Regulatory Effectiveness and Social Equity in Environmental Governance: Assessing Goal Conflict,Trade-Off,and Synergy

Abstract

Public organizations function in an environment of goal multiplicity and constantly juggle goal trade-off and synergy. However, little empirical research explores how the potential conflict between effectiveness and equity affects government agencies’ decision making. This study examines the extent to which public agencies are committed to regulatory effectiveness and social equity in environmental policy management, and the circumstances under which administrative agencies engage in goal trade-off and synergy. Analyzing data on the Clean Air Act in New York State, this study finds that although regulatory effectiveness is salient to government’s policy implementation, equity-oriented policy is likely to give rise to trade-off in this goal domain. The state agency manages environmental programs in an equitable way, and policy intervention has inconsistent effects on the equity goal achievement. The agency does, in some instances, synergize two goals in loci reflecting the convergence of task demands, but equity-oriented policy does not reinforce such behavioral pattern. Findings beg the question regarding how public policies and programs can be devised in ways that help avert goal trade-off and engender the maximum level of social outcomes.

Keywords

goal conflict trade-off synergy effectiveness equity environmental policy

Introduction

Public organizations function in an environment of value plurality and goal multiplicity (Meier & Bohte, 2007; Rainey, 2014; Rosenbloom, 1983). Multiple values and goals may or may not lead to conflicts. In the instances of goal conflicts, public organizations constantly juggle goal trade-off and synergy (Chackerian & Mavima, 2001; Slocum, Cron, & Brown, 2002; Wenger, O’Toole, & Meier, 2008). Effectiveness and equity are two public values and policy goals that govern different criteria of public administration accountability (Bailey, 2010; Denhardt & Catlaw, 2014; Le Grand, 1990). Centering on problem solving and means–ends relationships, effectiveness is one of the predominant values in public organizations (Frederickson, 1971; Rutgers & van der Meer, 2010; Shafritz, Russell, & Borick, 2012). In the meantime, over the years, government agencies have played a critical role in fulfilling principles of fairness, justice, and equity, particularly pertaining to vulnerable populations because of their demographic and socioeconomic status (Frederickson, 1971, 2010; Guy & McCandless, 2012; Johnson & Svara, 2014; Riccucci, 2009; Wooldridge & Gooden, 2009).

However, gaps remain between normative claims and empirical evaluation regarding the implications of incorporating social equity into public administration (Gooden, 2015; Pitts, 2011; van der Wal, de Graaf, & Lawton, 2011). Relatedly, albeit a rich scholarship on goals of effectiveness and equity and their respective ramifications for government agencies’ decision making, “much remains unclear, such as how often governing good conflicts with governing well and what trade-offs in values the conflicts lead to” (de Graaf & van der Wal, 2010, p. 628). Environmental governance well illustrates the important role of regulatory effectiveness and social equity (Durant, Fiorino, & O’Leary, 2004; Gooden, 2014; Guy & McCandless, 2012; Johnson & Svara, 2014; Rosenbaum, 2014). Although the potential tension has been broadly identified related to the simultaneous achievement of these two goals in environmental policy management, few studies explore how such a conflict affects government’s decision making.

This study examines two research questions:

Research Question 1: What is the extent to which government is committed to regulatory effectiveness and social equity?

Research Question 2: What are the circumstances under which administrative agencies engage in goal trade-off and synergy?

In the setting of the implementation of the Clean Air Act (CAA) in New York State, this study analyzes data at the block-group level in the 2000s and finds that achieving regulatory effectiveness is a predominant determinant of government’s policy implementation practices. However, the introduction of equity-oriented policy is associated with reductions in compliance inspections and punitive actions for areas with task demands for regulatory effectiveness, implying a goal trade-off in the agency’s decision making. The state agency produces administrative outputs in an equitable way to all communities, regardless of their demographic and socioeconomic characteristics. Equity-oriented policy has no impact on the agency’s inspections for communities of social justice concern, but it is related to more punitive actions against compliance violators in these areas. The agency synergizes two goals by allocating more inspection efforts to locales with a high level of task demands for both regulatory effectiveness and social equity. Nevertheless, the newly adopted policy does not reinforce such behavioral pattern.

The article continues with a section reviewing the theory of organizational goal, with focuses on goal conflict, trade-off, and synergy. It next discusses goals of regulatory effectiveness and social equity in environmental governance and their latent conflict. The next section describes data, measures, and methods. This is followed by the “Results” section. The next section discusses the implications of findings and the study’s limitations. The article concludes with a section on future research agenda.

Organizational Goals: Conflict, Trade-Off, and Synergy

Public organizations are characterized by multiplicity, ambiguity, distinctiveness, intangibility, and incompatibility of values and goals (Bryson, Crosby, & Bloomberg, 2014; de Graaf & Paanakker, 2015; Jørgensen & Bozeman, 2007; Rainey, 2014). Goal setting influences an organization’s decision making, with respect to action direction, persistence, and effort allocation (Miles, 2012). Although goal conflicts are not always the case in complex policy environments, it is not uncommon for public organizations to strike uneasy balances to achieve multiple goals that are often incompatible and in competition with each other (Oldenhof, Postma, & Putters, 2014). Goal conflict in organizational management primarily arises from two circumstances (Slocum et al., 2002). First, some policy implementation settings require organizations to produce multiple policy outcomes through a single task. These “formally established” objectives are, in many instances, “logically contradictory or in clear deviation from each other” (Wenger et al., 2008, p. 179). A second circumstance relates to efforts to simultaneously accomplish multiple independent, “apparently straightforward” tasks that serve different goals. This is referred to as a “multidimensional value-maximization problem” (Wenger et al., 2008, p. 179). Here, challenges occur among horizontal, lower order, intermediate goals that jointly contribute to higher order, ultimate, collective purposes (Resh & Pitts, 2013; Simon, 1947/1997). In this scenario, “established production functions” for policy outcomes are “relatively invariant in the short run” (Wenger et al., 2008, p. 180). Existing production functions during a given planning period constrain organizations’ adaptive capacities, and inadequate infrastructure, personnel, financial, and time resources make administrative agencies’ adaptation more difficult.

Facing multiple, sometimes conflicting, policy goals, public organizations constantly involve in decision making that is either “a zero-sum or positive-sum game” (Wenger et al., 2008, p. 181). Goal conflict can easily entail trade-offs among various goals and opportunity costs for alternative priorities (Simon, 1947/1997; Wenger et al., 2008). In the short term, organizational actors may adapt pragmatically to goal conflict by “emphasizing certain existing goal sets while ignoring others” (Maynard-Moody & McClintock, 1987, p. 133). Goal trade-offs occur if public organizations choose to commit to more salient goals that contribute to the attainment of larger policy benefits in the hierarchy of ends (e.g., higher order goals, ultimate organizational and social outcomes; Resh & Pitts, 2013). Goal trade-offs may also occur when a higher authority “assigns a performance goal for a new task or deliverable in addition to one’s regular tasks” (Slocum et al., 2002, p. 78). The implication is that on one hand, administrative agencies may reduce efforts for accomplishing the existing goal, as a result of attention diverted to the new goal. On the other hand, it is equally plausible that the existing goal is so salient that organizational actors do not alter their decision-making pattern. As such, this article expects that policy or organizational mandates that are oriented toward a new goal may or may not affect public organizations’ achievement of the established goal.

Despite the broadly observed goal conflicts and the resultant trade-offs in the process of policy implementation, goal synergy is possible. Chackerian and Mavima (2001) suggested that goal interaction is contingent on different situational contexts, combinations of scale of resources, and similarities of resources for policy implementation. Overlapping production functions or joint production processes for multiple outputs can prevent undesired zero-sum trade-offs among goals (Wenger et al., 2008). Studying public education, Resh and Pitts (2013) showed that multiple goals can be accomplished in harmony if “achieving more than one policy goal simultaneously can make it more likely that the agency achieves benefits above and beyond its proximate expectations” (p. 132, emphasis in original). Daley and Layton (2004) noted that administrative convenience primarily accounts for the U.S. Environmental Protection Agency (EPA)’s decision making in remedying Superfund sites: The vigorousness in goal pursuit and effort investment is negatively associated with the costs of goal attainment. Congruent production processes of different tasks should lower the costs of policy implementation and generate more administrative convenience. As such, this article expects that multiple goals can be synergized in loci of joint production processes. Specifically, government agencies allocate more implementation efforts for areas of goal convergence. Also, policy or organizational mandates that are oriented toward a new goal will augment government agencies’ implementation efforts toward areas of goal convergence.

Achieving Effectiveness and Equity in Environmental Governance

Environmental policy management well illustrates an issue context in which government agencies devise strategies to accomplish different goals that are greater or lesser incompatible with one another (Durant et al., 2004; Rosenbaum, 2014). Over the years, government agencies have been expected to achieve both regulatory effectiveness and social equity in administering environmental programs. Nevertheless, recurring tensions have marked the balance between these two goals, as each one has exerted a distinct influence on the logic, processes, and practices of policy implementation and role expectations of public administrators (Collins & Gerber, 2008; de Graaf & van der Wal, 2010; Winn & Taylor-Grover, 2010).

Regulatory Effectiveness

In public management, effectiveness is conceptualized as regards “discharging the administrative and operational functions pursuant to [an agency’s] mission . . . and the institutional mandate” (Rainey & Steinbauer, 1999, p. 13). Emphasizing the connection between input, output, and outcome, effectiveness is concerned about “the extent to which an organization accomplishes some predetermined goal or objective” (Shafritz et al., 2012, p. 51). In environmental governance regime, the fundamental mission of administrative agencies charged with implementing environmental regulations is to ensure that “national efforts to reduce environmental risk are based on the best available scientific information” and “federal laws protecting human health and the environment are enforced fairly and effectively” (EPA, 2016).¹ The CAA, the analytical focus of this study, serves as an apt example. To accomplish the ultimate goals of the national ambient air pollution control law, the EPA, over time, has directed its regional offices and delegated states to prioritize implementation efforts toward “the most important and environmentally significant violations of major and synthetic minor stationary sources of air pollution” within their jurisdictions (EPA, 2009, p. 1), and to “focus appropriate and adequate enforcement and compliance activities on those violations identified by this policy” (EPA, 1998, p. 3). As a result, it is expected that local policy implementers will prioritize and allocate more regulatory resources against violators, so as to bring these facilities into full compliance. By doing so, government agencies can effectively achieve the goal of enhancing environmental quality and public health. In this sense, the EPA and its subnational regulatory counterparts are typical representatives of “professional technocrats,” who are “an outgrowth of public administration’s ties to scientific management and efficiency, effectiveness, and economy” (Winn & Taylor-Grover, 2010, p. 148).

Social Equity

In the past decades, public administration scholars have called for social equity to be meaningfully integrated into administrative values and goal system, given that the currently predominant values are effectiveness, efficiency, and economy (Frederickson, 1971, 2010; Guy & McCandless, 2012; Johnson & Svara, 2014; Riccucci, 2009). As Denhardt and Catlaw (2014) succinctly described, equity “involves a sense of fairness or justice—specially, the correction of existing imbalances in the distribution of social and political values. In contrast to equal treatment for all, equity proposes that benefits be greater for those most disadvantaged” (p. 123). In the environmental domain, recent empirical evidence points to the particular susceptibility of people of color and economically disadvantaged groups to environmental hazards and inequitable policy implementation by government agencies (Konisky, 2009a; Konisky & Schario, 2010; Lester, Allen, & Hill, 2001; Liang, 2016b; Lynch, Stretesky, & Burns, 2004; Malley, Scroggins, & Bohun, 2012; Mennis, 2005; Mohai, Pellow, & Roberts, 2009; Mohai & Saha, 2015; Ringquist, 2005; Spina, 2015). As a result, environmental justice issues are emerging on the public policy making and program administration agenda. Since President Clinton issued Executive Order 12898 in 1994, both federal and state governments have been increasingly committed to advancing environmental justice that ensure “fair treatment and meaningful involvement of all people regardless of race, color, national origin, or income with respect to the development, implementation, and enforcement of environmental laws, regulations, and policies” (EPA, 2004, p. 2). Meanwhile, governments at multiple levels have devised a variety of equity-conscious institutional arrangements, ranging from symbolic to substantive policy intervention, to promote environmental justice (Abel, Salazar, & Robert, 2015; Bonorris, 2010; Kim & Verweij, 2016; Konisky, 2015). As Paehlke (2013) suggested, one of the most pressing normative concerns and ethical challenges in environmental policy is how to ensure all individuals have equal environmental rights. Against this backdrop, the theoretical exploration and intellectual discourse on environmental justice and inequity have continued to be highly relevant and consequential, not only for environmental studies but also for research on American politics and public policy.

Potential Conflict Between Regulatory Effectiveness and Social Equity

Although programmatic effectiveness and social equity are integral components of organizational values and goals in the public sector, potential conflict exists. While effectiveness is part of competence (productivity) standards and reflects programmatic, instrumental, and operational values, equity is a responsiveness standard and reflects ethical and moral values (Meier & Bohte, 2007; Van Wart, 1998). As researchers observed, social equity has not been readily incorporated into program administration, which has primarily been driven by effectiveness and efficiency (Collins & Gerber, 2008; Levin, 1998; Moser & Rubenstein, 2002). “The importance of governing with moral public values like transparency, equity, and honesty is clear. Yet it is also clear that acting on moral values does not always produce the required policy outcomes” (de Graaf & van der Wal, 2010, p. 623). Government’s legitimacy and credibility are, in large measure, determined by its competence, which is usually gauged in terms of effective output production “pursuant to the mission or the institutional mandates” (Shafritz et al., 2012, p. 336; Rainey & Steinbauer, 1999, p. 13). A clash between the traditional and new roles of public administrators easily leads to the productivity–equity conflict in program management (Sowa & Selden, 2003).

Latent conflicts between programmatic effectiveness and social equity are observed in environmental policy. Compared with social equity, regulatory effectiveness, which is a central component of institutional mission, plays a predominant role in government agencies’ goal-directed behavior. As discussed earlier, in the post–Executive Order 12898 era, people of color and low-income communities remain, to a varying extent, more likely to experience environmental policy implementation inequalities. Also, at both federal and state levels, environmental policy design that explicitly integrates the consideration of social justice has limited efficacy (Dull & Wernstedt, 2010; Konisky, 2009b, 2015; Liang, 2016a; Ong, 2012). Plausibly, the implementation of policies aimed at advancing environmental equity is complicated by public agencies’ uneasy balancing of the established, statutory mandates (i.e., effectively enforcing regulations) and the emergent, programmatic requirements (i.e., promoting social justice). As environmental governance is jointly defined by regulatory and redistributive politics, this bidimensional issue context conveys ambiguous and conflicting signals to policy implementers, further influencing their decision making (Liang, 2016b). To fulfill goal of regulatory effectiveness, administrative agents are expected to enforce laws in a way that is scientifically informed and target the sources of risks (e.g., violation of regulatory compliance). To achieve goal of social equity, administrative agents are expected to adopt distributional equity approaches to redressing access gaps related to governmental services and protective benefits and target socially marginalized and economically disadvantaged populations (e.g., racial/ethnic minorities, poverty-stricken neighborhoods; Johnson & Svara, 2014).

However, loci of target efforts for regulatory effectives and social equity do not necessarily converge. Over the years, both the Government Accountability Office (GAO) and the EPA’s Office of Inspector General (OIG) have been criticizing the EPA for inaction on defining environmental justice vulnerable community in terms of target population and geographic scale. The Agency is, however, concerned that strict and inflexible definitions would limit the ability of its regional offices and delegated states to assist communities actually overburdened by pollution and to effectively carry out the legal and mandated responsibilities (EPA, 2004; GAO, 2011). In a similar vein, observers noted that “Merely conveying the percentage of minority residents in a particular community does not tell us how many citizens are actually exposed to environmental threats” (Kamieniecki & Kraft, 2013, p. 11). Assessing the EPA’s awarding of small environmental justice grants, Abel and Stephan (2008) suggested that the Agency’s decision making largely reflects managerial rationalism, which stresses a “gospel of efficiency” (p. 154). Furthermore, additional efforts on regulatory enforcement related to environmental justice often encounter scrutiny and resistance from elected officials and regulated industries, which advocate for deregulation and reducing compliance costs (Harrison, 2016). Albeit the potential conflicts between regulatory effectiveness and social equity, few studies have systematically examined the extent to which policy implementers are committed to these two goals, as well as the circumstances under which public agencies engage in trade-off and synergy. The following sections evaluate these questions.

Data, Measures, and Methods

The empirical analysis is conducted in the setting of the implementation of the CAA in New York State, where an equity-oriented policy has been incorporated into the government’s environmental policy management since March 2003. Under the Commissioner Policy 29: Environmental Justice and Permitting (CP-29), based on 2000 U.S. Census,² the New York State Department of Environmental Conservation (DEC) designates potential environmental justice areas (PEJAs). A PEJA is “a census block group, or contiguous area with multiple census block groups” with either “a low-income population (i.e., having an annual income that is less than the poverty threshold) equal to or greater than 23.59% of the total population” or “a minority population (i.e., Hispanic, African American or Black, Asian and Pacific Islander, or American Indian) equal to or greater than 51.1% in an urban area and 33.8% in a rural area of the total population.”³ CP-29 highlights two essential procedural components: requiring permit applicants to submit the Public Participation Plans and mandating the agency to perform supplemental compliance and enforcement inspections of regulated facilities.

This study draws neighborhood demographic and socioeconomic data at the block-group level from 2000 U.S. Census and 2005-2009 American Community Survey (ACS) 5-year estimates, which provide the “point” and “period” estimates, respectively. Information on the state’s environmental justice areas is gleaned from the PEJAs data set maintained by the New York State geographic information system (GIS) Clearinghouse database. Environmental and geographic data on the regulated stationary sources covered by the CAA are collected from the EPA’s Air Facility System (AFS) of the Integrated Data for Enforcement Analysis (IDEA) and Facility Registry System (FRS). Facility information is aggregated to the block-group level for analysis. The present study only considers block groups that host at least one federally reportable stationary source, as these communities have a nonzero probability of receiving compliance evaluation or punitive actions for noncompliance. With 4,172 observations in the final sample, there are 2,086 block groups for each time period, representing 13.83% of the total 15,079 block groups within the state.

Dependent Variables

Government’s goal-directed decision making can be conceptualized as a demand–supply relationship in terms of task demands and administrative outputs. Public agencies produce administrative outputs to meet task demands that correspond to different goals. The dependent variables are the state agency’s outputs of environmental policy implementation: inspections and punitive actions, which are measured by the aggregated number of compliance evaluations (partial and full) and administrative enforcement actions (informal and formal), respectively.⁴ Policy implementers conduct compliance monitoring activities to detect potential problems, and perform assurance activities to solve known problems. Policy implementation practices for the pre- and postpolicy periods are the aggregated number in 2000 and the 5-year average (rounded for analysis) from 2005 through 2009, respectively.

Independent Variables

Regulatory effectiveness

Mitigating environmental harm and risk through regulatory activities is one of the most salient goals to administrative agencies. To represent risk-related task demands that require governmental responses to accomplish goal of regulatory effectiveness, the analysis uses the number of facilities that are categorized as high priority violators, alleged violators of a permit requirement, or both. To be consistent with the dependent variables, this variable is the aggregated number in 2000 and the 2005-2009 average (rounded for analysis) for the pre- and postpolicy period, respectively.⁵

Social equity

To achieve goal of social justice, the state agency needs to allocate implementation efforts to target areas with equity-related task demands. The analysis measures such areas in three ways. The focal indicator is a dichotomous variable representing whether a block group is a PEJA. Such a neighborhood is coded as 1 for both the pre- and postpolicy period, if the state policy designates it as a PEJA, and otherwise as 0. It should be noted that the officially designated PEJAs represent the most vulnerable neighborhoods in terms of race/ethnicity and socioeconomic class. However, the implementation efforts of public agencies may be proportional to equity-oriented task demands in a given community. As such, the analysis employs two additional, continuous measures of community characteristics: the percentage of minority populations (i.e., race or ethnicity other than non-Hispanic White alone) and the percentage of residents living below poverty level in a community. These two measures are broadly used in current environmental justice studies to indicate focal neighborhood demographic composition.

Locus of goal convergence

Areas with task demands that are characterized by joint or overlapping production processes of different goals are likely to engender goal synergy or a positive-sum result. In this study, these areas should simultaneously reflect task demands for regulatory effectiveness and social equity. The analysis operationalizes locus of goal convergence by coding block groups that are PEJAs with at least one facility designated as a high priority violator or an alleged violator as 1,⁶ and otherwise as 0.

Equity-oriented policy intervention

The analysis codes the adoption of policy aimed at promoting social justice in the agency’s program management (i.e., CP-29) as 1 for observations in the postpolicy period (after 2003) and as 0 for those in the prepolicy period (prior to 2003).

Control Variables

The analysis considers several factors pertinent to an agency’s policy implementation practices in the environmental domain. First, residents’ characteristics are, in many instances, relevant to government’s provision of environmental protective outputs. Research on class-based environmental inequities suggests a positive relationship between residents’ economic conditions and government’s activeness in implementing environmental policy. In this respect, median household income (in 2009 inflation-adjusted dollars, in logarithmic form) is employed. Well-educated residents are a vital socioeconomic factor influencing neighborhoods’ political awareness and policy mobilization in environmental justice (Konisky, 2009a; Konisky & Reenock, 2013; Konisky & Schario, 2010; Mennis, 2005). Thus, the percentage of residents who have attained higher education (i.e., bachelor’s degree or higher) is included. Limited English skills negatively influence residents’ capacity to participate in the community decision making and in political processes (Goodkind & Foster-Fishman, 2002; Parkin & Zlotnick, 2011). This study examines the effects of language proficiency using two variables: the percentage of populations who are nonnative speakers of English but speak English well or very well, and the percentage of linguistically isolated households. Homeowners are important stakeholders in environmental governance, for example, in the “Not-In-My-Backyard” (NIMBY) movement. Homeowners are generally more active in participating in the community development process as well as in political and social activities to protect the asset investment and sustain the property values (Dietz & Haurin, 2003; Engelhardt, Eriksen, Gale, & Mills, 2010; McCabe, 2013). A higher level of homeownership should generate more pressure on state agencies to monitor polluters’ compliance statuses and to enforce regulations in a more rigorous manner. The analysis controls for the percentage of owner-occupied housing units.

In addition, the general task context shapes administrative agencies’ environmental program management strategies. Government agencies are more likely to perform rigorous regulatory practices in localities with a large number of air pollutant dischargers, especially pollution-intensive plants. Accordingly, the quantities and types of regulated entities are measured by the number of federally reportable stationary source dischargers of air pollutants, and the number of facilities that are classified as manufacturing, energy extraction (i.e., mining, quarrying, and oil and gas extraction), construction, or utility industries. Environmental protection through command-and-control regulations often is framed as a trade-off for economic growth. Administrative agencies may also attempt to boost states’ economic competitiveness by relaxing regulatory enforcement (the “race to the bottom” phenomena; Konisky, 2008; Woods, 2006, but see also Konisky, 2009c; Potoski, 2001). This study uses the percentage of manufacturing employment to estimate the possible effects of such opportunistic behavior. Nonattainment areas for the criteria pollutants under the CAA, which are documented in the EPA’s Green Book, may also be part of the regulatory task environment. Specifically, a county that is partially or completely classified as a nonattainment area in a given year is coded as 1; otherwise it is coded as 0. In the context of environmental federalism and partial preemption, the EPA maintains oversight authority to ensure that delegated states fulfill federal requirements. Thus, states are expected to be responsive to federal policy signals (Earnhart, 2004). To estimate federal and state interactions, this research uses a lagged variable of the EPA inspection. As the agency’s inspection stringency may be positively associated with its enforcement activeness, the punitive action model also considers the number of inspections. Population (in logarithmic form), population density (person per square mile, in logarithmic form), and a dichotomous measure of urban area are also included.

Finally, temporal and spatial dependence between observations may influence the behavior pattern of government agencies. The dependent variable lagged by 1 year is used to control for the temporal correlation of implementation activities as well as the “program inertia” patterns (Wood, 1992, p. 46). Facilities subject to higher levels of policy implementation activities in the previous year should receive more follow-up attention to ensure that the previously detected problems are solved and that full compliance is achieved. Moreover, policy implementation practices for different neighborhoods may hinge on their geographical proximity due to the strategic allocation of administrative resources. The analysis thus includes a spatially lagged dependent variable. Specifically, the spatial lag, which is “a weighted average of observations on the variable over neighboring units” (Drukker, Peng, Prucha, & Raciborski, 2013, p. 243), is computed based on a normalized-contiguity matrix, in which “contiguous units are assigned weights of 1, and noncontiguous units are assigned weights of 0” (p. 248).

Models

To examine the circumstances under which the state agency engages in goal trade-off and synergy, the study specifies the analytical model as follows:

\begin{array}{l} I_{i t} (or E_{i t}) = β_{0} + β_{1} R_{i t} + β_{2} S_{i t} + β_{3} C_{i t} + β_{4} P_{t} + β_{5} (P_{t} \times R_{i t}) \\ + β_{6} (P_{t} \times S_{i t}) + β_{7} (P_{t} \times C_{i t}) + β_{8} X_{i t} + ε_{i t}, \end{array}

where I_it is inspection for a block group i at time period t, E_it is enforcement, R_it is task demand for regulatory effectiveness (environmental risk mitigation), S_it is a vector of variables of task demand for social equity (environmental justice), C_it is the convergent locus of two goals, P_t is policy intervention, X_it is a vector of control variables, and ε_it is a stochastic disturbance term. The coefficients of three sets of interaction terms, that is, (P_t × R_it), (P_t × S_it), and (P_t × C_it), are of primary substantive interest.

The outcome variables (i.e., inspection, punitive action) are counts of events. To analyze event count data, the Poisson regression model (PRM) is often analytically inappropriate as overdispersion (i.e., the variance of the counts is larger than the mean) holds in most empirical cases. For this reason, the negative binomial regression model (NBRM) is an alternative analytical method. In certain instances, however, a sample may have a number of zeros⁷ that “often exceeds the number predicted by either the Poisson or the negative binomial regression model” (Long, 1997, p. 218). More important, distinct processes or mechanisms may underlie the absence of regulatory activity (i.e., zeros for the outcome variables)⁸ in block groups where federally reportable stationary source dischargers are sited (Long & Freese, 2014). When evaluating the goodness of fit, for both the dependent variables, the likelihood ratio (LR) tests show that the NBRM is preferable to the PRM. The Vuong tests compare the zero-inflated negative binomial (ZINB) regression model and the NBRM, indicating that the former best fits the data (Long & Freese, 2014). Another set of LR tests that compares the ZINB and the zero-inflated Poisson (ZIP) regression model suggests that the former is statistically better to estimate the number of inspections, and the latter is favored to estimate the number of punitive actions. As a result, the analysis employs the ZINB and ZIP regression model to estimate the agency’s inspections and punitive actions, respectively. The estimating model specifies all independent variables as the inflation variables and reports robust standard errors.

Results

Table 1 summarizes the descriptive statistics for the dependent and independent variables. The variance inflation factor (VIF) tests indicate that multicollinearity is not a concern in the analysis.⁹ State-designated PEJAs account for 39.65% of the sample. Approximately, 17.69% of block groups have at least one facility classified as a high priority violator or an alleged violator. Communities characterized as loci of goal convergence account for 7.19% of the sample. Tables 2 and 3 present the findings of the count model on inspections and punitive actions, respectively (results of the associated logit model reported in the appendix). Models 1 and 3 report the estimates (incidence rate ratios) without including the variable of policy intervention, and Models 2 and 4 incorporate equity-oriented policy and the related interaction terms.

Table 1.

Descriptive Statistics (N = 4,172).

Variable	M	SD	Minimum	Maximum
Dependent variable
Inspection	1.163	3.473	0	104
Punitive action	0.115	0.515	0	15
Independent variable
Regulatory effectives (risk mitigation)
Compliance violator	0.231	0.562	0	6
Social equity (environmental justice)
PEJA	0.396	0.489	0	1
Minority population (%)	35.065	34.657	0	100
Residents below poverty level (%)	14.851	15.187	0	100
Locus of goal convergence
PEJA with compliance violator	0.072	0.258	0	1
Equity-oriented policy intervention
Policy intervention	0.500	0.500	0	1
Control variable
Median household income (log)	10.340	2.072	0	12.429
Resident higher education attainment (%)	28.823	23.690	0	100
Resident English proficiency (%)	19.178	17.402	0	100
Household linguistic isolation (%)	6.904	11.004	0	100
Homeownership (%)	49.773	31.220	0	100
Regulated facility	1.462	1.064	1	18
Pollution-intensive industrial facilities	0.663	0.907	0	9
Manufacturing employment (%)	9.027	8.593	0	100
Nonattainment area	0.760	0.427	0	1
Lagged EPA inspection (t − 1)	0.018	0.156	0	3
Population (log)	6.946	1.309	0	10.105
Population density (log)	8.015	2.788	0	12.918
Urban area	0.781	0.413	0	1
Lagged inspection (t − 1)	1.158	3.393	0	104
Lagged punitive action (t − 1)	0.100	0.487	0	14
Spatial lag inspection	0.056	0.152	0	3.367
Spatial lag punitive action	0.006	0.018	0	0.306

Note. PEJA = potential environmental justice area; EPA = Environmental Protection Agency.

Table 2.

Estimates (IRR) of Predictors for Inspections: Zero-Inflated Negative Binomial Regression Model (N = 4,172).

	Model 1		Model 2
	IRR	SE	IRR	SE
Compliance violator	1.036	0.037	1.252***	0.070
PEJA	0.970	0.071	0.995	0.170
Minority population	1.000	0.001	0.996^†	0.002
Residents below poverty level	1.002	0.001	1.000	0.003
PEJA with compliance violator	1.135	0.094	1.368^†	0.231
Policy intervention	—	—	1.896***	0.246
Policy × Compliance violator	—	—	0.790**	0.056
Policy × PEJA	—	—	1.056	0.188
Policy × Minority population	—	—	1.006**	0.002
Policy × Residents below poverty level	—	—	1.001	0.003
Policy × PEJA with compliance violator	—	—	0.760	0.144
Median household income	1.001	0.015	1.005	0.013
Resident higher education attainment	1.000	0.001	1.000	0.001
Resident English proficiency	1.002	0.002	1.001	0.002
Household linguistic isolation	0.999	0.003	0.998	0.002
Homeownership	1.001	0.001	1.000	0.001
Regulated facility	0.938*	0.030	0.953^†	0.027
Pollution-intensive industrial facilities	1.078*	0.037	1.085*	0.036
Manufacturing employment	0.996	0.003	0.997	0.003
Nonattainment area	0.982	0.070	0.951	0.064
Population	1.050*	0.025	1.048*	0.022
Population density	0.927***	0.020	0.928***	0.016
Urban area	1.174^†	0.106	1.201*	0.104
Lagged inspection (t − 1)	1.183***	0.013	1.149***	0.015
Spatial lag inspection	1.201*	0.107	0.403	0.401
Policy × Spatial lag inspection	—	—	2.573	2.565
Constant	1.277	0.214	0.900	0.185
Log pseudolikelihood	−3,688.356		−3,520.164
Wald χ²(df)	401.72(19)***		1,377.89(26)***

Note. Estimated with robust standard errors. IRR = incident rate ratios; PEJA = potential environmental justice area.

†

p < .10. *p < .05. **p < .01. ***p < .001.

Table 3.

Estimates (IRR) of Predictors for Punitive Actions: Zero-Inflated Poisson Regression Model (N = 4,172).

	Model 3		Model 4
	IRR	SE	IRR	SE
Compliance violator	1.431***	0.081	1.593***	0.102
PEJA	1.147	0.272	0.522	0.209
Minority population	1.003	0.003	1.009*	0.004
Residents below poverty level	0.998	0.004	0.991	0.006
PEJA with compliance violator	0.964	0.202	1.588	0.588
Policy intervention	—	—	2.107**	0.481
Policy × Compliance violator	—	—	0.740**	0.072
Policy × PEJA	—	—	2.298^†	1.105
Policy × Minority population	—	—	0.990*	0.004
Policy × Residents below poverty level	—	—	1.011	0.007
Policy × PEJA with compliance violator	—	—	0.567	0.242
Median household income	1.010	0.028	0.982	0.022
Resident higher education attainment	1.000	0.003	1.000	0.003
Resident English proficiency	0.992*	0.004	0.997	0.003
Household linguistic isolation	0.997	0.006	0.998	0.005
Homeownership	1.005*	0.002	1.004*	0.002
Regulated facility	0.987	0.037	0.994	0.026
Pollution-intensive industrial facilities	0.969	0.055	0.972	0.047
Manufacturing employment	0.996	0.008	0.997	0.007
Nonattainment area	1.002	0.166	0.931	0.147
Lagged EPA inspection (t − 1)	0.793	0.170	0.884	0.174
Inspection	1.013**	0.005	1.017***	0.005
Population	0.981	0.047	1.027	0.050
Population density	1.090*	0.042	1.077*	0.039
Urban area	0.826	0.124	0.825	0.119
Lagged punitive action (t − 1)	1.194***	0.029	1.192***	0.038
Spatial lag punitive action	74.726***	82.373	92.597**	133.440
Policy × Spatial lag punitive action	—	—	0.010	0.032
Constant	0.213	0.073	0.172	0.059
Log pseudolikelihood	−852.2655		−796.4597
Wald χ²(df)	415.25(21)***		357.46(28)***

Note. Estimated with robust standard errors. IRR = incident rate ratios; PEJA = potential environmental justice area; EPA = Environmental Protection Agency.

†

p < .10. *p < .05. **p < .01. ***p < .001.

In terms of inspections (Table 2), when the estimates do not take into account the state’s equity-oriented policy, none of the focal independent variables is statistically significant (Model 1). The presence of pollution-intensive establishments is positively associated with the agency’s inspection stringency. Analogous relationships are observed regarding communities’ total populations and urban status. However, there is a negative relationship between two control variables: the total number of regulated facilities and population density, and the level of the agency’s actions. Currently, this study does not have a theoretical rationale for these counterintuitive findings. Both temporal and spatial correlations are statistically significant in the positive direction. It implies that the stringency of compliance monitoring activities in a given community is contingent on the previous amount of administrative outputs as well as on the agency’s policy implementation attention to the surrounding areas.

The model that further considers the state’s policy intervention pertaining to advancing social justice in the environmental realm reveals more dynamics in the agency’s compliance surveillance behavior (Model 2). Several variables of central interest display explanatory strength. In the postpolicy era, the state agency appears to increase its overall regulatory inspections by 89.6% (i.e., $\hat{β}$ of 1.896). With respect to goal of regulatory effectiveness, block groups with more actual or suspected violators of environmental laws have more rigorous compliance evaluations. For every additional unit of a stationary source designated as a high priority violator or an alleged violator of permit requirement, the agency’s compliance surveillance activities for the host neighborhoods increase by a factor of 1.252, or 25.2%, holding other variables constant. However, after the adoption of environmental justice policy, 1-unit increase in actual or suspected regulatory violators in a given block group is related to a 21% decrease in environmental inspections (i.e., $\hat{β}$ of 0.79).

As for goal of social equity, there is no discernible relationship between the community’s PEJA status and the government agency’s policy implementation practices. Moreover, the incorporation of environmental justice policy does not bring about significant change in the agency’s facility compliance evaluation activities for neighborhoods that are potentially susceptible to environmental burdens because of residents’ demographic and economic profile. Notably, variables regarding racial/ethnic minorities are statistically significant, albeit the small size of the coefficient. Block groups with a higher level of minority population have fewer inspections, but the introduction of equity-oriented policy helps rectify this circumstance. Furthermore, block groups that are designated as PEJAs and host at least one regulation violating facility receive more rigorous compliance monitoring outputs (i.e., $\hat{β}$ of 1.368, or 36.8%), compared with those that are not loci of goal convergence. Nevertheless, equity-oriented policy does not affect this pattern, as the interaction term is statistically insignificant. Several control variables are consistent indicators of the state’s inspection practices, and their effects are comparable with Model 1.

The government agency’s punitive actions are different in some aspects (Table 3). In Model 3, which does not include policy intervention, task demand for regulatory effectiveness remains statistically significant. Communities with more violating facilities have more administrative enforcement actions (i.e., $\hat{β}$ of 1.431, or 43.1%). Also, the agency is more likely to have punitive actions against regulatory compliance violations in block groups with a high level of residents with limited English proficiency. Homeownership is positively associated with governmental compliance assurance activities in a given neighborhood. As opposed to the model of inspection, the amount of regulated facilities, type of industrial sector, a neighborhood’s population, and its urban status do not have explanatory strength.

Model 4 additionally considers policy intervention and its interactions with the focal variables. Like inspections, regulatory punitive actions are more stringent in the postpolicy period. With other variables being the same, neighborhoods with a high level of facility compliance violation receive more enforcement actions (i.e., $\hat{β}$ of 1.593, or 59.3%). Nevertheless, the agency relaxes the stringency of compliance assurance activities for these locales after the introduction of equity-oriented policy (i.e., $\hat{β}$ of 0.74, or a decrease by 26%), implying a goal trade-off between regulatory effectiveness and social justice. Figure 1 depicts the relationships between task demands for regulatory effectiveness and the predicted number of the state agency’s policy implementation activities (left panel for inspections and right panel for punitive actions), moderated by equity-oriented policy. It is clear that in the prepolicy period, there is a positive relationship between task demands for risk mitigation and the agency’s administrative outputs. After the adoption of equity-oriented policy, the average effect of risk-related task demands on the agency’s compliance monitoring activities is substantially smaller. Similar moderating effect of the policy is observed in compliance assurance activities, but the net effect of risk mitigation tasks is larger than in inspections.

Figure 1.

Relationship between task demand for regulatory effectiveness and the state agency’s policy implementation activities, moderated by equity-oriented policy appendices.

Akin to inspections, the agency’s decision making on violation punitive actions does not vary across communities composed of predominantly racial/ethnic minorities and/or residents living under the poverty threshold and those that are not. To respond to the newly introduced policy, when other factors are held constant, administrative actors allocate more enforcement efforts by a factor of 2.298 (i.e., 129.9%, at the 0.10 level) to the most vulnerable neighborhoods. Meanwhile, despite statistical significance, the signs of two variables representing a block group’s minority composition are different from those in the inspection model, but the magnitude of both the main (i.e., $\hat{β}$ of 1.009, or an increase of 0.9%) and interactive effect (i.e., $\hat{β}$ of 0.990, or a decrease of 1%) of this variable is negligible.

Neither the main nor interactive effect of goal convergence locus is statistically significant. It means that those neighborhoods, where the most socially marginalized and economically disadvantaged reside and the regulatory violating facilities are located, do not exert influence on the administrative agency’s punitive actions. Relatedly, the adoption of equity-oriented policy does not change the level of the agency’s policy implementation outputs for these areas. Findings on the control variables remain largely unchanged; and it is noteworthy that homeownership is a consistent predictor of the agency’s decision making.

Discussion

This research examines how the state agency addresses task demands for achieving regulatory effectiveness and social equity in the process of environmental policy implementation and the circumstances under which government engages in goal trade-off and synergy in the scenario of goal conflict. As results consistently suggest, regulatory effectiveness, which requires policy implementers to ameliorate impending or existing environmental harms, plays a predominant role in the agency’s decision making. Specifically, the effect size of this variable is substantially larger in punitive actions that solve existent problems than in inspections that detect potential problems. These findings comport with prior studies, substantiating the significant effects of risk-related task environments on public agencies’ policy implementation pattern, independent of the possible effects of equity-related factors (Eckerd & Keeler, 2012; Hird, 1993; Konisky & Reenock, 2013; Konisky & Schario, 2010; Liang, 2016a). Policy intervention aimed at promoting social equity is anticipated to stimulate changes in administrative agency’s behavior. The empirical analysis substantiates the consistent moderating effects of equity-oriented policy and the resultant goal trade-off involving regulatory effectiveness in the major types of policy implementation activities. Redirecting policy implementation efforts appears to be a pragmatic strategy for the government agency to respond to organizational environmental shocks and to a constellation of goals and objectives, at least in the short term.

In terms of the equity goal, overall, the administrative agency manages environmental programs in an equitable way, irrespective of local demographic and socioeconomic composition. Despite not altering the allocation of institutional resources for facility compliance monitoring activities, the policy that reinforces social justice sheds light on the agency’s behavioral response in compliance assurance practices. Government imposes more punitive actions against facility violators in communities composed of predominantly racial/ethnic minorities and/or residents living under the poverty threshold. Taking together the evidence, this study echoes two interrelated implications from previous research. First, given the existing equitable delivery of public services to all social members, equity-oriented policy may have limited efficacy “if there is little or no inequity to amend” (Liang, 2016a, p. 13). But in some instances, the state agency still allocates more resources to target areas in response to the expectations of policy mandates. Meanwhile, adopting such a policy is likely to give rise to trade-offs in other goal domains, as this study finds in regulatory effectiveness. The second implication is that without taking into account the condition of environmental risks in local communities, which directly corresponds to goal of regulatory effectiveness, equity-oriented policy that exclusively considers demographic and socioeconomic dimensions may displace administrative resources and efforts, but meanwhile contributes little to alleviating the actual environmental burdens unevenly faced by people of social justice concern (Liang, 2016a).

Government has, in some instances, been attentive to loci that intersect goals of regulatory effectiveness and social equity, as these areas have overlapping or joint production processes that enable the administrative agency to achieve different goals simultaneously. Related to the core research questions, the analysis suggests that goal trade-off and synergy exists under different circumstances. Specifically, the former is likely to occur in areas with task demands that do not reflect goal convergence (i.e., both risk mitigation and social justice), and the latter is possible in loci of joint production processes, whereby public administrators are capable of reducing environmental risks and enhancing service access to vulnerable populations simultaneously. As such, multiplicity of policy goals does not necessarily lead to inevitable tensions (e.g., trade-off); rather, their dynamics are contingent on contexts that are characterized by different levels of convergence or alignment. Although positive spillover effects in goal accomplishment are possible in program administration, there is no significant change in the agency’s decision making after policy intervention. Facing potential goal conflict, constrained resources for adaptation, and accountability expectations, public administrators can buffer external shocks, enhance organizational performance, and synergize different goals by identifying loci of goal convergence. More important, by targeting locales at the nexus of task demands for both goals, government agencies are able to produce substantive protective benefits to vulnerable communities and, meanwhile, to fulfill their institutional mission and statutory responsibilities. As such, this study begs the question regarding how public policies and programs can be devised in ways that help avert goal trade-off and engender the maximum level of social outcomes. In practice, well-designed information management technologies can facilitate public administrators to make more informed decisions. For instance, GIS helps map and screen areas of interest (Danziger & Andersen, 2002; Haque, 2001; Kellogg & Mathur, 2003). In the long term, policy design needs to comprehensively diagnose the underlying problems and explicitly set goals that better integrate production processes of different tasks.

The present research has several limitations. First, as Chackerian and Mavima (2001) summarized, “[t]ime allows synergistic learning, and reduction of ambiguity and conflict” (p. 353). Given the preset strategic planning and resource allocation, organizations’ adaptation takes time. This study relies on a cross-sectional research design (i.e., 1-year and a 5-year average in the pre- and postpolicy period, respectively), which is likely to mask some important temporal dynamics among the central variables. This caveat certainly requires more in-depth and contextual inquiry, as well as a longer research time frame to reveal the effects of organizational changes on an agency’s decision making. In addition, this study simply analyzes one state, which limits the findings’ external validity. New York State’s policy actions on environmental equity issues have been comparably aggressive (Abel et al., 2015). This implies that the generally favorable political and policy environments may, to some extent, strengthen public agencies’ efforts on advancing social equity. Goal interactions may be different in other states that have a mixed commitment to different goals. Moreover, focusing on block group as the unit of analysis, this study does not consider factors related to multilevel governance structure (e.g., county-, region-, or state-level). Public policy implementation and program management are embedded in multilevel interactions of various institutional actors (Lynn, Heinrich, & Hill, 2000; May & Winter, 2009). Environmental issues are no exception (Bulkeley & Betsill, 2005; Bulkeley & Betsill, 2005; Liang, 2016b; Rabe, 2007). As this study centers on a short-term assessment, some higher level factors (e.g., organizational structure, program design, budget, political and administrative leadership) that are critical to the implementation regime may be time invariant during the research time frame. For a comprehensive evaluation of administrative actors’ behavior, we need to consider these sets of multilevel elements. Finally, although goal trade-off and synergy are among the common responses of policy implementers to multiple and conflicting goals, they are not the only courses of action. Thacher and Rein (2004) identified three other strategies: (a) cycling: “policy actors may focus on each value sequentially, emphasizing one value until the destructive consequences for others become too severe to ignore”; (b) firewalls: “policy actors may establish and sustain multiple institutions committed to different values, walling off each institution from the responsibilities of the others”; and (c) casuistry: “policy actors may eschew general decisions about how conflicting values should be weighed. Instead they encourage and facilitate case-by-case judgment about how decisions should be made, typically using analogical reasoning to do so” (pp. 463-464). Related to the weaknesses noted earlier, the lack of time series analysis and cross-state comparison limits this study in exploring these important topics.

Future Inquiry

Findings on the coexistence of goal trade-off and synergy as well as their contingencies set the stage for future research on at least two crucial questions on goal relations and organizational behavior. First, what is the role of management in averting goal trade-off and fostering the mutual reinforcement of goals? As Meier and O’Toole (2009) noted, “[m]anagers must choose among competing goals” is a “proverb of new public management” (p. 16). Skillful management enables administrative agencies to synergize multiple goals and improve their performance in the wake of organizational change and administrative reforms that often create complex goal and task environments (Fernandez & Rainey, 2006; Ingraham, Joyce, & Donahue, 2003; Meyers, Riccucci, & Lurie, 2001; Moynihan & Pandey, 2005). Capable management maintains organizational stability, buffers organizations from environmental shocks, and seeks opportunities and resources to support organizations (O’Toole & Meier, 1999).

A second and equally important question is how performance-based accountability system shapes goal integration and conflict. Public organizations often make trade-offs to respond to goal prioritization and target setting that are part of their performance evaluation, as managerial strategy of this type creates high-powered incentives (Bevan & Wood, 2006). Under performance regimes, public organizations may be dedicated to accomplishing goals of high visibility in a selective manner (Liang & Langbein, 2015). As discussed earlier, regulatory effectiveness is more closely related to organizations’ missions and institutional mandates. It may also be easier to produce tangible outputs and outcomes related to programmatic effectiveness than those related to social equity. Consequently, to align the incentives of policy implementers, performance and accountability mechanisms need to better incorporate social equity into administrative evaluation standards and, ultimately, into the public value system.

Footnotes

Appendix

Table A2.

Estimates (IRR) of Inflation Variables for Punitive Actions: Logit Model (N = 4,172).

	Model A3		Model A4
	IRR	SE	IRR	SE
Compliance violator	−2.849***	0.348	−4.028***	0.577
PEJA	0.538	0.695	−0.438	0.987
Minority population	−0.002	0.011	0.015	0.015
Residents below poverty level	−0.001	0.011	−0.023	0.018
PEJA with compliance violator	0.734	0.606	0.530	0.957
Policy intervention	—	—	0.605	0.587
Policy × Compliance violator	—	—	2.899***	0.697
Policy × PEJA	—	—	0.260	1.220
Policy × Minority population	—	—	−0.014	0.017
Policy × Residents below poverty level	—	—	0.034	0.023
Policy × PEJA with compliance violator	—	—	1.263	1.401
Median household income	−0.058	0.104	−0.106	0.085
Resident higher education attainment	0.009	0.009	0.008	0.009
Resident English proficiency	−0.027*	0.012	−0.022^†	0.012
Household linguistic isolation	0.013	0.019	0.014	0.017
Homeownership	0.004	0.007	−0.002	0.008
Regulated facility	−0.588*	0.284	−0.592*	0.233
Pollution-intensive industrial facilities	0.457	0.291	0.441	0.299
Manufacturing employment	−0.033†	0.020	−0.030^†	0.018
Nonattainment area	0.561	0.456	0.254	0.554
Lagged EPA inspection (t − 1)	−1.291	1.035	−1.381^†	0.711
Inspection	−0.031	0.075	−0.075^†	0.044
Population	0.249	0.178	0.294	0.195
Population density	−0.083	0.118	−0.093	0.141
Urban area	−0.134	0.478	−0.192	0.583
Lagged punitive action (t − 1)	−28.311***	0.492	−26.416***	0.581
Spatial lag punitive action	−8.692*	3.565	−10.345^†	6.088
Policy × Spatial lag punitive action	—	—	5.872	9.582
Constant	3.629**	1.094	4.043**	1.340
Log pseudolikelihood	−852.2655		−796.4597
Wald χ²(df)	415.25(21)***		357.46(28)***

Note. Estimated with robust standard errors. IRR = incident rate ratios; PEJA = potential environmental justice area; EPA = Environmental Protection Agency.

†

p < .10. *p < .05. **p < .01. ***p < .001.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

Author Biography

Jiaqi Liang is an assistant professor in the Department of Public Administration at the University of Illinois at Chicago’s College of Urban Planning and Public Affairs. Her primary research focuses on public management, bureaucratic politics, public policy process, social equity, race and ethnicity, and environmental policy. Her work has appeared in Journal of Public Administration Research and Theory, International Public Management Journal, Policy Studies Journal, Review of Policy Research, and Public Performance & Management Review.

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