adopting a unit pricing system for municipal solid waste: policy and socio-economic determinants

Environmental and Resource Economics14: 503–518, 1999.© 1999Kluwer Academic Publishers. Printed in the Netherlands.

503

Adopting a Unit Pricing System for Municipal SolidWaste: Policy and Socio-Economic Determinants

SCOTT J. CALLAN and JANET M. THOMASDepartment of Economics, Bentley College, 175 Forest Street, Waltham, MA 02452, U.S.A.

Accepted 22 October 1998

Abstract. Concerns about the environmental and aesthetic damages of municipal solid waste pollu-tion have triggered policy reform at all levels of government. As part of this effort, public officials areintegrating market-based policy instruments such as unit pricing into their solid waste plans. Despitethe economic advantages of unit pricing, constituency response has been mixed and hence adoptionrates have been below expectations. If the associated gains are to be realized, public officials mustidentify the key factors that influence this decision. To that end, this research empirically estimatesthe determinants of unit pricing adoption at the community level of analysis. Based on data for allcities and towns in Massachusetts, the results indicate that demographics, socio-economic attributes,fiscal capacity, and policy instruments influence this decision.

Key words: flat fee system, government policy, material recycling facility, municipal solid waste,unit pricing system

JEL classification: Q2, H7, and D4

1. Introduction

An observable trend in municipal solid waste (MSW) policy reform is the gradualintegration of market instruments that encourage both source reduction and wastediversion activity. Among these is the use of variable-rate or unit pricing schemes,which impose an explicit fee for waste disposal that varies with the quantityof waste discards. This incentive-based pricing approach replaces the heretoforeconventional use of a flat fee system. A flat fee system is one that chargeseach household or commercial establishment a fixed charge per period for MSWservices that is independent of the quantity of waste discarded.

Flat fee systems are unsound from both an economic and an environmentalperspective. Economically, such an approach preempts the use of marginal costpricing and hence disallows an efficient solution. From the perspective of the wastegenerator, a flat fee system charges an effective marginal price of zero for every unitof waste discarded beyond the first, even though the community incurs a nonzeroincremental cost.1 Hence, there is no market incentive to economize on generationor disposal. The result is that too many resources are allocated to the provisionof MSW services. Strathman et al. (1995) estimate that the associated efficiencyloss relative to disposal expenditures increases exponentially with the deviation

504 SCOTT J. CALLAN AND JANET M. THOMAS

between price and long-run marginal cost. They argue, therefore, that even if thetrue marginal cost cannot be accurately determined, policy makers should replaceflat fee systems with some type of variable-rate pricing for solid waste services.

Given the apparent gains of unit pricing and the need for MSW policy reform,many local governments have begun to integrate this market-based initiative intotheir overall policy plans. Well publicized are the results of prototype programssuch as Seattle’s subscription plan and the bag-and-tag system used in Perkasie,Pennsylvania, which attribute significant reductions in waste generation to unitpricing schemes.2 From the late 1980s to the early 1990s, the number of localcommunities employing variable-rate pricing grew to an estimated 1000 across theUnited States (Skumatz and Zach 1993). Yet, adoption rates nationwide have fallenbelow expectations, and many cities and towns struggle to gain support from theirconstituencies.

Sub-par adoption levels commonly are attributed to a general lack of awarenessabout the environmental and economic costs of using flat fees. This misperceptionis exacerbated by the conventional practice of financing solid waste services fromproperty tax collections, which masks even a minimal fee charged to each house-hold. Although such explanations are plausible, they do not explain why somemunicipalities have overcome these universal hurdles and adopted unit pricingschemes, while others have not.

Although the importance of unit pricing has stimulated recent research suchas Miranda et al. (1994) and Reschovsky and Stone (1994), empirical evidenceidentifying the determinants of unit pricing adoption is lacking. It is preciselythis void that is addressed by this research. To that end, we use Massachusettstown-level data to model and empirically estimate the policy, socio-economic,and demographic determinants of a municipality’s decision to adopt unit pricing.The theoretical specification is discussed in Section 2. This is followed by apresentation of the empirical model and the data used in the study in Section 3.The empirical results are presented and interpreted in Section 4, with concludingcomments offered in Section 5.

2. Theoretical Specification

A model of unit pricing adoption at the community level of analysis can be derivedfrom a structural model of supply and demand in the MSW services market.3 Thismarket is assumed to be affected by state and local MSW policy decisions, socio-economic attributes, and demographics. Using a conventional Marshallian demandfunction, the quantity demanded (Qd) of MSW services by residents in theithcommunity is modeled as follows:

Qid = Q(P,X), i = (1,2, . . . n), (1)

where:P represents the price that residents in towni are willing and able to payfor MSW services,X is the set of all non-price demand determinants, andn is thenumber of municipalities.

PRICING SYSTEM FOR MUNICIPAL SOLID WASTE 505

Non-price demand determinants in this market can be characterized as fallingwithin two major groups. The first is captured by a vector of socio-economiccharacteristics,s, expected to influence consumption of MSW services. Followingconvention, these characteristics are income, wealth, age, education, urbanization,and population of the community. A second vector,m, describes the types of muni-cipal disposal and recycling services offered to residents – curbside or drop-off. Inthis market, disposal services and recycling services are substitute goods. Thus, asthe relative convenience of these services changes, the relative opportunity costs ofconsumption change in the opposite direction, which in turn influences the demandfor each good. For example, if a town offers more convenient recycling servicesthrough curbside pickup, the opportunity cost of consuming these services declinesrelative to disposal, and theQd of MSW services should fall.

The comparable function for the quantity supplied (Qs) of MSW services by theith town is specified as:

Qis = Q(P,Z), i = (1,2, . . . n), (2)

where:P represents the price at which towni is willing and able to provide MSWservices to residents, andZ represents the set of all non-price supply determinants.

Several types of non-price determinants are expected to influence supplydecisions in this market. The first is the governance structure of each town,G,which represents the public decision-making process. There is also a vector offiscal variables,f, that captures the town’s financial capacity to provide MSWservices to residents. Since MSW services are commonly funded by property taxcollections, the relevant fiscal variables are the town’s property tax rate and arepresentative measure of housing values in the community. Another set of determ-inants,t, includes those factors affecting production technology. Among these aredemographic characteristics, such as housing density and the number of single-family homes in the town, as well as physical capital measures, such as landfillcapacity. Finally, a vector of government policy instruments,g, captures the influ-ence of state and local policy on supply decisions. Policy is expected to affect atown’s marginal costs of production, since more convenient services, for example,are more costly to produce. Judge and Becker (1993) refer to the cost trade-off thatarises when local policy makers attempt to discourage disposal by offering moreconvenient recycling services. If the policy is successful, the reduction is wastediscards lowers the town’s landfilling costs, but these cost savings are offset atleast in part by the increased cost of providing more convenient recycling services.

Structural equations (1) and (2) can be solved simultaneously for the munici-pality’s equilibrium price of MSW services. The result is a reduced-form equationthat defines price (Pi) as a function of the identified supply- and demand-sidemarket determinants as follows:

P i = P(X,Z), i = (1,2, . . . n) (3a)


or equivalently,

P i = P(s,m,G, f, t,g). (3b)

In the absence of externalities,Pi represents the efficient price for MSW servicesin the ith community.

Since the objective is to model the determinants of a qualitative decision,whether or not a community adopts unit pricing, we modify the dependent vari-able in equation (3b) to be the probability (pi) that price takes on a nonzerovalue, representing unit price adoption. This modification appropriately identifiescommunities using a flat fee system as those charging a zero price for MSWservices. In these municipalities, each household pays a fixed charge for MSWservices that is independent of the quantity of waste discarded. Hence, the marginalprice for each unit of services beyond the first is zero.4 The relevant distinction inthis analysis, therefore, is whether the price is zero or nonzero, which is capturedby the following unit pricing adoption function:

pi = p(s,m,G, f, t,g), i = (1,2, . . . n), (4)

where:pi = Prob(price> 0) and 06 pi 6 1.General support for this adoption function is given by Feiock and West (1993)

who present a fairly comprehensive discussion of various theories of local publicadoption. Using local recycling programs as a context, they empirically assess thepredictive capacity of seven competing models, each based upon a set of political,social, or economic factors. Finding some support for each, they then specify andestimate a combined model. In this latter specification, they find both supply anddemand characteristics, such as landfill capacity, population, governance structure,and income, are among the significant determinants of local adoption of curbsiderecycling.

Existing research on unit pricing adoption is limited, and there is no directempirical evidence upon which to base predictions about the expected effect ofeach hypothesized determinant. However, inferences drawn from the related literat-ure on MSW generation, disposal, and recycling provide insight about the expectedresults (Feiock and West 1993; Jenkins 1993; Miranda et al. 1994; Reschovskyand Stone 1994; Strathman et al. 1995; Callan and Thomas 1997).5 In general, weexpect that factors linked positively to waste disposal levels, landfill limitations,private or public support of recycling, relatively high costs of providing disposalservices, or favorable fiscal capacity should increase the probability of adoptingunit pricing, and the converse is true.

3. The Empirical Model and Data

The objective of the estimation is to identify the determinants of a qualitativedecision – whether or not a community adopts a unit pricing system. Thus, the


relevant empirical specification is a binary-choice model that describes the prob-ability that unit pricing is adopted, given certain socio-economic, demographic,technological, and policy conditions. To avoid the potential problem of estimatesthat yield probability values outside the (0,1) interval, a logistic regression modelis used. The form of this model is such that the dependent variable is the naturallog of the odds that a given decision is made. The resulting empirical specificationof the unit pricing adoption model is as follows:

ln[pi/(1− pi)] = β0 + β1I + β2HV + β3E + β4E2+ β5A+ β6A

2+β7CS + β8CR + β9RSCURB+ β10T SCURB+ β11Pop+β12Pop2+ β13G+ β14T + β15LFM + β16LFCL +β17LFCAP+ β18D + β19SF + β20MRF + β21Gr, (5)

where:

pi is the probability that towni adopts unit pricing,I is median household income in thousands,HV is the median value of single-family homes in thousands,E in the number of residents in thousands aged 25 years or older that have received

post-baccalaureate education.6

A is the median age of towni’s residents,CS is a dummy variable equal to unity if towni is classified as suburban, and zero

otherwise,CR is a dummy variable equal to unity if towni is classified as rural, and zero

otherwise,RSCURB is a dummy variable equal to unity if towni provides curbside recycling

services, and zero otherwise,TSCURB is a dummy variable equal to unity if towni provides curbside pickup for

trash disposal, and zero otherwise,Pop is the population of towni in thousands,G indicates the form of governance in the town through an ordinal ranking of least

to most democratic,7

T is property tax rate measured as dollars per assessed value (in thousands ofdollars) of housing,

LFM is a dummy variable equal to unity if a municipal landfill is located in thetown, and zero otherwise,

LFCL is a dummy variable equal to unity if the landfill is scheduled to close withinthe next 24 months and zero otherwise,

LFCAP is the landfill’s remaining lifetime capacity in tons per housing unit in towni,

D is housing density measured as the number of housing units per square mile ofland area in towni,

SF is the number of single-family homes in the town,


MRF is a dummy variable equal to unity if towni has access to the state’s materialsrecycling facility, and zero otherwise, and

Gr is a dummy variable equal to unity if the town received state funding forrecycling equipment or educational materials and zero otherwise.

The context for the empirical estimation is the Commonwealth of Massachu-setts, which has instituted a comprehensive “Integrated Solid Waste Master Plan”(Commonwealth of Massachusetts June 1990; February 14, 1994; December 7,1995).8 Among the state’s key initiatives are: (i) promotion of MSW sourcereduction through instruments such as unit pricing; (ii) waste diversion throughrecycling and composting; and (iii) better management and planning of land-based disposal. The long-term objective is to achieve a 46 percent recycling rateby the year 2000. Massachusetts has made significant progress since the 1990adoption of its master plan. By 1992, 341 of the state’s 351 towns and citieshad established local recycling programs, up from 190 in 1990. As of 1994, 97percent of the state’s residents have access to either drop-off or curbside recyclingservices (Commonwealth of Massachusetts February 14, 1994; December 7, 1995).However, officials recognize that access does not necessarily guarantee particip-ation. Hence, the current focus is to increase participation rates through publiceducation, the equipment grant program, and the “. . .implementation of unit-basedpricing systems to promote source reduction and provide dedicated solid wastemanagement revenues.”9

Statewide data on unit pricing are used to identify communities that have adop-ted any type of quantity-based pricing for waste disposal regardless of the type ofprogram used or whether the price is weight- or volume-based.10 In so doing, themodel captures the determinants responsible for adoption of quantity-based pricingvis-à-vis a flat rate scheme. To accommodate the logistic regression function, priceis set equal to unity for all towns who have adopted unit pricing and 0 otherwise.After excluding observations with missing data, 317 of the 351 Massachusettscities and towns are included in the estimation, with 79 of these communitiesemploying the MSW unit pricing approach. Descriptive statistics for each variableare given in Table I.

Data for all state and local policy variables and the landfill characteristicsare obtained from the Massachusetts Department of Environmental Protection(DEP).11 Variables for the form of governance in each community (G) and allsocio-economic, demographic, and fiscal measures (I, HV, E, A, Pop,T, D, SF)are derived primarily from 1990 census data (U.S. Department of Commerce1990; Horner 1994).12 The two community classification variables (CS andCR) areconstructed from seven more refined classifications defined by the MassachusettsDepartment of Education (1985). Each of the seven classifications was derivedfrom a series of socio-economic attributes, using cluster analysis. Among theincluded economic attributes are unemployment rate, a composite index ofcommercial activity, and a manufacturing index. Demographic data include the


Table I. Sample statistics for unit pricing adoption variables.

Variable Mean Standard

(n = 317) deviation

p 0.249 0.433

I 42.206 12.203

HV 169.403 59.782

E 1.291 3.298

A 35.222 3.111

CS 0.464 0.499

CR 0.401 0.491

RSCURB 0.429 0.496

TSCURB 0.549 0.498

Pop 17.628 37.971

G 5.555 0.865

T 10.121 2.615

LFM 0.300 0.459

LFCL 0.836 0.371

LFCAP 3.631 21.300

D 508.403 981.026

SF 3587.620 3824.090

MRF 0.249 0.433

Gr 0.584 0.494

All annual policy variables are based upon the state’s fiscalyear, which runs from July 1 to June 30.

percentage of minorities in the town and the percentage of the population livingin rental property. A description of each state-derived classification (labeled asC1 through C7), is presented in Table II. Following Bender et al. (1993), the CS

variable is defined as any town classified by the state as C2, C3, or C4, and the CRvariable represents any town classified as C5, C6, or C7.

4. Empirical Results and Interpretation

The method of maximum likelihood is used to estimate the logistic regressionequation (5).13 The estimated parameters and their asymptotic standard errors arepresented in Table III. Each parameter gives the estimated change in the log of theodds of adopting unit pricing associated with a unit change in the correspondingindependent variable.14 Based upon the likelihood ratioχ2 test statistic of 59.096,the coefficients of the independent variables are jointly significant.15

Beginning with the socio-economic variables, we find that income, housingvalue, education, age, and the rural classification variable are statistically signi-


Table II. Socio-economic classifications for Massachusetts communities.

Variable Classification Description

C1 Urbanized centers Manufacturing and commercial centers; denselypopulated; culturally diverse.

C2 Economically developed suburbs Suburbs with high levels of economic activity,social complexity; relatively high incomes.

C3 Growth communities Rapidly expanding communities in transition.

C4 Residential suburbs Affluent communities with low levels of economicactivity.

C5 Rural economic centers Historic manufacturing and commercialcommunities; moderate levels of economicactivity.

C6 Small rural communities Small towns; sparsely populated; economicallyundeveloped.

C7 Resort/retirement and artistic Communities with high property values; relativelylow income levels, and enclaves of retirees, artists,vacationers, and academicians.

Source: The Commonwealth of Massachusetts, Department of Education (1985), ‘A NewClassification Scheme for Communities in Massachusetts’.

ficant determinants of unit pricing adoption. The population parameters are notsignificant, however, suggesting that the absolute number of residents in a town isnot likely to influence the decision. Overall, these results indicate that the probab-ility that a community will adopt unit pricing is not entirely within the control oflocal public officials.

The inclusion of an income (I) variable in the estimation follows conventionalmodels of demand, including those dealing with MSW markets. In this study, theβ1

parameter estimate on income is negative, implying that the probability of adoptinga unit pricing program is lower for higher-income communities.16 We hypothesizethat the negative influence is likely linked to the consumption and discard patternsof higher-income households. Although more goods and services are purchasedby these households, the types of commodities they consume and their tendencyto donate rather than discard certain goods may result in lower disposal levels ofsome waste materials (Richardson and Havlicek 1975, 1978; Rathje and Thompson1981). Furthermore, some researchers find that higher-income households recyclemore of at least certain types of waste materials (Duggal et al. 1991). As aconsequence, there may be less of an incentive to encourage source reductionand waste diversion through unit pricing in communities already engaging in theseactivities.

The positive and significant finding for theβ2 parameter estimate on communityhousing values (HV) implies that wealthier communities are likely candidates for


Table III. Results of logit estimation of unit pricing adoption function.

Parameter Variable Estimate Asymptotic standard

error

β0 Intercept −22.0201c 12.2659

β1 I −0.0664a 0.0240

β2 HV 0.0090c 0.0047

β3 E 0.6262c 0.3446

β4 E2 −0.0296c 0.0174

β5 A 1.1512c 0.6855

β6 A2 −0.0166c 0.0098

β7 CS −1.0613 0.7782

β8 CR −1.2833c 0.7668

β9 RSCURB 0.0728 0.5501

β10 TSCURB −0.2788 0.5341

β11 Pop 0.0080 0.0359

β12 Pop2 0.0001 0.0001

β13 G 0.3246 0.3219

β14 T 0.1282b 0.0623

β15 LFM 1.1975a 0.4142

β16 LFCL 0.3474 0.5610

β17 LFCAP −0.0263 0.0335

β18 D −0.0006c 0.0003

β19 SF −0.0003b 0.0001

β20 MRF 0.6657c 0.3913

β21 Gr 0.2283 0.3245

Likelihood ratioχ2 test statistic = 59.096 (p-value = 0.0001)Percent correctly predicted = 74.4n = 317a significant at the 0.01 level;b significant at the 0.05 level;c significant at the 0.10 level.

unit pricing. The complexity arises in attempting to identify the source of thiseffect. As indicated in the theoretical exposition, HV is expected to affect bothdemand and supply decisions in MSW services markets. On the demand-side ofthe market, the positive estimate is supported by Miranda et al. (1994) and Benderet al. (1993) who argue that affluence is expected to influence receptiveness tounit pricing and its eventual success, although they offer no empirical evidenceof this assertion. Also, Granzin and Olsen (1991) cite various research findingssupporting the theory that individuals of higher socio-economic status are morelikely to support proenvironmental activities. For example, Jacbos et al. (1984)link areas with higher-valued properties to greater recycling participation rates.


On the supply-side of the market, higher housing values provide a larger tax basefrom which the requisite funding for implementing a unit pricing program can bedrawn.17 In their analysis of policy adoption, Feiock and West (1993) specificallylink affluence to local fiscal capacity for policy development and implementation.Since this supply-side argument also supports the positive sign ofβ2, we cannotdiscern whether the estimated wealth effect originates from one or both sides ofthe market.

Theβ3 andβ4 parameters on education (E), are significant, and their respectivesigns suggest that higher education levels in a community increase the probabilityof adoption but at a decreasing rate.18 Based on the marginal probability estimates,a unit increase inE from its mean value should increase the probability of adoptionby 8.7 percentage points. These results are expected and supported by existingresearch. The fundamental theory is that more educated individuals are likely to bemore aware of environmental issues and hence more supportive of environmentalprotection policy (Van Liere and Dunlap 1980; Granzin and Olsen 1991; Lansana1992; Smith 1995).

Theβ5 andβ6 parameter estimates on age (A) are significant and qualitativelyanalogous to the findings on education. The quadratic relationship implies thatadoption of unit pricing is least likely for communities whose residents are eitherrelatively young or relatively old. This result follows a theory of waste generationposited by Richardson and Havlicek (1978), which states that waste levels arelikely to be low at early stages of the life cycle, rise through the middle years,and then decline in the later years.19 As waste generation levels change with theage of a community, one would expect an analogous change in the need to adoptwaste reduction policies such as unit pricing.

With the urban classification suppressed, we find that the parameter for therural classification (CR) is negative and statistically significant, implying that ruralcommunities are less likely to adopt unit pricing than their urban counterparts.In fact, the calculated marginal probability for this parameter suggests that theprobability is 20.3 percent lower. This result is supported by Repetto et al. (1992),who argue that sparsely populated areas are not considered likely candidates forunit pricing because disposal costs in these regions are relatively low compared todensely populated areas.

Because the provision of curbside services increases the convenience and hencelowers the opportunity cost of recycling, its expected influence on unit pricingadoption is positive. Although the sign ofRSCURB parameter,β9, bears out thisexpectation, it is not statistically significant. A symmetrical argument applies tothe expected sign of theTSCURB parameter, but in the opposite direction. That is,the offering of curbside trash disposal effectively lowers the opportunity cost ofwaste disposal, which is counter to the intent of unit pricing. Consequently, thenegative sign ofβ10 follows expectations, but it is not statistically significant.

We offer two possible explanations for the statistical insignificance ofβ9 andβ10. First, the suppressed dummy in each case is the provision of an alternative,


albeit less convenient, type of service. Hence, the lack of significance implies onlythat the difference between offering curbside and drop-off services, and not thepresence or absence of all such services, is not a factor in unit pricing adoption.20

Second, because the Massachusetts master plan and its recycling initiatives havebeen in effect for over five years, many towns in the sample already have taken stepsto encourage recycling over disposal regardless of how they price MSW services.For example, although only 25 percent of the included observations have institutedunit pricing, 43 percent offer curbside recycling. Hence, the lack of a measuredeffect for RSCURB andTSCURB might be a function of the state’s initial emphasison recycling, which means that many communities initiated unit pricing longafterrecycling plans were well underway.

Included among the supply-side determinants is the form of town governance(G), ranked from least to most democratic. Although theβ13 parameter in ourmodel is not significant, it does bear a positive sign, which follows the resultsof Feiock and West (1993). In both their political institutions model and theircombined specification, they find that a more democratic form of governmentincreases the probability of adopting a recycling program. In Massachusetts, theoverwhelmingly predominant governance structure is open town meeting. Hence,the limited variability ofG across observations may explain its lack of significancein the model.

The expected importance of municipal fiscal capacity to unit pricing adoptionis borne out by the significantly positive estimates forβ2 andβ14 on housing values(HV) and property tax rate (T), respectively. What these findings imply is that townswith relatively strong revenue sources are better able to finance the start-up costsof instituting new policies like unit pricing.

Among the technological factors included in the model are those dealing withlandfills. Each of the associated parameters bears the expected algebraic sign,and theβ15 parameter onLFM is strongly significant. Given increasingly strin-gent requirements on landfill construction and the historical dependence of mostcommunities on land-based disposal, communities currently operating landfills areexpected to have a strong incentive to discourage excess waste generation. Theytherefore should be more likely to adopt unit pricing. According to the marginalprobability for LFM , the increased likelihood is estimated to be approximately19.0 percent. Similar arguments are applicable to declines in landfill capacityand impending closure (Skumatz and Breckinridge 1990). According to Jenkins(1993), preserving landfill space may be the most important consequence of unitpricing for local communities, and Skumatz (1991) argues that municipalities areincreasingly drawn to unit pricing as a means to extend available landfill capacity.In Massachusetts, there are 104 municipal landfills currently in operation, and themajority of these are unlined and expected to close within a fairly short period oftime. Thus, it is not unreasonable to expect that more towns will adopt unit pricingprograms as these scheduled closure dates approach.


Certain demographic factors, such as the number of single family homes (SF)in a community and housing density (D), should affect the production technologyof MSW services and hence the cost of provision. The expected impact on theadoption decision is that municipal suppliers facing relatively high average costsshould have a greater incentive to institute unit pricing, since the resulting costsavings are expected to be larger. Of course, the converse would also be true. Sincethe parameter estimates onD andSF, β18 andβ19 respectively, are negative andsignificant, it appears that these factors may give rise to scale economies, whichin turn would lessen the likelihood of adopting unit pricing. Evidence in partialsupport of this hypothesis is offered by Dubin and Navarro (1988) who find thatMSW collection services exhibit significant economies of household density.21

Counter to our finding onSF, however, they do not find the presence of scaleeconomies based on the number of households in a community. However, thisinconsistency may be a function of their use of households versus our use of singlefamily homes as the unit of analysis.

Finally, the influence of state and local policy variables on unit pricing isconsidered. Analogous to our findings on curbside services, the parameter on muni-cipal recycling grants (Gr) is not significant. However, the estimation does suggestthat access to a state-owned materials recycling facility (MRF) increases the likeli-hood that unit pricing will be adopted by approximately 10.5 percent. This findingis intuitively appealing. Access to the state’s MRF means that recycling processingand marketing services are provided at no charge. As suggested by Skumatz andBreckinridge (1990), access to services that facilitate the community’s recyclingeffort complement a unit pricing system and improve its cost-effectiveness.

5. Conclusions

For some time, economists have maintained that much of the so-called solid wastedilemma can be traced to the inefficient provision of MSW services. Rooted infundamental economic theory, the argument links excess waste generation anddisposal directly to the zero marginal price effected by a flat fee system. Withtougher regulations on land-based disposal and no apparent let-up in the growth ofMSW generation, federal and state policy makers have begun to accept this argu-ment and to encourage local communities to adopt variable-rate pricing of MSWservices. Despite some very favorable and highly-publicized successes, adoptionrates have been disappointing. If the environmental and economic gains of sucha market-based approach are to be realized on a broad scale, it is important thatpublic officials identify the most viable candidates for unit pricing and, wherefeasible, tailor policy decisions to encourage adoption.

Recognizing the importance of this issue and the lack of empirical evidence todate, this research empirically estimates the influence of various theorized determ-inants of unit pricing adoption. From a broad perspective, this study finds thatcertain socio-economic and demographic characteristics appear to influence the


adoption decision. Although such factors are not controllable by policy makers,awareness of these determinants can correct false expectations and hence diminishthe risk of costly failures. Further, an understanding ofwhy these characteristicsmatter can sometimes suggest policy innovations thatare within the control ofpublic officials. For example, if higher levels of formal education increase theprobability of adopting unit pricing, officials can develop programs to educate thecommunity about the social costs of flat fee systems and the expected gains ofconverting to a more market-oriented approach.

We also determine that policy decisions can affect a community’s decision toadopt unit pricing. Our findings suggest that state support of local recycling, inthis case through access to a state-owned recycling facility, increases the prob-ability that adoption will be achieved. Landfill regulations also seem to influencethe outcome. According to our results, administration of municipal landfills canbe an incentive for local officials to promote unit pricing as a way to maintainexpected landfill capacity. There are, however, some relevant policy questions thatour analysis leaves unanswered. A case in point is the insignificant finding for curb-side recycling services. Logically, a curbside recycling program should encourageadoption of a unit pricing scheme. More research is needed to test this theory in adifferent venue where policy priorities at the state level are less likely to affect theresults.

Supply-side conditions also appear to affect unit pricing adoption, such as localfiscal capacity. The policy implication is that federal and state officials may needto provide funding or technical support to communities as they initiate adminis-trative and operational changes to convert to variable-rate pricing. Other identifiedcost determinants relate to the presence of scale economies in producing MSWservices. Awareness of such scale economies can be invaluable to town plannersand may also facilitate the difficult process of setting the unit price, once adoptionis achieved.

On balance, the evidence offered by this study suggests that a community’sdecision to adopt unit pricing is explainable and therefore predictable to someextent. In certain instances, the decision may be directly or indirectly controllablethrough policy initiatives. The relevance of these findings to MSW policy develop-ment should motivate further empirical investigation of unit pricing adoption andthe associated implications for policy makers and society at large.

Notes

1. According to Repetto et al. (1992), this incremental cost is over $100 for every additional ton ofwaste generated.

2. In the early 1980s, the weekly disposal for a typical single-family household in Seattle wasreported as 31/2 30-gallon containers of trash. As a result of instituting unit pricing, 87 percentof Seattle residents subscribed to one 32-gallon trash container per week in 1989 (Seattle SolidWaste Utility 1991).

3. For a comprehensive theoretical and empirical exposition of this market, see Jenkins (1993).


4. Such a pricing scheme cannot yield an efficient solution as long as there are nonzero marginalprivate costs. This in turn implies that communities using a flat fee system are operating at adisequilibrium.

5. Support for specific variables provided by these and other research papers is discussed in thesubsequent presentation of empirical results.

6. As is commonly done in the literature, squared terms forE, A, and Pop are used to captureany nonlinearity in the measured effect of these variables on the dependent variable. Adding asquared term forI was unsuccessful, causing the parameter onI to become insignificant.

7. In Massachusetts, six types of town governance are used throughout the state. However, 97percent of the communities have adopted one of the following three: open town meeting (themost democratic), representative town meeting, or mayor-council. The remaining types ofgovernance are town council, council-manager, and selectperson and council.

8. The Massachusetts master plan follows the EPA’s recommendation for employing an “IntegratedWaste Management System,” which uses a combination of policy initiatives aimed at sourcereduction, recycling, and combustion and land disposal, ranked in order of priority (U.S. EPANovember 1989).

9. Commonwealth of Massachusetts (December 7, 1995, p. 15).10. Data on unit pricing adoption are obtained from the Commonwealth of Massachusetts

(September 1993) and updated for the 1995 fiscal year through direct communication with DEPrepresentatives.

11. We are particularly grateful to Joseph Lambert and John Crisley at the Massachusetts DEP forassisting us in collecting the data for this research and for explaining the variable definitionsand calculation methods used by the state. The specific data sources are Commonwealth ofMassachusetts (August 30, 1994; February 22, 1995; March 16, 1995; December 7, 1995; March7, 1996). Based on these data, a distinction could not be made between private and publicsuppliers of MSW services. Further, the consumption decisions captured by the demand functionreflect only residential waste generators.

12. The 1990 census is the most recently available data source for the socio-economic anddemographic variables used in this study.

13. The SAS procedure LOGISTIC was used to obtain the parameter estimates.14. Comparable estimates for the change in the probability of adoption are calculated from

the parameters using conventional methods suggested by Pindyck and Rubinfeld (1981) andMaddala (1986). Where appropriate, these are reported in the subsequent discussion as marginalprobabilities.

15. Greene (1997) suggests that an additional measure of “goodness of fit” is the percent of actualoutcomes that are predicted correctly. For this model, 74.4 percent of actual outcomes areaccurately predicted. Greene notes, however, that in determining a model’s goodness of fit, theinterpretation of this measure is not as clear as the statistical significance associated with thelikelihood ratio test.

16. Since the housing value (HV) variable is a proxy for wealth, the estimate of income’s influenceis not confounded by the presence of any wealth effect.

17. Further discussion of the effect of town fiscal capacity is given subsequently.18. It is important to note that these estimates are not confounded by population changes, which are

explicitly controlled for in the model.19. According to Jenkins (1993), this theory has been validated in models studying thequantityof

waste but not its composition.20. Since most towns in Massachusetts offer at least one type of each service, we were not able to

determine whether the presence of either curbside or drop-off services is a significant factor.21. Analogous findings are determined by Carroll (1995) who investigates the cost of providing

MSW recycling services.


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adopting a unit pricing system for municipal solid waste: policy and socio-economic determinants

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