacity. This measure is similar to

prior measures of administrative centralization and bureaucratization (Robinson, 2004; Robinson

et al. 2007). Second, we asked specifically about the districts strategy toward delegating

preparedness activities to the individual campus level. The specificity of the question should

balance the pressures toward reporting centralized emergency preparedness with the general

predisposition among education administrators (and the programs that certify them) to pursue


17/35


16

campus-based policymaking. The result was a response pattern that suggests variety in district

delegation strategies (ranging from delegation to centralization).1

While we expect that these measures of size and centralization to explain some of the

variation in observed collaborations between school districts and nonprofit organizations, we do

not expect that these propositions will explain all of the variation. We take various steps to

ensure that spurious forces are not contaminating our inferences related to propositions one and

two. To control for the differences in disaster vulnerability and recent disaster experience, we

included variables that measure reported impact of recent disasters as well as the perceived

likelihood that the district will experience a disaster in the near future.

Even with the control for disaster vulnerability, there are a variety of factors that could be

spuriously related to both organizational structure and collaboration with nonprofit

organizations. The set of variables omits a variety of important concepts in collaborative public

management that we simply could not simultaneously measure. To assess whether our model is

greatly affected by this under-specification, we tested two models. The first is as described

above. The second specification includes an additional variable of how many collaborations,

other than those reported with nonprofit and relief organizations, the superintendent reported.

This serves as a control for general collaborative predisposition and will account for all of the

unmeasured factors related to both general collaboration and nonprofit collaboration. Any

reported effects in the models that include the general collaborative tendency measure indicate

the influence of the variable on the specific probability of collaborating with nonprofit and relief

1These two measures of centralization do not present multi-collinearity problems in that they are uncorrelated

with each other.


18/35


17

organizations beyond the effect that the variable has on general collaborative tendencies. This

represents a hard test for the relationships considered.

Regression Model

We test our two propositions using a probit regression model. The dependent variable

was given a value of 1 if the superintendent reported that the district had collaborated with

nonprofit or relief organizations (or had regularly scheduled meetings with them in the third

model). If the superintendent reported that they had not collaborated with nonprofit

organizations, the dependent variable was given a value of 0. Non-responses were omitted from

the analysis.

Besides logging the size and centralization variables (as described above), we assume

simple linear relationships between all of the independent variables and the dependent variables.

While nonlinear relationships are potentially interesting, we reserve those possibilities for future

research. The result is the following regression equation.

EQ.1 - P (Nonprofit Collaboration) = f [0 + 1 (Size)) + 2 (General Delegation) + 3

(Emergency Planning Delegation) + 4(Likelihood) + 5 (Recency) + 6(Impact) + ]

We also tested models that added the general collaborative predisposition. The result is equation

2.

EQ. 2 - P(Nonprofit Collaboration) = f [0 + 1 (Size)) + 2 (General Delegation) + 3

(Emergency Planning Delegation) + 4(Likelihood) + 5 (Recency) + 6(Impact) + 7 (General

Collaborative Tendency.)+ ]

For each of the models, we employ robust estimators to produce robust standard errors.

Results


19/35


18

The results of the three models are presented in Table 1. Model 1 performs relatively

well, successfully predicting 68% of collaborations and for a proportionate reduction of error of

37%. The model follows previous results on generalized collaborative behaviors in suggesting

that organizational size and centralization both make collaboration with nonprofit organizations

more likely. These three variables (including the two measures of centralization) are in the

predicted direction (larger organizations and greater centralization induce greater probability of

collaborating with nonprofits) and were strongly significant.

Models 2 and 3 provide tests on the robustness of these initial findings. Model 2 adds the

general collaborative tendency as an independent variable. This robust model performs better

overall with a predictive accuracy of 76% with a proportionate reduction in error of 52%. This

additional variable changes the nature of the estimated values. As one would predict, the general

collaborative tendency is strongly significant. The effect size is also quite large.

Figure 1 illustrates the size of the effect of general collaborative tendency. Using the

simulation techniques described in Appendix 1, we estimated the predicted probability of

collaboration for each level of general collaborative tendency while holding all other variables at

their mean (for continuous variables) or median (for ordered variables). For purposes of

comparison, note that 48% of superintendents reported collaboration overall. As Figure 1

illustrates, the expected value of an otherwise typical district with no other collaborations is less

than 20% likely to collaborate with a nonprofit or relief organization. The line in the center of

each box plot is the median prediction with the grey and outer enclosed areas representing the

uncertainty around the estimated median. An organization with five other collaborative partner

types (the maximum possible) is over 80% likely to collaborate with a nonprofit or relief


20/35


19

organization. These predicted probabilities give an indication of the large effect size of

generalized collaborative tendency, which is not surprising.

The introduction of such a baseline variable, much like adding a lagged dependent

variable in a time series model, reduced the magnitude and significance of the other components

in the model. The size of the organization is still significant though the effect size is reduced by

25%. Still, the variables representing organizational size and centralization were significant in

both models.

Model 2 serves as a hard test for any variable, so we focus our analysis on the results of

this hard test. Figure 2 illustrates the effect of organizational size on the probability of


21/35


20

collaboration with nonprofit or relief organizations. The figure reports the estimated probability

of collaboration for an organization at each decile within the sample (e.g., an organization that is

larger than only 10% of other districts, an organization larger than only 20% of other districts,

etc.).

As seen in Figure 2, when all other variables were held at typical values larger

organizations are much more likely to collaborate with nonprofit and relief organizations.

Districts larger than only 10% of other districts were about 20% likely to have reported

collaboration. Districts larger than 90% of other districts were about 80% likely to report

collaboration.

Figures 3 and 4 report the effect of our two measures of centralization. Both general

centralization and emergency management centralization are significant factors in the prediction


22/35


21

of collaborations with nonprofit and relief organizations. Figure 3 shows that the effect of

general centralization can increase the probability of collaboration from 25% to 70% as one

moves from the first to the ninth decile of centralization. Similarly, Figure 4 shows that as one

moves from planning that is reported as entirely or mostly campus-based to entirely district-

based, the expected probability of collaboration with nonprofit or relief organization increases

from 34 to 54%.


23/35


22

Model 3 changes the dependent variable to regularly scheduled meetings with nonprofit

and relief organizations. The baseline probability of such collaborations is much lower. In the

survey sample, only 15% of districts reported holding regularly scheduled meetings with

nonprofit or other relief organizations. The relative rarity of the collaborations makes estimating

a model predicting the relatively rare events difficult. The predictive power of the model seems

strong at 85%, but this represents only a 3% proportionate reduction of error because the nave

model of simply always predicting that a district will hold no regularly scheduled meetings will

be correct only 84% of the time. Proportionate reduction of errors is typically low in the presence

of such relatively rare events, as in this case.

Given the importance of the generalized collaboration variable in model 2, we have

included it here as well. Here again, generalized collaborative tendency is the most important


24/35


23

explanatory variable. Figure 5 illustrates the effect of general collaborative tendencies, all other

variables held at typical values. It is important to notice that the scale of the figure has been

changed. Although the figures for collaboration reported simulated probability ranging to 100%,

the figures for regularly scheduled meetings only go to 50% because of the rarity of the

prediction.

Figure 6 illustrates the impact of organizational size on regularly scheduled meetings. Again, the

variable is significant but the effect is only dramatic at large district sizes.


25/35


24

Figure 7 illustrates the impact of general centralization and emergency management

centralization. General centralization has the same muted effect seen for size and general

collaborative tendency as shown in Figures 5 and 6. Interestingly emergency management

centralization is not significant in this model. The implication is that the tendency to schedule

regular meetings with nonprofit and relief organizations is not related to reported tendencies for

internal emergency planning, but only to general district policies regarding centralization.


26/35


25

Discussion and Directions for Further Research

Our results just scratch the surface of the issues related to collaboration with nonprofit

organizations. These initial results suggest that there is much to learn about why collaborations

emerge. Structural factors provide some leverage in explaining the collaborative behaviors of

these civil servants. The mix of influences depends on the nature of the collaboration. The results

do more to reveal the limitations of the extant conceptual toolbox related to collaborative public

management than to shed light on the collaborations themselves.


27/35


26

Consider this example. The findings depend on whether collaboration is defined openly

(in this case, allowing survey respondents to define it for themselves) or whether a strict

definition requiring regularly scheduled meetings is used. The evidence suggests that these two

different activities are quite distinct. This raises the question of whether the data represent two

different types of collaboration, two extremes of one essential type of collaboration, or whether

only one of these is bona fide collaboration. We do not currently possess the conceptual

framework to distinguish between these possibilities nor is there sufficient consensus on the

definition of collaboration to distinguish between different types. This sort of conceptual work is

a precondition for compelling interpretations of the data.

While conceptual limitations affect our ability to interpret the results of the analysis,

significant data limitations are also present. The survey used here went to public managers and

provides insight into the government side of the cross-sector collaboration. What we do not have

is data on the nonprofit side of the collaboration. The review of the literature revealed some

important similarities between the theories of public sector collaboration and cross-sector

collaborations; in the present research we were not able to test propositions about the nonprofit

side of the relationship.

The key data we still need to develop is on the supply side of the cross-sector

collaborations. Scholars have generally hypothesized that nonprofit supply is expressed as a

function of the needs and resources of the region (Bielefeld & Murdoch 2004, p. 222). Studies

(e.g., Gronbjerg & Paarlberg, 2001; Wolch & Geiger, 1983) have confirmed the robustness of

this assumption. More specifically, population growth (Bielefeld, 2000), community wealth

(Wolch & Geiger, 1983; Wolpert, 1993; Corbin, 1999), and proportion of older residents

(Gronbjerg & Paarlberg) have been identified as determinants of the nonprofit supply. Peck


28/35


27

(2008) summarizes the literature on the nonprofit location as tending to frame the determinants

of nonprofit location as a function of neighborhood characteristics, hypothesized as:

measures of need (such as poverty density), popula tion characteristics (such as

education levels, racial distribution, age, families' structure), community

characteristics (such as heterogeneity, incorporation date, urbanicity), resources (such

as government expenditures, property tax base, housing values), and other

organizations' locations (both for-profit and nonprofit organizations.(p. 139)

Although nonprofit supply is somewhat implicit in the environmental and structural

explanations for nonprofit motivation to collaborate, it has generally not been explicit and has

typically been defined in non-spatial terms. Measurement challenges may partially account for

this gap, as may the fact that the collaboration and supply literature streams have developed

concurrently, but for somewhat distinct purposes. Service provision by nonprofits has been used

as a measure of nonprofit sector size, as has annual expenditures by nonprofits. Spatial analyses

are increasingly promising, with new methods allowing multidimensional considerations to

space, such as nonprofit income in relationship to nonprofit concentration and population

characteristics. To the extent that larger school districts are in locations with a greater supply of

partners, some of the role of size in the statistical models may be picking up factors related to

nonprofit partner supply. The inclusion of the general collaborative propensity addresses this

problem to a great extent. To the extent that nonprofit supply is related to the supply of other

sorts of actors (general partner density), the general collaboration propensity control variable will

partial out these factors. All that will remain are the elements unique to nonprofit supply and not

correlated with the supply of other actorsa much more limited problem.

It is not surprising that there are few data on the supply of nonprofit organizations. There

are considerable conceptual difficulties in developing such a measure. What is the relevant range


29/35


28

of a spatial measure of nonprofit supply? Should we only count nonprofit organizations located

within the geographic boundaries of, in this case, a school district? What prevents a nonprofit

from nearby assisting? How broad should we draw that circle? Our intuition is to bring the

measure up to the level of a metropolitan standard area but this measure is itself difficult to

implement and would dramatically reduce our sample to school districts within MSAs. Despite

these difficulties, future work that directly measures nonprofit partner supply would be a great

addition to the model. Further, to be able to parse nonprofit partner supply by primary or

secondary mission focus on emergency and disaster response could assist understanding of cross-

sector collaboration in the specific policy domain of emergency response and could have

implications for disaster planning, particularly in deliberative efforts to improve cross-sectoral

network formation.

This research leaves us wanting to press forward in three directions. First, we want to

elaborate the theory of collaboration to understand better which factors should be related to

which types of collaboration. Second, we would like to collect data (beyond the available IRS

Form 990 data) on the nonprofit side of these cross-sector collaborations to see how structural

elements on the partners side can influence the emergence of partnerships. Third, we would like

to develop measures of the supply of nonprofit organizations available to public agencies as

partners. These developments should provide considerable insight into cross-sector collaborative

networks.

With the increased importance of secondary disaster organizations, it becomes all the

more important to understand the dynamics of collaboration. In the cases studied here,

secondary disaster organizations (school districts) sought collaboration in relation to emergency

management and disaster response with nonprofits to varying degrees. The data suggest that


30/35


29

these secondary organizations may be limited by their organizational structures as to the level of

collaboration they are likely to seek. However, these secondary organizations are those that most

need to seek additional resources (including expertise) from partners. Attempts to cultivate

collaboration between secondary disaster organizations and others in matters of emergency

management and disaster response needs to be sensitive to the importance of structure and the

barriers to collaboration that poorly suited structures may represent.


31/35


30

Table 1.Regression of collaboration with nonprofit organizations

MODEL 1 - THE BASELINE COLLABORATION MODEL

MODEL 2 THE BASELINE COLLABORATION MODEL WITH THE GENERALIZED

COLLABORATION VARIABLE ADDED FOR ROBUSTNESS

MODEL 3 THE REGULAR MEETING COLLABORATION MODEL WITH THE

GENERALIZED COLLABORATION VARIABLE

______________________________________________________________________________

Model (1) (2) (3)

Organization Size .45 (4.4) .44 (4.5) .43 (4.3)

Planning Delegation .16 (2.0) .18 (2.1) .13 (.14)

General Centralization .68 (3.5) .84 (3.8) 1.1 (4.3)

General Collaborative ---------- .44 (4.5) .12 (2.2)

Propensity

N 435 435 470

Percent Correctly Predicted 68% 76% 85%

Percent Reduction in Error 37% 52% 3%

Note: The absolute values of the Z-statistics are in parentheses.

Controls for perceived likelihood of disasters and the impact of recent disasters were also

included in all models.

Coefficients significant at greater than .05 are in bold.*


32/35


31

REFERENCES

Alexander, J., & Nank, R. (2009). Publicnonprofit partnership: Realizing the new public

service.Administration and Society,41(3), 364-386.

Bailey, D. & Koney, E. M. (1996). Interorganizational community-based collaboratives: A

strategic response to shape the social work agenda. Social Work,41(6), 602-611.

Bardach, E. (1998). Getting agencies to work together: The practice and theory of managerial

craftsmanship. Washington, DC: Brookings Institution Press.

Benjamin, L. (2008). Bearing more risk for results: Performance accountability and nonprofit

relational work.Administration & Society, 39(8), 959-983.

Bielefeld, W. (2000). Metropolitan nonprofit sectors: Finding from NCCS data. Nonprofit and

Voluntary Sector Quarterly, 29(2), 297-314.

Bielefeld, W., & Murdoch, J. (2004). The locations of nonprofit organizations and their for-profit

counterparts: An exploratory analysis.Nonprofit and Voluntary Sector Quarterly, 33(2),

221-246.

Brinkerhoff, J. (2002). Government-nonprofit partnership: a defining framework. Public

Administration and Development, 22(1), 19-30.

Brown, T.L. & Potoski, M. (2003). Contract-management capacity in municipal and county

governments. Public Administration Review, 63(2), 153-164.

Bryson, J. M., Crosby, B. C., & Stone, M. M. (2006). The design and implementation of cross-

sector collaborations: Propositions from the literature. Public Administration Review,

66(Suppl. to Issue 6), 44-45.

Corbin, J. J. (1999). A study of factors influencing the growth of nonprofits in social services.

Nonprofit and Voluntary Sector Quarterly, 28, 296-314.

Comfort, L. K. (1994). Self-organization in complex systems.Journal of Public Administration

Research and Theory, 4(3), 383-410.

Comfort, L. K. (2007). Crisis management in hindsight: Cognition, communication,

coordination, and control. Public Administration Review, 67(Suppl. to Issue 6), 189-197.

Dicke, L. (2001). Ensuring accountability in human services contracting: Can stewardship theory

fill the bill?American Review of Public Administration, 32(4), 455-470.


33/35


32

Ebrahim, A. (2003). Making sense of accountability: Conceptual perspectives for northern and

southern nonprofits.Nonprofit Management & Leadership, 14(2), 191-212.

Foster, M. K., & Meinhard, A. G. (2002). A regression model explaining predisposition to

collaborate.Nonprofit and Voluntary Sector Quarterly, 31(4), 549-564.

Fung, A. (2006). Varieties of participation in complex governance. Public Administration

Review, 66(Suppl. to Issue 6), 65-75.

Galaskiewicz, J. (1985). Professional networks and the institutionalization of a single mind set.

American Sociological Review, 50(5), 639-658.

Gazley, B. (2008a). Why not partner with local government? Nonprofit managerial perceptions

of collaborative disadvantage.Nonprofit and Voluntary Sector Quarterly, OnlineFirst, no

volume number yet assigned.

Gazley, B. (2008b). Beyond the contract: The scope and nature of informal government

nonprofit partnerships. Public Administration Review, 68(1), 141-154.

Gazley, B., & Brudney, J. L. (2007). The purpose (and perils) of government-nonprofit

partnership.Nonprofit and Voluntary Sector Quarterly, 36(3), 389-415.

Gidron, B., Kramer, R., & Salamon, L. (Eds.). (1992). Government and the third sector:

Emerging relationships in welfare states. San Francisco: Jossey-Bass Publishers.

Graddy, E. A., & Chen, B. (2006). Influences on the size and scope of networks for socialservice delivery.Journal of Public Administration and Theory, 16, 533-552.

Gronbjerg, K., & Paarlberg, L. (2001). Community variations in the size and scope of the

nonprofit sector: Theory and preliminary findings.Nonprofit and Voluntary Sector

Quarterly, 30(4), 684-706.

Guo, C., & Acar, M. (2005). Understanding collaboration among nonprofit organizations:

Combining resource dependency, institutional, and network perspectives.Nonprofit and

Voluntary Sector Quarterly, 34(3), 340-361.

Jang, H. S., & Feiock, R. (2007). Public versus private funding of nonprofitorganizations. Public Performance and Management Review , 31(2), 174-190.

Jamal, T., & Getz, D. (1995). Collaboration theory and community tourism planning.Annals of

Tourism Research, 22(1), 186-204.


34/35


33

Jang, H. S., & Feiock, R. C. (2007). Public versus private funding of nonprofit organizations:

Implications for collaboration. Public Performance & Management Review, 31(2), 174-

190.

McGuire, M. (2006). Collaborative public management: Assessing what we know and how we

know it. Public Administration Review, Special Issue, 33-43.

Milne, G. R., Iyer, E. S., & Gooding-Williams, S. (1996). Environmental organization alliance

relationships within and across nonprofit, business, and government sectors.Journal of

Public Policy & Marketing, 15(2), 203-215.

OFlynn, J. (2009). The cult of collaboration in public policy. The Australian Journal of Public

Administration, 68(1), 112-116.

OToole, L. J. Jr. (1997). Treating networks seriously: Practical and research-based agendas in

public administration. Public Administration Review, 57(1), 45-52.

Peck, L. R. (2008). Do antipoverty nonprofits locate where people need them? Evidence from a

spatial analysis of Phoenix.Nonprofit and Voluntary Sector Quarterly, 37(1), 138-151.

Pfeffer, J., & Salancik, G. R. (1978). The external control of organizations: A resource

dependence perspective. New York: Harper & Row.

Provan, K. G., & Milward, H. B. (1995). A preliminary theory of network effectiveness: A

comparative study of four community mental health systems.Administrative Science

Quarterly, 40(1), 1-33.

Robinson, S.. E. (2006). A decade of treating networks seriously. Policy Studies Journal, 34(4),

589-598.

Robinson, S. E., Berrett, B., & Stone, K. (2006). The development of collaboration of response

to Hurricane Katrina in the Dallas area. Public Works Management and Policy, 10(4),

325-327.

Robinson, S. E., & Gettis, B. (2007). Seeing past parallel play: Survey measures of

collaboration in disaster situations. (Bush School Working Paper #391). Retrieved

February 17, 2010, fromhttp://bush.tamu.edu/research/workingpapers/

Salamon, L. M. (2002). The tools of government: A guide to the new governance . New York:

Oxford University Press.

Singer, M. I., & Yankey, J. A. (1991). Organizational metamorphosis: A study of eighteen

nonprofit mergers, acquisitions, and consolidations.Nonprofit Management and

Leadership, 1(4), 357-369.
http://bush.tamu.edu/research/workingpapers/http://bush.tamu.edu/research/workingpapers/http://bush.tamu.edu/research/workingpapers/http://bush.tamu.edu/research/workingpapers/


35/35


Simo, G., & Bies, A. L. (2007). The role of nonprofits in disaster response: An expanded model

of cross-sector collaboration. Public Administration Review, 67(Suppl. to Issue 6), 125-

142.

Sowa, J. E. (2008). Implementing interagency collaborations: Exploring variation in

collaborative ventures in human service organizations.Administration & Society, 40(3),298-323.

Smith, S.R. & Lipsky, M. 1993.Non-profits for hire: the welfare state in the age of contracting.

Cambridge, MA: Harvard University Press.

Thompson, J. D. 2003[1967]. Organizations in Action: Social Sciences Bases of Administrative

Theory. Edison, NJ: Transaction Press.

Van Slyke, D.M. (2003). The mythology of privatization in contracting for social services.

Public Administration Review, 63(3), 296-315.

Van Slyke, D. M. (2006). Agents or stewards: Using theory to understand the government-

nonprofit social service contracting relationship.Journal of Public Administration

Research and Theory, 17(2), 157-187.

Waugh, W. L. Jr., & Streib, G. (2006). Collaboration and leadership for effective emergency

management. Public Administration Review, Special Issue, 131-140.

Williamson, O. E. (1975).Markets and hierarchies: Analysis and antitrust implications. New

York: Free Press.

Williamson, O. E. (1985). The economic institutions of capitalism. New York: Free Press.

Williamson, O. E. (1991). Comparative economic organizations: The analysis of discrete

structural alternatives.Administrative Science Quarterly, 36(2), 269-296.

Wolch, J. R., & Geiger, R. K. (1983). The distribution of urban voluntary resources: An

exploratory analysis.Environment and Planning, 15, 1067-1082.

Wolpert, J. (1993). Decentralization and equity in public and nonprofit sectors. Nonprofit and

Voluntary Sector Quarterly, 22, 281-296.

Word, J. (2006).A structural examination of collaborative relations between nonprofitorganizations in the greater Jacksonville area. Unpublished doctoral dissertation, Florida

State University, Tallahassee.

Documents

Structured to Partner: School District Collaboration with Nonprofit Organizations in Disaster Response