JIAHR Issue 7 - Hospitality's Solid Waste Policy Patchwork: A Study of States' Regulatory Environments

November 1993 Issue 7 ISSN 1052-6099

Editor: Mahmood A. Khan
Department of Hospitality and Tourism Management
Virginia Polytechnic Institute and State University

Managing Editor: Eliza C. Tse
Virginia Polytechnic Institute
& State University

JIAHR publishes refereed papers on all aspects of hospital- ity and tourism research. When judged of sufficient qual- ity, individual papers are sent electronically as a single issue of the Journal to members of the Academy of Hospital- ity Research and to subscribing individuals and libraries. The material is copyrighted. JIAHR is indexed/abstracted in "Lodging and Restaurant Index" and "Leisure, Recreation and Tourism Abstracts."

Hospitality's Solid Waste Policy Patchwork:
A Study of States' Regulatory Environments

Leslie E. Cummings
D.P.A., R.D., C.I.A.


EDITOR'S NOTE: This is Part Two of a series of articles by Dr. Cummings on Solid Waste Management. The first Part appeared in JIAHR Issue No. 4.


Regulations that are fragmented across municipal borders raise the cost and complexity of regulatory compliance in the hospitality industry. This study is intended to illuminate socioeconomic, political, and bureaucratic forces of the policy environment in which hospitality organizations are embedded. Using the policy indicator systems model, the investigation tests the usefulness of a measure to characterize anticipated solid waste (SW) related regulatory activity in a given context. The hypotheses propose relationships between states' level of government centralization (system inputs), and two measures of SW regulation (outputs). The analysis consists of simple correlation, using the 50 United States as units. Analyses revealed that although state centralization is a useful SW management policy indicator, centralization forces vary inversely with state SW policy activity, contradicting the hypothesized direction. In addition to suggesting alternate explanations for findings, implications for hospitality organizations are discussed.

KEY WORDS: bureaucracy, centralization, government relations, policy-making, solid waste regulations, systems model

Statewide uniformity [of hospitality solid waste regulations] is the right way to go. ...[Its absence] will prove to be a nightmare!...URGE your state senator and representative...Immediate action is needed!
The above are but a few of the electrically-charged phrases from just the first page of a flyer to state members from the Upper Midwest Hospitality, Inc. organization ( Siegel, 1991 ). Their concern? Each of seven Minnesota cities had adopted separate solid waste- oriented food packaging regulations (e.g., mandated recycling, deposits). Consequently, hospitality organizations face an "incongruous patchwork of ordinances" statewide ( Siegel, 1991 ; and cf. Hamilton, 1991 ; Romeo, 1991 ). Evidence suggests that hospitality organizations experience numerous operational, economic, and competitive disadvantages when environmental regulations are imposed in a variety of local forms, rather than as regulations that are uniform statewide. This complexity only adds to concerns that the hospitality industry seems saddled with a disproportionately high rate of environment-related regulation ( National Restaurant Association, 1990 ).

The main purpose of this study is to evaluate the utility of state government centralization as a measure that could inform hospitality scholars, and plausibly, hospitality practitioners, about potential solid waste management (SWM) regulatory activity. If state government centralization is related to state SWM public policy activity, centralization level could provide clues about the probability and typical precursors of state- versus-local levels of regulatory activity. A second purpose is to examine forces of the socio-economic, political, and bureaucratic realm - the policy environment - in which hospitality organizations operate.

The investigation uses a comparative approach to answer the questions: What is the nature of the association between the degree of centralization of state government action, and the level of statewide policies in place for solid waste management (SWM)? Collectively, are states with a reputation as having a relatively centralized government more inclined to impose uniform statewide SWM actions? Or do states assessed as more- decentralized come sooner to a realization that statewide initiatives appear preferable to local variation in SWM policies?

The paper begins by documenting the value of the study, and moves to articulating core hospitality concerns over a "patchwork" of SWM polices. Next, the systems model of policy indicators research is explained as a conceptual frame, and the study's hypotheses are built thereon. Presentation of results follows descriptions of variables and the analysis technique. The final sections offer discussion of results, suggestions for future studies, and implications.


For the foreseeable future, SWM and other environment-related regulatory issues (i.e., water use, chemical use) are anticipated to remain among the most taxing problems faced by businesses, and especially, by hospitality organizations ( Van Warner, 1992 ; Vandermerwe & Oliff, 1992 ; Wittwer, 1992 ). Questions of local versus state and national policy mandates remain a fundamental and consequential concern for hospitality leaders and managers, for industry associations and lobbyists, and ultimately, for patrons ( Beck et al, 1989 ; Environmental Protection Agency, 1989 ; Hasek, 1991 ; Keegan & Howard, 1991 ; King, 1991 ; National Restaurant Association [NRA], 1990 ; Newsome, 1991 ). For instance, in an article entitled "Who's Leading the Green Revolution?," it is speculated whether "broad statewide waste laws will emerge from the present localized confusion..." ( Quinton et al., 1991 ).

Despite concerns, little hospitality research has been directed to hospitality policy ( Siegel, 1991 ; Rohring, 1992 ; Shanklin, 1991 ). Perhaps it is because a prescription for day-to-day operator responses to regulatory problems will not result from policy investigations. However, an enhanced understanding of the forces that do - and do not - appear to be associated with SWM regulatory action can be a useful first step. Analysis and discussion of policy-making forces can contribute to a greater understanding of the evolution of environmental regulations in the current political, economic, and socio-cultural milieu. Firms in Japan, for example, use this type of governmental behavior information as part of their planning base, just as they use economic forecasts and marketing research. Ultimately, familiarity with policy dynamics can support hospitality industry communications with lawmakers, bureaucrats, and the public.


Currently, scores of public and private bodies are drafting SWM and related environmental regulations and definitions that potentially pertain to the hospitality industry. "This sort of state-by-state, or even locality- by-locality, enactment of regulations... " seems certain to cause economic and efficiency hardships for the industry, invoke legal strife, confuse consumers, and limit environmentally beneficial innovations (e.g., compostable packaging) ( Co-Petitioners, 1991 ; and cf. Environmental Protection Agency [EPA], 1992 ; Greenberg, 1992 ). Fragmented regulations also can change the competitive climate when, for example, operators in one city must pay steep surcharges for disposables, while competitors just across the city limit line have no such directive. To intensify competitive concerns, SWM policies often apply to local hospitality properties, but not to supermarketers or other retail establishments.

John Cady, President of the National Food Processors Association, advocates that adoption of consistent regulations would provide "a level playing field with competitors, by having the same rules apply everywhere their products are marketed" ( Hemphill, 1991 ). An entire consortium of hospitality industry allies, the environmental task force of state attorney's general, and others urge adoption of broad-based, not local, eco- regulations ( Co-Petitioners, 1991 ; "Selling Green," 1991 ).

Regulatory compliance can be especially complex and costly for multi-unit operators with units in municipalities having diverse, sometimes-conflicting SWM regulations. "Legislation has a lot to do with how we handle waste management..We often are restricted by city (and state) laws that affect our accounts" admits George Pfeiffer, regional vice president of Marriott Food & Services Management ( King, 1991 ). The municipal checkerboard of SWM edicts creates hardship not only for commercial lodging, foodservice, and other enterprises, but among the most plaintive outcries are from those managing parallel activities in public schools, health care operations, etc.

Examples follow of the way in which hospitality is caught in the crossfire of intrastate patchworks of regulation. Officials in some localities, but not others nearby, may require hospitality operators to: pay surcharges for disposables; pay fees for single-service use permits; separate and store recyclables; and/or provide patrons with a choice of reusable or single- service products.

Inconsistent SWM regulations also raise costs throughout the production and distribution system by splintering markets. For example, one city's SWM regulations may allow operators to use pizza boxes with optimum insulation parameters, while regulations in a nearby city force them to use only "light-weighted" (designed with less material) boxes. This inhibits volume efficiences, with costs ultimately borne by hospitality operators and consumers. Regulatory inconsistencies also hamper building infrastructures between hospitality operators, vendors, and product processors. With sufficient regulatory homogeneity, such alliances could help to "close the loop" for recycling, refilling, and other constructive SWM tactics ( Frankel, 1992 ).

Because of the distress regulatory fragmentation imposes on hospitality, this study is a start toward building a body of knowledge about hospitality-related policy formulation.


In the research area of policy studies, numerous models have been developed to aid in understanding abstractions associated with public (or social) policy. For example, the "rational model" is based on subjective reason and logic. Those using the "incremental model" presume that policy takes shape via small, sequential, building-blocks. But for this study, the systems model of policy indicators (SM/PI) was chosen over other models because its construct makes it a useful and appropriate tool for studying hospitality organizations as an entity that both is influenced by public policy, and that can provide influence to the policy system.

The theoretical basis of the SM/PI is a body of work primarily developed in the decades of the late 1950's through the 1970's ( Dye, 1966 , p. 41; Easton, 1965 , p. 428; Foley, 1978 ; Jacob, 1976 ; Jacob & Vines, 1971 ). An extension of organization theory known as organizational systems theory forms the foundation of the approach. Incorporated into organizational systems theory is the physical science systems theory literature, including systems adaptation ( Buckley, 1967 , p. 4; Bertalanffy, 1968 , p. 13).

The SM/PI is intended to conceptualize the policy process as a system that survives through an ongoing cycle of input-output-feedback. This system adapts as necessary to cope with perceived changes (feedback). Figure 1 shows how the SM/PI might be conceptualized.


            Environmental Conditions      Policy Responses
          ===========================    ====================

                <-------      <-------      <-------

                               F E E D B A C K

               Inputs      +-------------------+      Outputs
               ------      |                   |      -------
                           |                   |
               Demands     |                   |
                           |        THE        |     ------->
                           |    HOSPITALITY    |
               ------->    |      POLICY       |      Decisions
                           |      SYSTEM       |
               ------->    |                   |     ------->
                           |                   |
               Support     |                   |
                           +-------------------+      Actions

             Note.  Adapted from Understanding Public Policy (6th
          ed.) by Thomas R. Dye, 1987, p. 41.

          Figure 1. This model represents the hospitality industry
          as embedded in a policy system. The model conceptualizes
          systems theory as a framework for policy indicators

The policy "environment" refers to the larger system external to hospitality organizations. The term is meant to capture the entire milieu in which policy-formulation is immersed. Environmental forces include government, society, the economy, technology, competition, etc. Characteristics and conditions that make-up this policy environment, directly and indirectly can influence other elements of the system.

SYSTEM INPUTS. In this study, the forces of a state's level of government centralization are deemed to be characteristics of the state policy environment that, as system inputs, influence (make demands of, support) the policy system. Centralization forces within each state's particular "environment" are expected to show a unique relationship with levels of each state's local and statewide SWM regulations (outputs).

SYSTEM OUTPUTS. Policy system outputs are decisions and/or actions by the system. Public policy outputs, including SWM regulations, are assumed to be a function of system inputs from the policy environment. Policy outputs are operationalized in this study as measures of enacted SWM regulations.

FEEDBACK leads to adaptation, an important mechanism of the systems model. Conceptually, a system (or organism) survives because, over time, it adapts as its environment (physical, socio-political, etc.) undergoes changes, and as feedback from conditional changes and from previous outputs influence the system.


ANALYSIS LANGUAGE: One convention of SM/PI research is the analysis language. The explanation is necessitated prior to the analyses discussion because the language is used in describing variables and hypotheses.

As presented under "Method" (below), correlation analysis is used to test the hypotheses. In the mathematics of correlation analysis, no basic difference is implied between an independent variable and a dependent variable. Rather, these labels are applied based simply on which side of the correlation equation a variable appears. However, in the case of the policy indicator model in use, public policy is designated as the dependent variable. Furthermore, the convention of SM/PI research is to infer that the independent variable indicates, explains, or even causes dependent outcomes. But causation can not be claimed outright. Evidence from previous studies, as well as informed reasoning, must be brought to bear in discussing such interpretations. This nomenclature is a premise of the model and is based on the theoretical justification of the models' composition.

LIMITATIONS: That which can be illuminated by studying relationships between variables has statistical and logical limits. Technically, this study is limited to understanding relationships between the degree of state centralization, and levels of state SWM policy outputs. It is intended to identify, analyze, and contribute to our understanding of a proposed significant policy choice determinant. Some have criticized the use of correlation analysis to study policy formulation (cf., Savage, 1976). Nonetheless, the difficulties in a systematic comparative study of policy outputs are so great that the ability to illuminate some bases of policy formulation is deemed to be more satisfying than having no understanding.

Finally, the systems model is limited in that it represents neither a prescriptive nor a normative policy analysis. The results will allow us neither to discern better policy outputs from worse, nor to recognize a right level of policy output from a wrong level.


Earlier researchers in policy studies and political science found that the level of government centralization is among the important environmental influences on many policy-related forces ( Cayer & Weschler, 1988 , pp. 40- 41, 44-45). Therefore, state government centralization was selected as a variable within the hospitality environment with an ability to explain SWM policy output variances across states.

Previous studies also support the use of a centralization variable because it represents an important element of organizational structure (Downs, 1976, pp. 10-11; Grumm, 1971; Mazmanian & Sabatier, 1981). In addition to its general usefulness as a policy indicator, an index of government centralization is an element of state organizational structure that has been shown to shape policy outputs in specific ways. In the environmental policy area, a greater degree of consolidation has been shown likely to result in greater levels of environmental policy outputs ( Enloe, 1975 , p. 181; Jones, 1976 ). More centralized state governments also appear inclined to concentrate other resources of power and action, and they tend to have greater access to resources such as dollars, technical expertise, and personnel in general. Their communications typically are superior to that of decentralized states in the number and strength of links. They tend also to have greater issue perspective and greater member loyalty. Conversely, state government decentralization appears to weaken policy output levels, perhaps because of resource fragmentation, lack of technological expertise, and/or a paralysis associated with decentralized authority, etc. ( Game, 1980 ; Sabatier, 1977 ; Worthley & Torkelson, 1981 ).

State government centralization also is an important factor politically. Lester et al. (1983) suggest that in more centralized states, greater accountability and broader authority creates pressure for action. Centralized state governments seem better able to deal with contentious intrastate jurisdictional matters. For example, because one state's officials had been unable to site a waste disposal facility for over a decade due to local opposition, the state legislature was forced to enact a law to pre-empt local opposition ( Doyle, 1987 ). Thus a centralization measure can be significant in illuminating responsiveness in a socio-political environment fraught with such dilemmas as NIMBY (not in my backyard), and LULU (locally undesirable land use). In order that downstream and intergenerational protection is achieved, centralized authority is believed imperative in negotiating boundaries for waste restrictions and even for "licenses to pollute" ( Hrezo & Hrezo, 1985 ; Joskow & Null, 1981 ; Ripa di Meana, 1991 ; Schwab, 1988 ).


Drawing from the above theoretical foundations, it is suggested that state centralization is significantly related to SWM policy output tendencies. Further, previous research indicates that the direction of the association will be that states with greater levels of statewide policy outputs will tend to be indicated by states with governments more centralized at the state level. The proposed relationships can be stated as follows:

HYPOTHESES I: In the aggregate, the degree of centralization of states will show significant linkage with each of the two variables used to measure statewide SWM policy outputs.

HYPOTHESIS II: The direction of the relationship between state centralization and each measure of statewide SWM policy output will be positive; centralization strength will covary with policy output strength.

Variables and Operationalization to Test Hypotheses

Independent Variable or Proposed Policy Indicator

The independent variable is a measure of the relative vertical centralization of government at the state level, as developed by Stephens (1985) . The absence of uniform bureaucratic arrangements for state SWM policy-making prompted use of this indirect measure ( Jones, 1976 ). The variable will be abbreviated as GOVCTR (government centralization). On a scale of 1 to 5, Stephens rates as "5" those states deemed to be most centralized in promulgating, funding, and implementing state policies. A "4" rating identifies a state that is partly centralized. A state with a score of "3" is regarded as balanced. He rates as "2" those states that are partly decentralized, with a score of "1" denoting the most decentralized states. Table 1 lists each state and its assessed centralization score. Table 2 provides frequency information for the GOVCTR variable.

               Table 1     GOVCTR: Proposed Indicator Variable-
                    Centralization Assessment by State

          State                              GOVCTR*

          Alabama                              B
          Alaska                               C
          Arizona                              B
          Arkansas                             B
          California                           D
          Colorado                             D
          Connecticut                          C
          Delaware                             C
          Florida                              PD
          Georgia                              PD

          Hawaii                               C
          Idaho                                PC
          Illinois                             B
          Indiana                              PD
          Iowa                                 PD
          Kansas                               B
          Kentucky                             C
          Louisiana                            PC
          Maine                                C
          Maryland                             B

          Massachusetts                        C
          Michigan                             PD
          Minnesota                            D
          Mississippi                          B
          Missouri                             B
          Montana                              C
          Nebraska                             B
          Nevada                               PD
          New Hampshire                        C
          New Jersey                           B

          New Mexico                           C
          New York                             D
          North Carolina                       B
          North Dakota                         C
          Ohio                                 B
          Oklahoma                             B
          Oregon                               PC
          Pennsylvania                         B
          Rhode Island                         C
          South Carolina                       B

          South Dakota                         C
          Tennessee                            B
          Texas                                B
          Utah                                 B
          Vermont                              C
          Virginia                             C
          Washington                           B
          West Virginia                        C
          Wisconsin                            B
          Wyoming                              B

               * D = Decentralized; PD = Partly Decentralized;
                 B = Balanced; PC = Partly Centralized; C = Centralized.

                 See Appendix for data source.
           Table 2           GOVCTR: Proposed Indicator Variable
          Frequency Data in 50 states

          State            Data        Frequency       Percent
          Centralization   Coded as:    within          within
          Character                    50 states       50 states

          Decentralized       1            4               8

          Decentralized       2            6              12

          Balanced            3           21              42

          Centralized         4            3               6

          Centralized         5           16              32

          Total               -           50             100

Dependent Variables

Two dependent variables are used to assess the influence of the degree of state centralization on statewide SWM policy levels. The first dependent variable is a composite score (zero to ten) depicting each state's level of initiative in responding to SWM policy needs. The measure is from the State of the States report by the Washington D.C. based Fund for Renewable Energy and the Environment ( Ridley, 1987 ). The scale was generated using "selected indicators of legislation enforcement, funding, and other considerations...that have been made into law, formal standards, or guidelines" (Ridley). It is abbreviated as SWMINIT (solid waste management initiative). The comprehensive SWMINIT scores range from five states with "one" (the lowest score, least initiative), to one state with "ten", the highest possible score.

The second dependent variable represents policy output as a score. The score represents up to four selected SWM policies that each state may have in place. These particular four SWM policies were chosen for this study because they represent a typical range of steps that states have adopted on both local and statewide bases as SWM responses. The four are: (1) general recycling, (2) beverage container recycling, (3) a waste- to-energy (combustion) plan, and (4) a mandate that state offices procure recycled products. This variable is abbreviated as SWMFOUR (four SWM policies). State scores for SWMFOUR could and did range from zero (none of these policies) to four (all policies). (See Appendix for source information on all variables.)


The GOVCTR variable was examined systematically and described according to SWM policy output relationships. The 50 states of the U.S. served as units of analysis. One equation for each of the two dependent variables (SWMINIT and SWMFOUR) was analyzed using the Pearson product moment correlation analysis subroutine in SPSS- X. The correlation coefficient, symbolized by "r", expresses the closeness of the association between two measures in standardized units, with the assessment ranging from +1.0 to -1.0. It provides a measure of the direction and the degree of linear interrelatedness of variables, an appropriate and typical statistic for measuring the extent to which sets of degree variables are related in a policy indicators study. This design is constructed to simulate the model view that policy outputs are related to state characteristics. Thus the coefficients express the direction of input/policy output relationships, and the comparable strength of the relationships. In order to simulate a cause-effect influence, the value of each dependent variable was assessed for a period that was two or more years following the period measured for the independent variable, GOVCTR.

Explicit measures of state economic and demographic characteristics intentionally are excluded from this study. However, partial correlation coefficients with traditional indicators of state economic and demographic status (e.g., average income per person; degree of urbanization) and GOVCTR were calculated and reviewed in order to assess the possibility of such variables obscuring independent effects or creating spurious relationships. In testing for multicollinearity, the correlation matrix of these variables was examined. The finding that no partial correlation coefficients exceeded .8 was interpreted as evidence that multicollinearity was sufficiently low to insure unambiguous interpretations of the bivariate coefficients ( Berry & Feldman, 1985 ).


The correlation analysis produced a coefficient expressing the interrelationship between the proposed policy indicator, GOVCTR, and each of the two dependent variables representing state SWM policy outputs. The results are presented in Table 3. The zero order correlation for GOVCTR with SWMINIT is -.31; for GOVCTR with SWMFOUR the result was -.39. Both coefficients are statistically significant (p is less or equal to .05). Comparing bivariate coefficients depicting actual variable relationships with the theorized relationships reveals the following.

HYPOTHESIS I: The significance of the bivariate coefficient supports Hypothesis I. The analysis reveals that both equations show significant linkage of GOVCTR with the SWM policy output variables. Thus, even when tested using two very different measures of state SWM policy output, a measure of state centralization upholds its hypothesized usefulness as a state SWM policy indicator.

HYPOTHESIS II: GOVCTR yielded a negative bivariate correlation coefficient with each dependent variable. The bivariate correlation results in Table 3 show that the level of vertical centralization of government in the state covaries inversely with both SWMVIGOR (-.31; p is less or equal to .05), and SWMNUMBR (-.39; p is less or equal to.05).Thus the theorized relationship, that state centralization strength would covary with state SWM policy output strength, is contradicted.

          Table 3  Zero Order Correlation Results
                                             | GOVCTR:
                                             | Government
                                             | Centralization
                                             | at State Level
          SWMINIT:                           |
          Initiative on SWM at state level.  | -.31 (p is less or
                                             | equal to .05
          SWMFOUR:                           |
          Number (of four) SWM policies at   | -.39 (p is less or
          state level.                       | or equal to .05

                          Note: Pearson r; N = 50

Discussion and speculation is provided below on the outcome that differed from that anticipated; the correlation of GOVCTR with SWM policy outputs did not support the reasoning underlying Hypothesis II. Due to the results patterns shared by the two dependent variables (SWMINIT and SWMFOUR) the discussion involves the two jointly.

Hypothesis II was based on theories that greater centralization of state government (a higher GOVCTR score) is associated with a greater concentration of, and less fragmentation of, governmental perspective, authority, communication, fiscal resources, and relevant types of support. Previous studies suggested that statewide regulations represent a resolute governmental response to ecological management challenges. Thus the interpretation applied was that greater centralization would tend to be associated with a greater inclination in that state to act on SWM problems with regulations that applied statewide (versus regulations that differ across municipalities within the state). As GOVCTR was operationalized, SWM policy activity was expected to increase as measures of state government tended toward a more centralized form. The independent variable, GOVCTR, was assumed to covary directly with the dependent (SWM policy) variables. However, the negative bivariate results imply instead that as state government organization moves away from more centralized to more balanced or more decentralized structures, statewide SWM policy output levels tend to increase. The paragraphs below suggest alternate explanations and suggested theory-based approaches for reformulation in future hospitality-policy studies.

Posited Explanation for these Results:

THINGS HAVE CHANGED. These findings provide evidence of the systems model's adaptation mechanism. The environmental dynamics surrounding states' political structures appear to be functionally different today from patterns of two and three decades ago, when studies on which hypothesis II is based were completed. With the 1980 advent of Ronald Reagan's Presidential administration came increasing reliance on a decentralized federal government ( Dye, 1988 ). President Bush continued the delegation of decision-making to state governments. Shifting from a more federally-based government has placed the locus of control for governmental decisions and actions, including SWM policy- making, at state and local levels ( Hamilton, 1991 ). Evidently, when states are in control, and are faced with intractable and expensive environmental problems, the earlier response is reversed. Now it appears that less centralized states respond first with statewide measures. Any of the alternate formulations described below also could lend explanation to the study's results.

CENTRALIZATION PARALYSIS. Another possibility for the result that GOVCTR is inversely related to SWM policy measures is that, in the case of waste and/or environmental management, the centralization of authority, responsibility, and resources is today disadvantageous to the formulation of policy, rather than being beneficial. This could come about due to such interference as shifting and competing state priorities, the isolation and stagnation of technical personnel, goal displacement, and the clouded accountability sometimes associated with centralization ( Cayer & Weschler, 1988, pp. 41-46).

Alternate Formulations for Future Research

ENVIRONMENTAL POLICY-MAKING HAS ITS OWN STRUCTURE. It is possible that a measure of state centralization more closely operationalized in SWM terms, rather than in general terms, would have produced the hypothesized outcome. The operationalization of the GOVCTR concept may need to be more specific to SWM, or to environmental- policy promulgation. Lester et al. (1983) found such an operationalization useful. They used a dual index of bureaucratic consolidation that was specific to state level environmental organizations, and found that centralization played "an overwhelming role" (positively correlated) in determining levels of hazardous waste policies in states with less critical circumstances. This finding was achieved by using control categories of high and low problem-severity (measured as the quantity of toxic chemicals generated).

LOCAL DISCRETION FACILITATES ACTION. A limitation of the systems model's input-output approach is the difficulty in excluding variables that mimic, or are intertwined with, measures under study. For example, the concept of public administrators' degree of local discretion provides a rival hypothesis for the influence of government centralization in this study. Others have provided groundwork on the policy influence of local discretion. Discretion can be interpreted as a measure of judgement in areas of personal, financial, structural, and functional decisions that may modify the fact of, or way of, performing functions ( Berman & Martin, 1988 ; Elazar, 1972 ; Stephens, 1985 ; Zimmerman, 1983 ). The fundamental questions become; What is the relationship between the strength of local-versus-state discretion, and measures of statewide SWM policies? Which, in general, is the precursor of state SWM policy? Does SWM policy tend to trickle up from local levels, or down from state levels?

Given that SWM problems are experienced locally, and that most existing SWM policies are at the sub-state level, local discretion on budgets and authoritative matters may be linked with greater impetus to stimulate statewide SWM policies when compared with the impetus of state-level discretion. After all, in the words of Mr. Philip Thomas "Tip" O'Neill, Jr., "all politics is local!"

EXTRA PUSH OF LOCAL COMMITMENT. Related to the above, an alternative measure is the strength of local- versus-state level commitment in influencing statewide SWM policy levels. For example, a more decentralized state official might have a greater identification with local conditions and actions. The suggested SWM policy influence of local commitment is supported by Dye's proposition of the parochial influence of individual policy-makers ( 1971, p. 208 ). Legislators represent deep- rooted local constituencies. State legislators typically are similar to, live among, socialize with, and, very- essentially, are elected by, local persons with local interests. This territorial bias of state legislators is "relatively clear, unmixed, and unambiguous" in being carried over into state policy matters ( Dye, 1971 , p. 209).

Those in less centralized state structures, then, might be more committed to, more active in, and ultimately more successful in generating local SWM policy responses. Strong, local resolve for local solutions, arguably, would be more likely than inaction to capture the attention, and possibly, the support, of state level decision makers. The fundamental questions essentially parallel those posed for local discretion above. A revised GOVCTR measure could target the existence, activity, and comprehensiveness of local SWM or environmental programs, regardless of the office of origination (water, land use, energy). The restructured measure of local concern might characterize states based on SWM grass roots activities, and/or a measure of the extent to which major sub-state jurisdictions (perhaps based on the percent of state population encompassed) are involved in initiating SWM activities.

OTHER PERSPECTIVES. While the systems model is especially of interest to those directly involved in policy formulation, alternate policy models may be more specific in informing hospitality practitioners; those affected by, or making demands on, the policy system ( Dye, 1987 , Chapter 2). Also, the discussion hints at the need for greater consideration in future hospitality policy research of policy sub-systems (e.g., environment- oriented agencies, interest groups).


These results offer tentative, qualified conclusions, along with suggestions and questions for further study. The study also is useful in its illumination of the dynamic socioeconomic, political, and bureaucratic environment in which hospitality organizations are embedded. Development of the GOVCTR equations represented an effort to conceptualize and empirically to examine linkages between state centralization and SWM policy outputs. Judging from the analysis results, it is possible to infer that state centralization, as operationalized in this study (GOVCTR), IS an indicator of state SWM policy output levels. However, contrary to prevailing theoretical bases, less centralized states appear to enact statewide policies ahead of states with more vertically centralized organizations.

An integral question sparked by attempting an applied interpretation of these research findings is: What is the association between the rigor and number of local SWM mandates - and the degree of state centralization? The policy literature suggests that less centralized states have more local activity. If this is the case, then it appears that hospitality operators in less centralized states who are mired in a myriad of local SWM mandates may find that their states are more likely to enact uniform, statewide SWM policies sooner than more centralized states. Conversely, this assumption would imply that states with more centralized state governments are associated with fewer or lagging statewide SWM regulations. However, the question of whether the level of local SWM policy activity covaries with the degree of state centralization remains to be tested under contemporary environmental conditions.

This study also highlights the intergovernmental gaps in our policy system which can leave hospitality operators in the crossfire of varied and inconsistent policies from one municipality to the next. Therefore it is suggested that industry leaders at all levels be well- informed on environmental issues, and sustain a presence as advocates and educators among legislators, lobbyists, bureaucrats, and other influential officials and organizations ( NRA, 1990 ; Shanklin, 1991 ).

Alliances with Leagues of Cities and Towns, and with similar private, non-profit organizations, could be an important first step for unscrambling the policy puzzle faced by hospitality operators. Increasingly such groups are working to establish effective SWM and other policies that are coordinated across sub-state jurisdictional boundaries (cf. Berman & Martin, 1988 ). Successes are in the form of more standardized policies, and policies elevated to a broader, less locally-fragmented level.


Hospitality organizations continue to be confronted with "an impossible patchwork" of environmental policies. Evidence shows that when SWM regulations do not apply statewide, but instead differ at the local government level (i.e. city, county), then hospitality organizations are especially burdened. Hardships include confusion, cost and performance inefficiencies, competitive disadvantages, and constraints on constructive SWM minimization responses ( Co-Petitioners, 1991 ; Siegel, 1991 ; Wolfe, 1990 ). This policy study was undertaken in response to the need for hospitality scholars, and for practitioners with governmental affairs and strategic planning roles, to move toward a better understanding of forces underlying policy-making in the hospitality sphere. Questions such as those raised in this study call for continued study of U.S. and international policy formulation because both environmental regulation and governmental relations seem destined to remain among the hospitality industry's most vexing challenges.


                             Data References
               Variable                       Reference

           Dependent Variable

               SWMINIT                        Ridley, 1987, p. 5.

               SWMFOUR (Four sub-variables):

                  General recycling           Ridley, 1987, p. 16.
                  Beverage can recycling      Mahar, 1988, p. 20.
                  Resource recovery plan      North, 1988, p. 65.
                  Must procure recycled
                   products                   North, 1988, p. 67

          Independent Variable

                  GOVCTR                      Stephens, 1985, p. 72


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Dr. Cummings is a member of the faculty of the Food and Beverage Department, William F. Harrah College of Hotel Administration, University of Nevada, Las Vegas.


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