The Publicness of Organizations and Policy Environments on ...

The Publicness of Organizations and Policy Environments on Public Service Outcomes:

A Multi-Level Analysis of Substance Abuse Treatment Services

WORKING DRAFT

Prepared for the Public Management Research Association Conference Syracuse, New York, June 2011

Susan Miller, PhD1 John Glenn School of Public Affairs

The Ohio State University [email protected]

Stephanie Moulton, PhD John Glenn School of Public Affairs

The Ohio State University [email protected]

Abstract: A robust literature evaluates dimensional publicness, i.e., the extent to which political authority constrains and enables all organizations, potentially influencing organizational behavior (e.g. Bozeman 1987). Despite this robust scholarship, there are unanswered questions about the relative impact of the publicness of the policy environment and the publicness of the individual organization on organizational behavior (and specifically, public service outcomes). Does the publicness of the policy environment exert an isomorphic influence on private organizations engaged in public service provision? In this paper, we explore this question, focusing on substance abuse treatment centers. Through hierarchical linear modeling, we integrate organizational-level data on substance abuse treatment centers participating in the National Survey of Substance Abuse Treatment Services (N-SSATS) (2009) with data on the larger substance abuse policy environment at the state level to predict service outcomes. Our findings indicate that publicness at the organizational and state policy levels are both important determinants of public service outcomes. Further, we find evidence that an increase in the publicness of the policy environment may be associated with a change in the public service outcomes of private sector organizations; however such effects are sometimes more public (indicative of isomorphism) and other times less public (indicative of differentiation), depending on the outcome at hand. Introduction

1Authors are listed alphabetically

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One of the classic questions in public administration is whether public organizations

operate differently than their private counterparts (Simon, Smithburg, and Thompson 1956). The

investigation of this question has evolved into a robust literature on the effects of dimensional

publicness, i.e., the extent to which political authority constrains and enables all organizations,

potentially influencing organizational behavior (see Bozeman 1987; 2007; Bozeman and

Bretschneider 1994; Pesch 2008; Moulton 2009). Empirical research in this area has explored

the ways in which publicness affects organizational operations (Rainey 1983; Bretschneider

1990; Chubb and Moe 1988), management practices and perceptions (Rainey, Panday, and

Bozeman 1995; Coursey and Rainey 1990; Lachman 1985; Goldstein and Naor 2005), and even

individual outcomes (Moulton and Bozeman 2010; Heinrich and Fournier 2004).

Despite this robust scholarship, there is still a need to unpack the different sources of

political authority at disparate levels of influence, and to consider their impact on organizational

outcomes (Moulton and Bozeman 2010; Lynn, Heinrich and Hill 2001). First, while scholars

have started to explore the effects of the publicness of the policy environment (see Moulton and

Bozeman 2010), questions about the relative impact of the publicness of the policy environment

and the publicness of the individual organization on organizational outcomes remain. In a

similar vein, scholars have not investigated whether the publicness of the policy environment

exerts an isomorphic influence on private organizations engaged in service provision.

In this paper, we further explore the effects of the publicness by addressing these

questions. We focus on an area of service provision that is notoriously public and private:

substance abuse treatment services (Heinrich and Fournier 2004). Compared with other health

services, substance abuse treatment practices are relatively un-standardized; while there are

trends that shape treatment center activities, there is considerable heterogeneity not only in the

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implementation and scope of services, but also in the state-level polices and standards that guide

implementation (Chriqui et al. 2008). We exploit this organizational- and state-level variation to

consider the influence of publicness at both levels on organizational outcomes. Through

hierarchical linear modeling, we integrate organizational-level data on the private, public, and

nonprofit substance abuse treatment centers participating in the National Survey of Substance

Abuse Treatment Services (N-SSATS) (2009) with data on the larger substance abuse policy

environment at the state and county levels to predict service outcomes. We examine the effects

of traditional indicators of organizational publicness (i.e., ownership, funding, and authority) as

well as original measures of contextual or environmental publicness, including state public

organization density and state funding for substance abuse treatment. We also consider the

extent to which private organizations function like their public counterparts in highly public

policy environments. Given that we are primarily focused on the effects of disparate levels of

publicness on public service outcomes as opposed to the differences between public and private

organizations in general, we focus on “soft” (more difficult to measure) organizational practices,

such as community outreach and provision of free services, that we expect public organizations

to be more likely to adopt, instead of “hard” (easier to measure) practices, such as efficiency

controls, that we might expect to be more prevalent in private organizations.

Our findings indicate that publicness at the organizational and state policy levels are both

important determinants of organizational outcomes, highlighting the value of incorporating

policy environments when evaluating publicness. However, our findings for the way in which

environmental publicness shapes the practices of private organizations are more complex. For

some organizational practices, we find that private organizations without direct public constraints

that exist in largely public environments behave more publicly than private organizations (with

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the same constraints) that operate in less public environments. Though, for other service

outcomes, the publicness of the environment allows the private sector to become more

differentiated from public organizations, creating greater division between the public and private

arenas. These findings highlight the utility of considering discrepant organizational practices,

and have important implications for our understanding of the potential benefits as well as

unintended consequences of increased publicness in the policy environment on private

organization behavior.

Dimensional Publicness

Rather than classifying organizations as public or private based on ownership alone, the

theory of dimensional publicness builds on the constructs of political and economic authority

(e.g. Dahl and Lindblom 1976; Wamsley & Zald 1973), suggesting that all organizations are

more or less public depending on the extent by which they are enabled or constrained by political

authority (Bozeman 1987; 2007). In addition to perhaps offering a more accurate representation

of reality, a primary benefit of such an approach is that it allows for empirical investigation of

the different sources of political authority and their relative impact on political authority, rather

than treating all political authority as monolithic (Moulton 2009). For example, we know that

government funding may have a different effect on organizational behavior than shared public

goals or increased contact with public officials. And, we have learned that similar to ownership,

dimensional publicness be more influential for certain types of organizational behaviors (such as

human resource practices within an organization), than other types of behaviors. Despite this

considerable progress, there is a substantial need to (1)generate more sophisticated expectations

about organizational outcomes- specifically public service outcomes- that may be linked in

varying degrees to organizational publicness; and (2) account for the multi-level structure of

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governance, and the relative influence of publicness operating at disparate levels on public

service outcomes. We build on publicness scholarship in both ways, through a multi-level

analysis of varying public service outcomes provided by substance abuse treatment centers.

Publicness and Public Service Outcomes

Grounded in an open-systems perspective of organizations, a core proposition of

publicness research is that external influences of political authority may have varying influence

on different organizational behaviors and thus outcomes (Wamsley & Zald 1973; Bozeman

1987). That is, while publicness may have little effect on outcomes that are largely tied to the

market (economic authority), influences of publicness may have considerable impact on

organizational practices (and thus outcomes) where there is more economic uncertainty and

considerable discretion. This makes sense from the perspective of organizational theory,

integrating institutional and resource dependence theories. In an analysis of hospital rates of

cesarean child-birth deliveries, Oliver (1990) found that physicians were more likely to turn to

“institutional norms” and personal discretion under conditions where insurance companies and

external authority was vague regarding the appropriateness of a cesarean delivery. Similarly,

Moulton (2009) proposes that we might expect influences of publicness, particularly those that

are associative or cultural, to be more influential for outcomes lacking clear economic or

regulatory authority.

Thus, rather than treating publicness like a black box that may be associated with variation

in organizational behavior, there is a strong theoretical rationale to link influences of publicness

to the specific outcomes being observed (Moulton 2009). For example, recent research on public

values has challenged researchers to intentionally integrated considerations of public value

outcomes and “normative” publicness when analyzing the publicness of organizations and

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expected impact on outcomes (Antonsen and Jorgensen 1997; Bozeman 2007; Pesch 2008).

Thus, rather than limiting an analysis of outcomes to those that are predominately economic in

nature, such as efficiency or cost-effectiveness, it is important to consider certain “processes” as

outcomes that may be indicative of public service values, like access, quality, and affordability

of services.

For substance abuse treatment organizations, previous research has found significant

differences between public and private organizations and public outcomes. Friedman,

Alexander, and D’Aunno (1999) found that public substance abuse treatment units provided

more nonessential but beneficial services (e.g., primary care and mental health services), which

should translate to other ancillary services, such as community outreach and aid with social

services. Quality management practices have also been linked to organizational publicness

(Goldstein and Naor 2005). Moreover, there is substantial evidence that public organizations are

more likely to provide free services and greater access to care and serve underprivileged

populations, while, alternatively, private organizations generate more revenue from private

insurance and client fees (Edlund, Wheeler, and D’Aunno 1990; Fadel and Wheeler 1991;

Wheeler et al. 1992; Rafferty 1990; Rodgers and Barnett 2000).

In addition to ownership, research by Heinrich and Fournier (2004) considered the funding

to substance abuse treatment centers (and other organizational level variables) and client level

outcomes, such as recidivism and employment. They did not find significant differences in

client-level outcomes based on organizational level indicators of publicness (ownership and

funding). However, we hypothesize that publicness may be more important for indicators of

organizational processes and service affordability.

Multi-level Publicness

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Research in public affairs increasingly acknowledges the multi-level nature of governance

structures; that is, outcomes intended by public policies may be the result of individual,

organizational and environmental characteristics that operate at multiple levels.

Methodologically, a multi-level model, such as a hierarchical linear (or non-linear) model, is

often more appropriate to account for the nested structure of the data (Lynn et al. 2001; Heinrich

and Lynn 2002). Theoretically, intentionally incorporating multiple levels of influence can allow

for the exploration of indirect influences from the environmental context on (individual)

outcomes, as distinct from direct influences from individual (or organizational) characteristics.

Recent research by Moulton and Bozeman (2010) extends this line of reasoning to

dimensional publicness. While previous research on dimensional publicness measures

publicness at the organizational level of analysis, there are strong rationales for extending

measures of publicness to the policy environment. In particular, while an organization may be

more public to the extent that is directly constrained or enabled by political authority, it may also

be more (or less) public because of the policy environment in which it is situated, which may

have an indirect-yet still important- influence on organizational behavior (particularly when there

is considerable uncertainty or discretion at the organizational level). According to institutional

theory, there may be isomorphic pressures for organizations to adopt similar practices, regardless

of whether or not they directly are influenced by public funds or regulations (Meyer and Rowan

1977; DiMaggio and Powell 1983; D’Aunno et al. 1991; Moulton 2009). Further, to the extent

that publicness changes the competitive playing field in the environment, organizations may

adopt similar or distinct practices strategically to preserve or differentiate their market niche

(Greenwood and Hinings 1996; D’Aunno et al. 2000).

With regard to substance abuse treatment organizations, there is a strong rationale to

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consider the indirect influence of the policy environment on organizational behavior, and in

particular, the publicness of the policy environment. Substance abuse treatment organizations

have considerable discretion with regards to the treatment practices that they adopt due to

considerable uncertainty regarding best practices, and lack of a centralized regulatory

environment to prescribe specific practices (Chriqui et al. 2008; D’Aunno et al. 1991; Heinrich

and Lynn 2002). Therefore, isomorphic or competitive influences from the policy environment

may play a substantial role in the behaviors adopted by organizations. While previous theoretical

and empirical research highlights the importance of multi-level influences for substance abuse

outcomes (Heinrich and Lynn 2002; Etheridge and Hubbard 2000), no known previous research

explicitly models the policy environment level of analysis.

Recent research by Chriqui et al. (2008) demonstrates the importance of moving beyond

ownership and funding to consider state level policy variables, such as accreditation or licensure

requirements, on variation in practices adopted by substance abuse treatment organizations.

While Chriqui et al. (2008) do find evidence of variation in treatment center outcomes due to the

state policy environment, they do not adopt a multi-level modeling approach to isolate the

relative influence of different levels of analysis or potential interactions between levels.

Importantly, we hypothesize there may be an interaction between ownership (public, private or

nonprofit) and the publicness of the policy environment. That is, privately owned substance

abuse treatment organizations operating in a policy environment that is more public (i.e., more

publicly owned and publicly funded treatment abuse organizations), may be more likely to adopt

practices that are similar to those of public substance abuse treatment organizations (due to

isomorphic pressures). However, this influence is likely strongest for outcomes related to

organizational processes (where strategic decoupling may be more likely) than for outcomes that

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are directly tied to their bottom line (such as cost of services).

Data and Methods

Substance abuse treatment organizations in the U.S. provide an appropriate policy area in

which to evaluate multi-level influences of publicness for several reasons. First, substance abuse

treatment has long been the purview of both the private and public sectors (see Price 1997). In

2009, 11.4 of all substance abuse treatment programs were government owned, 29.3 were private

for-profits, and 57.9 were private not-for-profits.2 Additionally, 61.8 percent of treatment

centers received government funding, including 23.1 percent of private for-profit programs and

76.2 percent of the private non-profits. Given the diversity in the level of publicness across

substance abuse treatment centers, this is a promising area in which to explore the effect of

dimensional publicness and the publicness of the policy environment.

Second, as mentioned above, there is substantial variation in treatment practices across

organizations and states (Chriqui et al. 2008), which is important for testing our theoretical

expectations. One reason for this is the decentralized nature of substance abuse treatment policy.

In 1970, states were required to create state agencies for drug and alcohol abuse to manage

formula grants from the federal government and develop standards for the delivery of treatment

services (SAMHSA 2005). With the exception of opioid treatment programs, state governments

are the primarily authority for substance abuse treatment centers in the U.S. This has resulted in

great diversity not only in state substance abuse policies but also in practices across

organizations because it has allowed individual organizations a lot of flexibility and the ability to

address local concerns and respond to their local environment.

Third, there are practical considerations. Recently, concerns have been expressed

2 The other 1.4 percent of substance abuse treatment programs are owned by tribal governments.

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regarding the quality of care in substance abuse treatment programs (see McGlynn et al. 2003).

Quality substance abuse treatment not only includes practices such as comprehensive

intake/assessment (American Psychiatric Association 1994; Mee-Lee, Shulman, Fishman,

Gastfriend, and Griffith 2005) but also services such as transitional assistance (Lo, MacGovern,

and Bradford 2002; McCarty 2000) and aftercare services (Brown, Seraganian, Tremblay, and

Annis 2002; Siegal, Li, and Rapp 2002). Thus, by focusing on a variety of substance abuse

treatment practices, we can further our understanding of the organization- and state-level factors

that contribute to different service outcomes.

We utilize the 2009 National Survey of Substance Abuse Treatment Services (N-

SSATS), which is administered by the Substance Abuse and Mental Health Services

Administration. This survey includes information on the characteristics of all of the substance

abuse treatment centers across the entire U.S. Programs that did not report substance abuse as

their primary or combined focus (e.g., combine substance abuse treatment mental health care) as

well as tribally and federally owned programs (e.g., VA hospitals) were excluded. Further,

centers that reported no substance abuse treatment admissions within the most recent 12 month

period or did not provide outpatient services were also excluded (Chriqui et al. 2008).

Dependent Variables

The dependent variables for this analysis are indicators of public service outcomes at the

organizational level. We investigate six dichotomous dependent variables: four related to

organizational processes, and two related to affordability of services. In terms of processes, we

consider two variables that deal with ensuring access to health care and other types of social

services. These are whether a substance abuse treatment organization 1) conducts community

outreach for persons who might need substance abuse treatment (Outreach) and 2) assists

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substance abuse clients obtain social services, e.g.: Medicaid, WIC, SSI, SSDI, etc. (Refer Social

Services). We also examine two process variables that capture aspects of a facility’s operating

procedures: whether or not the treatment organization 3) conducts case reviews by an appointed

quality review committee (Quality) and 4) follows-up with a client after discharge (Follow-up).

Finally, we consider two variables measuring the organization’s commitment to affordability of

services. These variables are whether the treatment organization: 5) offers free treatment and 6)

uses a sliding fee scale. Table 1 provides the frequency distributions for these dependent

variables.

[Insert Table 1 Here]

Key Independent Variables

Given our focus on dimensional publicness at the organizational and state levels, we

include variables that capture ownership, funding, and political authority at the different levels.

At the organizational level, we include traditional measures of organizational publicness, with

descriptive statistics provided on Table 1. We include ownership (private, nonprofit or

government), with government-owned as the reference category. As indicated on Table 1, 54%

of the organizations in our sample are nonprofit, 37% are private, and about 9% are government

owned. We also consider a few different measures of public funding3. First, we include a

variable capturing whether an organization receives government funding, not including

Medicare, Medicaid or other public health insurance payments. Additionally, we include

measures capturing whether an organization accepts public health insurance. We have separate

indicators for whether a center accepts Medicare, Medicaid, or other state-sponsored health

insurance, such as adultBasic in Pennsylvania or Family Health Plus in New York.

3Unfortunately, the N-SSATS dataset does not indicate the amount of funding from various sources, but rather includes a dichotomous variable for whether or not the organization receives or accepts funding from different sources.

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Finally, we include variables that capture the extent to which organizations are exposed to

additional public pressures through licensing/accreditation. We focus on two types of

licensing/accreditation – state government accreditation and non-governmental accreditation.

For state government licensing/accreditation, we consider separate measures of whether a

substance abuse treatment unit is licensed/accredited by the state substance abuse agency, the

state mental health department, and the state department of health. These are not mutually

exclusive certifications; in fact, 16 percent of treatment centers are licensed/accredited by all

three. We also include non-governmental accreditation because these accreditation organizations

represent another form of external influence in the substance abuse treatment center (see

Heinrich and Fournier 2004). We consider separate indicators of whether a treatment unit was

accredited/certified by the Joint Commission on Accreditation of Health Care Organizations

(JCAHO) or Commission on Accreditation of Rehabilitation Facilities (CARF).

In addition to these traditional indicators of organizational publicness, we also include a

number of measures capturing the publicness of the policy environment, at the state level.

Descriptive statistics for state level variables are provided on Table 2. First, for every measure

of organizational-level publicness, we create a state-level measure of the proportion of substance

abuse treatment centers that fall into that category. For example, one measure is the proportion

of substance abuse treatment facilities in the state that are government-owned. These measures

capture public organization density. We also include other measures that capture the general

ubiquity of public authority affecting substance abuse treatment in the state, such as the state

spending per capita on substance abuse treatment and prevention (Office of National Drug

Control Policy 2006) and the number of state health agency bureaucrats, which include substance

abuse treatment officials (Census 2006), per 1,000 state citizens. Finally, we also look at

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whether the state has its own accreditation standards or if it accepts accreditation through

national accrediting organizations in lieu of accreditation by the state (SAMHSA 2005).


In addition to the direct effect of publicness (at the organization or policy environment

levels), we also hypothesize an interaction between private organizations and the publicness of

the policy environment, where private substance abuse treatment organizations might act more

similarly to public treatment organizations when they exist in highly public environments for

certain processes (outcomes). To test this interaction, we include indicators of the publicness of

the policy environment on the slope coefficient at level one for private organizations (slope as

outcome model), and in particular include the proportion of organizations in the state that are

government owned or nonprofit owned, as well as the proportion that receive public funding.

While we expect the direct relationship between private treatment centers and the process

dependent variables to be negative, we expect the interactions to be positive, suggesting an

isomorphic effect of environmental publicness.

Control Variables

We include a number of control variables that are suggested by the literature. At the

organizational level (Table 1), we control the types of clients served, including the intensity of

clients served (i.e., total number of intensive outpatient clients/total number of outpatient

clients), the percentage of clients with a co-diagnosis of mental health issue, the percentage with

both alcohol and drug abuse, and the percentage of outpatient clients served who are under the

age of 18. We also include whether the treatment center is affiliated with a religious

organization, whether its focus is mixed (e.g., focuses on both mental health and substance abuse

treatment) as opposed to focusing on substance abuse treatment solely, and its size (the log of the

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number of clients served in a specified period). In addition, we control for types of operations,

including whether it is a solo practice, whether the facility operates a halfway house or other

transitional housing at this location, whether or not the facility offers residential treatment, and

whether it is located in a hospital. Finally, because the survey has three different response modes

(i.e., phone, internet, and mail), we include a dichotomous variable for phone responses and

another for mail responses.

We also include characteristics of the location of the substance abuse treatment center at

the organization level (Table 1). First, we include a dummy variable indicating whether or not

the treatment center is located within a metropolitan statistical area (MSA). We also include

county characteristics. Of particular importance, we include of the percentage of county

residents who have recently used drugs. We also control for the percentage of county residents

that have less than a high school education, the percentage of unemployed county residents, and

the percentage of residents who are on Medicaid. Additionally, we control for the size of the

county’s population (logged). County data are taken from the 2009 Community Health Status

Indicators, which are compiled by the U.S. Department of Health and Human Services.

At the state level (Table 2), we include control variables for the state political climate, as

well as controls for the substance abuse environment in the state. The state’s political climate is

measured by whether or not the state has the initiative process (direct democracy), gubernatorial

voter turnout, whether or not the state has a Democratic governor, and citizen ideology measured

by the ranking of state policies from most liberal to least liberal (Berry et al. 1998). We control

for the state substance abuse environment by including the number of substance abuse centers in

the state (logged) and the percent of the population with a substance abuse treatment problem.

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Methodology

This analysis employs multi-level modeling, specifically hierarchical generalized linear

modeling (HGLM), to take advantage of the nested nature of the data and the binary outcomes

(see Raudenbush and Bryk, 2002, for a detailed discussion of HGLM). The multi-level

modeling framework is specifically designed to evaluate the influence of group level variation on

individual outcomes (Heinrich & Lynn 2001). Because substance abuse treatment organizations

are nested within states with distinct policy environments, there is reason to believe that the

organization observations within a particular state are not truly independent of one another.

Proceeding to estimate using traditional logistic regression techniques violates the independence

between units assumption, and thus may result in inefficient or even biased estimates, depending

on the severity of the between group differences.

Further, because the binary dependent variables take on values of 0 or 1 according to

probabilities given by the Bernoulli distribution, hierarchical generalized linear models (HGLM)

are preferred. The logit function is a convenient way to link the prediction equation to the binary

outcomes at level one. Models for level two take the same form as in a traditional hierarchical

linear model. The structural equations are:

Level-1 Model: ηij = β0j + β1jO1j + …+ βnjOnj

Level-2 Model: β0j = γ00 + γ01ST1j +…+ γ0kSTkj + u0j

β1j = γ10 + u1j, …, βnj = γn0 + un

The outcome, ηij is the log of the odds of an organization (i) in a particular state (j)

adopting a particular practice (outreach, refer to social service agencies, quality reviews, follow-

up, free services, or sliding scale fees for services), expressed through the logit link function as:

ηij = log

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where is the probability of observing a particular practice.

In the level one model, O1j through Onj are (n) explanatory variables that measure

organization characteristics. Unique random intercepts and random slopes are allowed for each

organization across states (j). In the level-two model, the intercept for organization

characteristics (β0j) is expressed as a linear function of (k) state level predictors (ST1j to STkj) and

the residual (u0j) represents the random state level effects.

Findings

We estimate six multi-level models representing each of six the dependent variables, four

measuring organizational processes (Table 3), and two measuring affordability of services (Table

4). First, in terms of organizational processes (Table 3), we find a significant, positive

relationship between an organization’s receipt of government funding and the adoption of three

of the four public service processes (Outreach, Refer to Social Services, and Quality Review).

Whether or not the organization accepts public (state funded) health insurance is also positively

associated with all four processes. Interestingly, ownership alone is not consistently predictive of

the adoption of public service practices; privately owned organizations are significantly less

likely to refer clients to social service agencies, but are not significantly less likely than

government or nonprofit owned organizations to adopt other practices.

In terms of organization-level affiliations, being licensed by any agency (substance

abuse, mental health or public health) is significantly associated with the adoption of quality

review practices, and those organizations licensed by public health agencies are also more likely

to conduct outreach and conduct follow-up with clients. Control variables for the focus and size

of the organization, as well as the location of the organization, are also significantly associated

with adoption of certain practices. For example, those organizations also operating a halfway

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house or providing residential treatment services are more likely to adopt many of the practices,

whereas organizations located within a hospital are less likely to adopt most of the practices.

Larger organizations (as measured by whether or not they are a solo practice, and the number of

clients) are more likely to conduct outreach and engage in quality review.


At the state policy level, there are a few significant relationships between publicness

indicators and organizational practices, though less so than the measures at the organization

level. For example, an increase in the proportion of treatment organizations that receive

government funding is associated with an increase in referrals to social service agencies;

however, an increase in the proportion of treatment organizations that are government owned is

associated with a decrease in referrals to social service agencies, but a significant increase in

outreach. The more interesting finding is the interaction between the indicators of publicness at

the state policy level and private ownership at the organization level. In particular, while

privately owned organizations are significantly less likely to refer to social service agencies,

there is an interaction with the proportion of agencies in the state that are government or

nonprofit owned, where privately owned organizations located in policy environments with an

increase in the proportion of government or nonprofit owned organizations are more likely to

refer to social services. Similarly, private organizations located in state policy environments

with an increased proportion of government owned organizations are significantly more likely to

adopt follow-up practices. Thus, there is limited evidence of potential isomorphic publicness.

Aside from the adoption of public organizational processes, we also model public service

outcomes that indicate affordability of services (Table 4). At the organization level, we find

strong, significant relationships between indicators of publicness and the provision of affordable

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services, where organizations receiving government funds (or accepting Medicaid) are

significantly more likely to provide free or sliding fee services, and privately owned

organizations are significantly less likely to provide such affordable services. This makes sense,

as government funding may be necessary to offset the costs of providing free or reduced fee

services. Interestingly, both JCAHO and CARF accreditations are associated with reduced

probability of providing free or reduced fee services, and organizations licensed by substance

abuse or mental health agencies are more likely to offer reduced fee services. Religious

organizations are more likely to offer free services.


At the state level, some of the publicness indicators are significantly associated with free

or reduced fee services. For example, an increase in the proportion of organizations that receive

government funding is associated with an increased probability of providing free services. State

level- accreditation of substance abuse treatment centers is also associated with an increased

probability of an organization providing free services. Again, the most interesting finding is the

interaction between privately owned organizations and the publicness at the state policy level.

Unlike the adoption of processes, an increase in the publicness of the policy environment, as

measured by an increase in the proportion of government owned treatment centers, is associated

with an even greater decrease in the probability of a privately owned organization providing free

services. Thus, rather than being indicative of isomorphic behavior, this may indicate greater

differentiation; that is, when private organizations can depend on public organizations to “pick

up the slack” in terms of providing free services, they may be less likely to offer such services.

[Preliminary] Conclusions

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Our findings indicate that publicness at the organizational and state policy levels are both

important determinants of organizational outcomes. In general, organizational publicness tied to

funding is more predictive of public service processes than ownership, supporting the rationale

for a dimensional publicness approach. For affordability of services, ownership also seems to be

important, where private organizations are significantly less likely to provide free or reduced fee

services than public organizations.

However, there are interesting interactions between the proportion of publicly owned

substance abuse treatment organizations in the state (an indicator of the publicness of the policy

environment), and the behavior of private organizations. For organizational outcomes tied to

processes, specifically referrals to social service agencies and follow-up with clients after

service, we find that private organizations operating in more public environments are

significantly more likely to adopt these practices than private organizations operating in a less

public context. This may be indicative of mimetic isomorphism, where private organizations are

more likely to adopt the practices of public organizations when there are simply more of them in

their environment.

On the other hand, for organizational outcomes tied to affordability, specifically the

provision of some free services and having a sliding fee scale, the publicness of the policy

environment interacts with private organizations in a much different way. In general, privately

owned substance abuse treatment organizations are significantly less likely to offer affordable

services than their publicly owned counterparts. However, an increase in the publicness of the

policy environment is associated with an even greater decrease in the probability that a privately

owned organization will offer free or reduced fee services. Thus, rather than adopting practices

of their publicly owned counterparts, private organizations in more public environments seem to

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differentiate themselves even more, and are even less likely to provide affordable services. It

may be that the public organizations “pick up the slack”, allowing the privately owned

organizations to focus on those clients with the ability to pay. When publicness is lower in the

policy environment (i.e., there are fewer public substance abuse treatment organizations in the

state), private organizations may be more likely to pick up the service gap for those unable to

pay.

Our preliminary analysis offers two important contributions to the research on

dimensional publicness. First, it highlights the importance of incorporating a multi-level

approach to publicness, allowing for interactions between the publicness of the policy

environment and organizational ownership. Second, it calls attention to the potential for both

isomorphic and strategic responses to increases in the publicness of the policy environment,

depending on the outcome at hand. For outcomes that have more uncertainty, such as processes

for providing substance abuse treatment services, an increase in publicness in the policy

environment may lead to isomorphic adoption of more public practices, even by privately owned

organizations. However, for outcomes that have economic authority, such as fees charged for

services, an increase in publicness in the policy environment may lead to less public outcomes

(in this case, lower probability of free or reduced fee services).

References

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Table 1: Descriptive Characteristics, Organizational Level Variables

Mean Stand. Dev Min Max

Outcome Variables

Outreach 53.9% 0 1

Refer Social Services 48.0% 0 1

Quality Review 69.9% 0 1

Follow‐Up 66.9% 0 1

Some Free 48.7% 0 1

Pay Scale 67.3% 0 1

Organizational Publicness

Government Funding 58.6% 0 1

Ownership: Nonprofit 54.2% 0 1

Ownership: Private 37.0% 0 1

Ownership: Government 8.8% 0 1

Medicare 31.7% 0 1

Medicaid 56.6% 0 1

Public Health Insurance 39.3% 0 1

Organizational Affiliations

JCAHO Accreditation 17.7% 0 1

CARF Accreditation 22.7% 0 1

Licensed: Substance Abuse 87.2% 0 1

Licensed: Mental Health 33.6% 0 1

Licensed: Public Health 40.0% 0 1

Religious Organization 5.0% 0 1

Organizational Focus & Size

Mixed Focus 35.0% 0 1

% Co‐diagnosis 36.04 29.29 0 100

% Alcohol & Drug 49.77 30.05 0 100

% Intense Services 19.7% 0 1

% Youth 11.4% 0 1

Halfway House 5.2% 0 1

Residential Treatment 10.4% 0 1

Hospital 9.2% 0 1

# Clients (log) 5.04 1.15 0 9.22

Solo Practice 8.3% 0 1

Phone Survey 12.1% 0 1

Mail Survey 29.7% 0 1

Organizational Location

MSA 76.6% 0 1

% No High School (County) 0.127 0.049 0.019 0.653

% Unemployed (County) 0.030 0.008 0.012 0.103

% Drug Abuse (County) 0.070 0.011 0.041 0.122

% Medicaid (County) 0.197 0.093 0.006 0.598

County Population (log) 12.685 1.673 7.728 16.104

(N=7,767)

25

Table 2: Descriptive Characteristics, State Level Variables

Mean Stand. Dev Min Max

State Level Publicness

% Government Funding 62.9% 0.157 22.7% 92.0%

% Government Owned 9.5% 0.114 0.0% 53.3%

% Nonprofit Owned 57.1% 0.191 18.2% 90.7%

% Accept Medicaire 35.7% 0.172 8.8% 76.8%

% Accept Medicaid 61.7% 0.225 17.0% 100.0%

% Accept Public Insurance 45.9% 0.191 11.1% 88.4%

Substance Abuse $ per capita 15.727 9.627 5.601 53.929

Health Bureuacrats 0.811 0.526 0.145 2.594

State Accredication 48.0% 0.505 0.0% 100.0%

State Level Affiliations

% JCAHO Accreditation 16.2% 0.102 2.6% 45.5%

% CARF Accreditation 24.9% 0.181 2.6% 75.0%

% License SA 84.9% 0.121 49.5% 100.0%

% License Public Health 43.6% 0.222 11.9% 96.1%

% License Mental Health 42.8% 0.226 3.6% 87.0%

State Political Climate

Direct Democracy 48.0% 50.5% 0.0% 100.0%

Voter Turnout 44.292 9.933 26.000 63.700

Democratic Governor 56.0% 0 1

Liberal Policies 25.500 14.577 1 50

State Control Variables

# SA Organizations, log 4.833 0.916 3.091 7.007

% Population with SA 9.367 1.007 7.540 11.480

N=50

26

Table 3: Multilevel Logit Models, Predicting Treatment Process

Organizational Level Variables

β e^β β e^β β e^β β e^β

Intercept ‐0.211 0.81 ‐0.847 ** 0.43 ‐0.363 0.70 0.030 1.03


Government Funding 0.303 ** 1.35 0.544 ** 1.72 0.197 * 1.22 ‐0.094 0.91

Ownership: Nonprofit ‐0.008 0.99 ‐0.348 ^ 0.71 0.330 * 1.39 0.174 1.19

Ownership: Private 0.114 1.12 ‐0.822 ** 0.44 ‐0.097 0.91 0.188 1.21

Medicare 0.115 1.12 0.142 1.15 0.086 1.09 ‐0.018 0.98

Medicaid 0.328 ** 1.39 0.667 ** 1.95 0.072 1.07 ‐0.091 0.91

Public Health Insurance 0.245 ** 1.28 0.215 * 1.24 0.269 ** 1.31 0.339 ** 1.40


JCAHO ‐0.141 0.87 0.122 1.13 0.221 1.25 ‐0.259 ^ 0.77

CARF 0.253 ^ 1.29 0.494 ** 1.64 0.956 ** 2.60 1.666 ** 5.29

Licensed: Substance Abuse 0.115 1.12 0.091 1.10 0.542 ** 1.72 0.304 * 1.36

Licensed: Mental Health 0.105 1.11 0.238 ** 1.27 0.350 ** 1.42 ‐0.011 0.99

Licensed: Public Health 0.291 ** 1.34 0.122 1.13 0.336 ** 1.40 0.485 ** 1.62

Religious Organization 0.404 * 1.50 0.257 1.29 0.021 1.02 ‐0.021 0.98


Mixed Focus ‐0.005 0.99 0.060 1.06 ‐0.055 0.95 ‐0.272 ** 0.76

% Co‐diagnosis 0.002 ^ 1.00 0.013 ** 1.01 0.003 * 1.00 0.001 1.00

% Alcohol & Drug 0.001 1.00 ‐0.002 1.00 ‐0.002 1.00 ‐0.002 ^ 1.00

% Intense Services 0.173 1.19 ‐0.029 0.97 0.378 * 1.46 0.573 ** 1.77

% Youth 0.263 1.30 ‐0.540 ** 0.58 0.202 1.22 0.525 ** 1.69

Halfway House 0.355 * 1.43 0.743 * 2.10 ‐0.113 0.89 0.557 ** 1.75

Residential Treatment 0.080 1.08 0.604 ** 1.83 0.309 * 1.36 0.724 ** 2.06

Hospital ‐0.779 ** 0.46 ‐0.376 ^ 0.69 ‐0.482 ** 0.62 ‐0.403 * 0.67

# Clients (log) 0.069 * 1.07 0.035 1.04 0.076 * 1.08 ‐0.060 0.94

Solo Practice ‐0.355 ** 0.70 ‐0.094 0.91 ‐0.591 ** 0.55 ‐0.193 0.82

Phone Survey 0.747 ** 2.11 0.143 1.15 0.427 ** 1.53 0.432 ** 1.54

Mail Survey ‐0.706 ** 0.49 ‐0.627 ** 0.53 ‐0.289 ** 0.75 ‐0.168 * 0.85


MSA ‐0.339 ** 0.71 0.224 * 1.25 ‐0.015 0.99 ‐0.039 0.96

% No High School 2.235 * 9.35 ‐1.415 0.24 0.668 1.95 0.052 1.05

% Unemployed ##### ** 0.00 7.256 #### 6.192 ##### ‐10.752 ^ 0.00

% Drug Abuse 6.316 #### 4.666 #### ‐1.696 0.18 ‐7.592 0.00

% Medicaid 1.672 ** 5.32 3.061 ** 21.35 0.188 1.21 1.656 ** 5.24

Population (log) ‐0.003 1.00 0.019 1.02 0.041 1.04 ‐0.011 0.99

^p<.10; *p<.05; **p<.01

Outreach Refer SS Quality Follow‐Up

27

Table 3 (cont.): Multilevel Logit Models, Predicting Treatment Process

State Level Variables

State Level Publicness β e^β β e^β β e^β β e^β

% Government Funding ‐0.686 0.50 2.380 * 10.81 ‐1.318 0.27 ‐1.288 0.28

% Government Owned 2.173 * 8.79 ‐3.069 * 0.05 1.142 3.13 0.344 1.41

% Nonprofit Owned 1.007 2.74 ‐2.016 * 0.13 0.745 2.11 3.266 * 26.20

% Accept Medicaire ‐0.410 0.66 0.604 1.83 0.354 1.43 ‐1.542 0.21

% Accept Medicaid ‐1.288 ** 0.28 0.459 1.58 ‐0.238 0.79 0.443 1.56

% Accept Public Insurance 0.983 ^ 2.67 1.122 * 3.07 0.178 1.19 ‐2.299 * 0.10

Substance Abuse $ per capita 0.003 1.00 ‐0.004 1.00 0.015 1.02 ‐0.023 0.98

State Accredication 0.329 * 1.39 ‐0.050 0.95 ‐0.151 0.86 0.015 1.02

Health Bureuacrats ‐0.084 0.92 0.394 * 1.48 0.173 1.19 0.200 1.22


% JCAHO Accreditation 0.267 1.31 ‐2.033 * 0.13 ‐1.551 0.21 ‐1.998 0.14

% CARF Accreditation 0.374 1.45 0.089 1.09 0.255 1.29 0.211 1.24

% License SA 0.234 1.26 ‐0.226 0.80 0.405 1.50 2.781 * 16.13

% License Public Health ‐0.410 0.66 0.014 1.01 ‐0.423 0.66 0.711 2.04

% License Mental Health ‐0.155 0.86 ‐0.572 0.56 ‐0.450 0.64 1.755 * 5.78


Direct Democracy 0.222 1.25 ‐0.176 0.84 0.009 1.01 ‐0.278 0.76

Voter Turnout ‐0.005 1.00 ‐0.021 * 0.98 0.001 1.00 ‐0.010 0.99

Democratic Governor 0.092 1.10 ‐0.089 0.92 ‐0.052 0.95 ‐0.712 ** 0.49

Liberal Policies 0.006 1.01 ‐0.002 1.00 ‐0.004 1.00 ‐0.010 0.99


# SA Organizations, log ‐0.033 0.97 0.068 1.07 ‐0.161 0.85 ‐0.012 0.99

% Population with SA ‐0.051 0.95 ‐0.021 0.98 ‐0.169 * 0.84 0.135 1.14

Slope Interaction Variables

Private 0.114 1.12 ‐0.822 ** 0.44 ‐0.097 0.91 0.188 1.21

Private* ST_NPP ‐0.018 0.98 1.812 ^ 6.12 0.225 1.25 1.315 3.72

Private*St_GOVP ‐0.360 0.70 1.913 ^ 6.77 0.998 2.71 2.281 * 9.79

Private*ST_GFNDP, 1.151 3.16 ‐1.984 * 0.14 ‐0.007 0.99 ‐0.483 0.62

Private*ACRD_ST, 0.024 1.02 0.176 1.19 0.073 1.08 0.285 ^ 1.33

^p<.10; *p<.05; **p<.01

Outreach Refer SS Quality Follow‐Up

28

Table 4: Multilevel Logit Models, Predicting Treatment Affordability

Organizational Level Variables

β e^β β e^β

Intercept ‐0.212 0.809 ‐0.063 0.939


Government Funding 1.163 ** 3.200 1.239 ** 3.452

Ownership: Nonprofit ‐0.376 * 0.686 0.149 1.160

Ownership: Private ‐1.537 ** 0.215 ‐0.484 * 0.616

Medicare 0.098 1.102 0.299 * 1.348

Medicaid 0.414 ** 1.513 0.878 ** 2.405

Public Health Insurance 0.052 1.053 0.573 ** 1.774


JCAHO ‐0.667 ** 0.513 ‐0.685 ** 0.504

CARF ‐0.419 ** 0.658 ‐0.900 ** 0.407

Licensed: Substance Abuse ‐0.062 0.940 0.261 * 1.298

Licensed: Mental Health 0.180 ^ 1.198 0.249 * 1.282

Licensed: Public Health 0.067 1.069 ‐0.017 0.983

Religious Organization 0.544 ** 1.723 ‐0.046 0.955


Mixed Focus ‐0.136 0.873 0.248 * 1.282

% Co‐diagnosis ‐0.001 0.999 ‐0.005 ** 0.995

% Alcohol & Drug 0.008 ** 1.008 0.004 ** 1.004

% Intense Services ‐0.110 0.896 ‐0.067 0.935

% Youth 0.164 1.178 ‐0.546 ** 0.579

Halfway House 0.241 1.273 ‐0.067 0.935

Residential Treatment 0.272 ^ 1.312 ‐0.437 ** 0.646

Hospital ‐0.213 0.808 ‐1.264 ** 0.282

# Clients (log) 0.086 * 1.090 0.041 1.042

Solo Practice 0.015 1.015 0.094 1.099

Phone Survey 0.512 ** 1.668 0.247 ^ 1.280

Mail Survey 0.187 ^ 1.205 ‐0.073 0.930


MSA 0.044 1.045 ‐0.598 ** 0.550

% No High School 2.107 * 8.226 1.053 2.866

% Unemployed ‐14.444 * 0.000 ‐3.399 0.033

% Drug Abuse 1.488 4.430 10.897 ######

% Medicaid 1.023 ^ 2.782 ‐0.049 0.952

Population (log) 0.029 1.029 0.112 ** 1.119

^p<.10; *p<.05; **p<.01

Some Free PayScale

29

Table 4 (cont.): Multilevel Logit Models, Predicting Treatment Affordability

State Level Variables

State Level Publicness β e^β β e^β

% Government Funding 2.373 * 10.734 0.026 1.027

% Government Owned ‐0.099 0.905 1.542 4.676

% Nonprofit Owned ‐1.108 0.330 0.365 1.441

% Accept Medicaire 1.604 * 4.974 ‐0.825 0.438

% Accept Medicaid ‐1.226 * 0.293 1.884 ** 6.582

% Accept Public Insurance ‐0.328 0.720 ‐2.649 ** 0.071

Substance Abuse $ per capita 0.019 ^ 1.019 0.007 1.007

State Accredication 0.391 * 1.479 0.164 1.178

Health Bureuacrats 0.287 1.332 0.387 ^ 1.473


% JCAHO Accreditation ‐0.808 0.446 0.784 2.189

% CARF Accreditation 1.092 * 2.982 ‐0.990 ^ 0.372

% License SA 0.847 2.333 2.558 * 12.916

% License Public Health ‐0.306 0.736 0.789 ^ 2.201

% License Mental Health ‐0.451 0.637 1.182 * 3.260


Direct Democracy ‐0.198 0.820 0.313 ^ 1.367

Voter Turnout ‐0.007 0.993 ‐0.030 ** 0.971

Democratic Governor 0.288 ^ 1.334 ‐0.107 0.899

Liberal Policies ‐0.001 0.999 ‐0.011 0.989


# SA Organizations, log 0.023 1.023 0.138 1.148

% Population with SA 0.034 1.035 ‐0.079 0.924

Slope Interaction Variables

Private ‐1.537 ** 0.215 ‐0.484 * 0.616

Private* ST_NPP ‐0.136 0.873 ‐0.591 0.554

Private*St_GOVP ‐2.453 * 0.086 ‐2.394 ^ 0.091

Private*ST_GFNDP, ‐1.237 0.290 ‐0.210 0.811

Private*ACRD_ST, ‐0.139 0.870 ‐0.008 0.992

^p<.10; *p<.05; **p<.01

Some Free PayScale

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