United States
Environmental Protection
Agency
Municipal Environmental Research
Laboratory
Cincinnati OH 45268
EPA-600/5-79-008
August 1979
Research and Development
&EPA
Impact of User
Charges on
Management of
Household
Solid Waste
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Research reports of the Office of Research and Development, U.S. Environmental
Protection Agency, have been grouped into nine series. These nine broad cate-
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EPA-600/5-79-008
August 1979
IMPACT OF USER CHARGES ON
MANAGEMENT OF HOUSEHOLD SOLID WASTE
by
Fritz Efaw and William N. Lanen
MATHTECH, Inc.
Princeton, New Jersey 08540
Contract No. 68-03-2634
Project Officer
Oscar W. Albrecht
Solid and Hazardous Waste Research Division
Municipal Environmental Research Laboratory
Cincinnati, Ohio 45268
MUNICIPAL ENVIRONMENTAL RESEARCH LABORATORY
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
CINCINNATI, OHIO 45268
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DISCLAIMER
This report has been reviewed by the Municipal Environmental
Research Laboratory, U.S. Environmental Protection Agency, and approved for
publication. Approval does not signify that the contents necessarily
reflect the views and policies of the U.S. Environmental Protection Agency,
nor does mention of trade names or commercial products constitute
endorsement or recommendation for use.
11
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FOREWORD
The Environmental Protection Agency was created because of increasing
public and government concern about the dangers of pollution to the health
and welfare of the American people. Noxious air, foul water, and spoiled
land are tragic testimony to the deterioration of our natural environment.
The complexity of that environment and the interplay between its components
require a concentrated and integrated attack on the problem.
Research and development is that necessary first step in problem solu-
tion and it involves defining the problem, measuring its impact, and search-
ing for solutions. The Municipal Environmental Research Laboratory develops
new and improved technology and systems for the prevention, treatment, and
management of wastewater and solid and hazardous waste pollutant discharges
from municipal and community sources, for the preservation and treatment of
public drinking water supplies, and to minimize the adverse economic, social,
health, and aesthetic effects of pollution. This publication is one of the
products of that research; a most vital communications link between the re-
searcher and the user community.
User charges for financing the collection and disposal of municipal solid
waste are used by a number of communities in the United States. Interest in
user charges has been growing because of increasing public resistance to prop-
erty taxes—the traditional way of financing the solid waste service. There
is a lack of information, however, on whether such charges or fees produce
net social benefits. The effects of incremental charges on the quality and
quantity of collected residential solid waste, litter, resource recovery and
economic efficiency have never been fully evaluated. The research reported
here represents an initial attempt at answering some of the questions raised
by local community leaders and managers concerning user charges for management
of solid waste. These results should be considered preliminary as additional
studies are needed before conclusions and recommendations can be made about
the economic efficiency of user charges.
Francis T. Mayo
Director
Municipal Environmental Research
Laboratory
111
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ABSTRACT
A basic proposition of economic theory is that when the price of a good
increases, other things being equal, the quantity of that good demanded will
decrease. Economists have suggested that this relationship between price and
quantity demanded could be used by solid waste managers as a tool for effi-
cient management of household solid waste.
This study presents empirical evidence from five selected communities
with various charge structures for solid waste collection and disposal. It
suggests that although household choices between types or levels of collec-
tion and disposal services may be sensitive to price, the total quantity of
waste generated by households may not be sensitive to price. It also suggests
that quantity of waste increases with household income at a rate consistent
with that found by other studies.
This report was submitted in fulfillment of Contract No. 68-03-2634 by
MATHTECH, Inc. under the sponsorship of the U.S. Environmental Protection
Agency. This report covers a period from November 1, 1977 to February 1,
1979, and work was completed as of May 16, 1979.
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CONTENTS
Foreword ill
Abstract iv
Figures vii
Tables ix
Acknowledgments xi
1. Introduction 1
2. Conclusions 3
Effects on demand 3
Effects on cost 5
Social and political issues 6
Administrative issues 9
3. Recommendations 11
4. The Economic Efficiency of User Fees: Theory and
Empirical Results 12
Economic efficiency 13
A classification of fee structures 18
Review of literature 26
Conclusions 32
5. Burbank, California 33
The Burbank solid waste system 33
Data collected 37
Empirical results 45
Litter effects and administrative costs 50
Conclus ion 51
6. Sacramento, California 52
The Sacramento solid waste system 52
Data collected 58
Empirical results 63
Litter and administrative costs 66
7. Provo, Utah 69
The Provo solid waste system 69
Data collected 72
Empirical results 76
8. Grand Rapids, Michigan 78
The Grand Rapids solid waste system 78
Data collected 82
Empirical results 96
Litter and administrative costs 100
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CONTENTS (continued)
9. Tacoma, Washington 105
The Tacoma solid waste system 106
Data collected Ill
Empirical results ,....» 126
Litter and administrative costs.... 131
References 133
Appendices
A. List of user fee cities 135
B. Multinomial logit demand model of Tacoma 164
VI
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FIGURES
Number Page
1 Illustration of the calculation of total
willingness-to-pay 16
2 Illustration of the calculation of consumers surplus 16
3 Evaluating economic efficiency 17
4 Burbank residential refuse routes 35
5 Burbank collection regulations 36
6 Tonnes of household waste collected, Burbank 40
7 Annual per capita residential waste, kgs 42
8 Functional organization chart 54
9 Sample bill, Sacramento 57
10 Sacramento household solid waste collected 59
11 Detailed organization chart, Sacramento 62
12 Provo collection districts 73
13 Census tracts 74
14 Provo billing districts 75
15 Grand Rapids collection districts 81
16 Location of waste disposal sites 83
17 Tonnes of solid waste collected, Grand Rapids 87
18 Kilograms of waste collected per bag (or tag) sold 88
19 Relative popularity of bags and tags 91
20 Map of Tacoma, showing landfill location 109
vn
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FIGURES (continued)
Number Page
21 Tonnes of household solid waste, Tacoma 114
22 Percent of household waste collected and self-hauled 115
23 Number of extra bags collected, Tacoma 117
24 Kilograms per container collected 118
25 Percent of households at minimum service levels 122
26 Percent of households at various quantity levels 123
27 Percent of households with carryout service 124
B-l Percentage of households at each service level under
present charge rates and under a flat rate system 171
Vlll
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TABLES
Number Page
1 Tonnes of Solid Waste Collected, Burbank 39
2 Burbank Population Estimates 41
3 Annual Per Capita Residential Waste, Kgs 41
4 Average Daily Residential Waste, Burbank 43
5 Total Waste Collected from Municipal Agencies 44
6 Top Monthly Salaries for Solid Waste Personnel
During Fiscal Year (Ending 30 June) Shown 44
7 Fee Schedules 45
8 Retail Sales Tax Collected, Burbank 46
9 Alternative Income Elasticities 49
10 Sacramento Fee Schedule 55
11 Household Waste Collected, Sacramento 58
12 Tonnes of Waste Disposed, Sacramento 60
13 Salary Range for Waste Removal Personnel 61
14 Residential Collection Fees, Sacramento 63
15 Retail Sales Tax Collected, Sacramento 65
16 Tonnes/Day, Residential Collection, August 1976
through January 1977 72
17 Number of Customers Selecting Curb and Backyard Service 72
18 Price of Residential Service, Monthly 76
19 Quantities of Waste Collected and Containers Sold 85
20 City Refuse Tag Sales, 1974 89
ix
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TABLES (continued)
Number Page
21 Households Receiving City Collection 90
22 Income Tax Collected 90
23 Cost of Collection, Grand Rapids 93
24 Base Annual Wage of Collection Employees 94
25 Relation of Operating Costs to Sales Revenues 94
26 Price of Bags to Households and Cost to City 94
27 Annual Cost and Collection Figures 95
28 Quantity of Waste Disposed, Tacoma 112
29 Prices for Residential Collection 119
30 Number of Households Selecting Various Service Levels 120
31 Taxable Retail Sales, City of Tacoma, 1973-77 125
32 Annual Figures Related to Quantity of Household Solid
Waste and Cost of Processing Household Solid
Waste in Tacoma 127
A-l Cities Employing User Fees for Household
Solid Waste Collection 139
B-l Estimated Parameters of Multinomial Logit Model 167
B-2 Estimated Price Elasticities using Multinomial
Logit Model 167
B-3 Regression Model Results (t-Statistics Shown in
Parentheses) 168
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ACKNOWLEDGMENTS
The authors would like to express their gratitude to the following
people who assisted in the preparation of this report:
The case studies which form the basis of the report would not have
been possible without the cooperation of solid waste systems managers in
each city we visited. In particular, we would like to thank Mr. James
Biener and Ms. Jacquelyn Rosloniec of the Grand Rapids, Michigan,
Environmental Protection Department; Mr. Ken Pierson, Sanitation
Superintendent, Burbank, California; Messrs. Reginald Young and Paul
Smilanich of the Division of Waste Removal, Sacramento, California; Mr.
John Farley, Superintendent of Sanitation, Provo, Utah; and Messrs. William
Larson and Klaus Hagel of the Tacoma, Washington, Public Works Department.
We would also like to thank Deborah Piantoni, Michael Remich, Pam
Stonier, and Sally Webb for their dedicated work in preparing the report
manuscript.
Finally, we would like to thank Messrs. Oscar Albrecht and Haynes
Goddard of EPA-MERL for their helpful comments and criticism in writing the
final report.
XI
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SECTION 1
INTRODUCTION
Various strategies for collection of residential household solid
waste have been developed over the years in an attempt to provide efficient
solid waste collection and disposal services. As our understanding of
environmental problems associated with solid waste has evolved, the
operational concept of efficiency has also changed to encompass a broader
meaning than that of minimizing collection and disposal costs.
Demand for solid waste collection and disposal, like economic
demand for any service, is affected by its price relative to the prices of
other goods and services. As household cost rises, demand for
"conventional" waste disposal falls. This is reflected both in a decline
in waste generation and in increased use of other disposal methods such as
littering and self-hauling. Also, as with many other goods and services,
the consumption of solid waste disposal services leads to the imposition of
costs on third parties. These external costs are often not reflected in
prices paid by individual households.
The qualitative effects on households that result from imposition
of (or an increase in) service charges or changes in quality of service can
be developed from economic theory. Similarly, the cost of
collection/disposal systems can be determined from engineering data.
However, the quantitative effects of service user charges on behavior of
households are not yet well understood. While it is important to recognize
that the imposition of a charge will probably result in increased
littering, for example, it is also necessary to understand the quantitative
relationships between charges and patterns of household waste generation
and disposal in order to provide guidelines for setting prices in a way
that approaches system efficiency.
This report contains the results of case studies of the use of user
charges for collection and disposal of household solid waste in selected
communities. The project was comprised of five tasks: to develop a
descriptive list of communities employing a user charge system for
household solid waste collection and disposal; to group these communities
into general categories of user charge systems and to select one example
from each category for case study; to carry out case studies and collect
data on the operation of the user charge system in each city investigated;
to make an empirical analysis of these data regarding the effects of each
type of user charge system studied; and to report the conclusions of this
analysis.
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In completing the first three of these tasks we first made an
exhaustive search of previous empirical studies of solid waste management
systems in order to compile what we believe to be the most extensive list
to date of U.S. cities employing user charges. This list appears in
Appendix A of this report. We next grouped these cities into five
categories according to the type of user charge employed: flat-fee
systems, container-based (or capacity-based) systems, location-based (or
service-based) systems, metered-bag (or quantity-based) systems, and
systems involving combinations of the first four. Finally, we conducted
case studies of cities which employ each of these types of systems:
Burbank, California; Sacramento, California; Grand Rapids, Michigan; Provo,
Utah; and Tacoma, Washington.
Subsequent sections of this report contain conclusions of our
analysis of data from the case studies, recommendations based on these
conclusions, a review of the literature on and development of econometric
models of user charge systems, and the results, data, and description of
the case studies.
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SECTION 2
CONCLUSIONS
Two principal conclusions emerge from analysis of the user charge
systems studied in this project: first, demand for household solid waste
service, in most cases, seems to be highly inelastic with respect to price;
and second, interaction between households and solid waste management
systems is considerably more complex than a simple supply-and-demand model
might indicate. On the face of it, our findings of price elasticities not
significantly different from zero conflict with what has often been assumed
in theoretical discussions of user charges as well as with some earlier
empirical findings. Although the observations we report come from only
five cities, they demonstrate that managerial decisions to institute or to
modify user charges take place in social, political, and technological
contexts which may influence decisions more strongly than narrowly
conceived economic considerations. While this does not affect theoretical
questions of the efficiency of user charges, it is important to the solid
waste manager who must decide whether to implement such a system or to
modify an existing one.
EFFECTS ON DEMAND
A fundamental proposition of economic theory is that for any good
the quantity demanded declines as price increases. If this is true of
demand for solid waste services, then certain responses in household
behavior can be expected under a user charge regime where prices vary
according to quantity of service received. First, when the price of solid
waste service increases, households are expected to demand less service.
If price varies according to quantity of waste presented for collection, a
price increase is expected to result in presentation of less waste; if
price varies according to some other service component, a price increase is
expected to reduce demand for that component. We shall later show that an
increase in price of a non—quantity service component is also expected to
result in presentation of less waste. Second, when the price of solid
waste service increases, households are expected to alter their behavior
regarding waste generation and disposal. Household waste generation is
affected because an increase in the price of service reduces income
available for expenditure on waste-generating goods, although these goods
become less expensive relative to the price of solid waste service. Of
course, so long as the price of service is a very small portion of income,
the magnitude of the resulting income and substitution effects are
negligible. Household waste disposal practices are affected because
households have a choice between presenting waste for collection and
disposing of waste by alternative methods. They often have additional
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choices between levels or types of collection service and between
self-disposal methods which may be socially costly (e.g., littering) or not
(e.g., self-hauling). A price increase is expected to result in an
increase in quantity of waste self-disposed and in a decrease in quantity
of expensive types of collection demanded. And third, households are
supposed to adjust their behavior so as to equate the marginal utility of
various goods, which has the effect of efficiently allocating resources to
and between solid waste services if their prices are equal to their
marginal social costs.
Various elasticities of demand are relevant to the foregoing
description of household behavior. These elasticities express how the
choices made by households are related to factors over which they have far
less control, such as income and prices of solid waste service. In three
cities (Burbank, Sacramento, and Grand Rapids) we found significant income
elasticities consistent with previous findings (.22, .20, and .40,
respectively), and in Provo we found indirect evidence of positive income
elasticity. This indicates that solid waste service is a normal good for
which demand increases with, but not as fast as, income. We found no
evidence of significant price elasticity. Burbank employs a flat fee
structure, so we would not expect price elasticity there, since the
marginal price of service is zero. Adequate data for estimation of price
elasticities were not available in Provo, where price varies according to
service level. Grand Rapids and Sacramento employ fee structures which
vary according to quantity presented for collection. In neither of these
cities did we find price elasticities significantly different from zero.
In Tacoma, where price varies according to both quantity presented and
level of service demanded, we found that although significant price
elasticities for number of containers selected and for level of service
exist, price elasticity for quantity of waste generated is not
significantly different from zero. This seeming paradox may be explained
by recalling that self-hauling is a readily available substitute for
collection service in Tacoma.
These findings suggest that for the price levels we observed,
quantity of service demanded, and hence quantity of waste presented for
collection, is highly inelastic. This suggested conclusion must be
qualified by noting that any statistical test is a joint test of the
adequacy of the model and the parameters tested. Because we rely on data
collected for other purposes and employ surrogates for unavailable data,
Throughout this report we test hypotheses about elasticity at the 95%
level. That is, a finding of significant elasticity indicates a
probability of less than 5% that elasticity is in fact zero. In some
places a different level of significance is additionally indicated
(e.g., "significant at the 80% level," etc.). Caution should be taken
in interpreting the latter figures, of course, because if the level of
significance is chosen after regression results are known, then all
elasticities are significant at some level and hence rtb hypothesis
about significance has been tested.
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the presence of measurement error must be considered likely. Further, the
extent to which relevant variables are omitted and functional forms are
presumed raises the possibility of specification error. Therefore, our
findings are consistent either with there being no price effect on waste
generation or with statistical problems masking the true effect. Three to
five cities is not a large sample, but those we studied did provide a great
deal of variety of types of data. It is therefore interesting that the
finding of no price effect on quantity is so uniform.
One simple theory of economic behavior which might account for
price inelasticity of demand for solid waste services would be to invoke
the joint product aspect of solid waste and the time separation of
consumption decisions affecting solid waste generation and disposal. Most
household solid waste originates as a joint product of goods consumed by
households in the form of packaging, for example. This means that the
decision to generate waste is made simultaneously with the decision to
consume goods and services other than waste collection. Not only is the
price of collection of the waste component of most goods very small in
comparison to the price of the good, but also household decisions about how
to dispose of waste are separated in time from decisions affecting how and
in what quantity waste is generated. Testing hypotheses which follow from
this theory addresses questions related to product charges rather than user
charges. However, this may be an area which warrants further study.
EFFECTS ON COST
The main effect of user charges on the cost of solid waste
collection is the addition of billing and collection costs. Savas, Baumol,
and Wells (1976) [1] estimate billing costs at 3.1% of total collection
costs based on a sample of 39 cities with municipal collection and user
charges. Our findings concur with this figure. In four of the five cities
we studied residents are billed for collection together with electric
and/or water and sewage bills. The fifth city, Grand Rapids, employs a
metered-bag system, which involves no billing costs, but retail
distributors, who account for 62% of revenues, are allowed to keep 4% of
their sales receipts, and there are costs from maintaining stocks as well
as costs of refuse bags and tags. The cost of tags is about 3% of their
retail price; on the assumption that 97% of the price of tags constitutes
payment for service and that 97% of the price of bags constitutes payment
for service plus the value of the bag, the ratio of "billing" to total cost
of collection in Grand Rapids is about 5.5% (.03 + .04 x .62). In Provo,
billing appears to constitute about 3.2% of the price of residential
collection, and in Tacoma billing and collection is about 3.4% of total
collection expenses. No estimate of billing costs was available in Burbank
or Sacramento.
It is sometimes argued that the ratio of collection cost per
household to user fee is much nearer unity in user charge cities with
contract collection than in user charge cities with municipal collection.
Such arguments speculate that this is because cities with contract
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collection know how much collection costs, while cities with municipal
collection do not. Our study considered only cities with municipal
collection; we found great disparities in the amount of cost information
available, although it is not clear that these are related to the type of
user fee employed or to the presence of municipal collection. In three of
the five cities we visited — Provo, Burbank, and Sacramento — almost no
information was available about costs apart from items in the annual
budget. In particular, since costs and revenues from residential and
commercial collection were not available separately, there was no way to
estimate ratios from household collection. Rather more detailed accounts
were available in Grand Rapids, aided by the fact that there is no
municipal collection of commercial waste in that city, and the cost
accounting procedures in Tacoma were found to be excellent for our purposes
as well as those of the solid waste manager. In Grand Rapids the ratio of
collection cost to user charge is 2.22; in Tacoma the ratio is .96. It is
doubtful, however, that the difference between these two cities is due
entirely to differences in knowledge or record-keeping. User charges in
Grand Rapids are not intended as the exclusive means of financing
collection; they are in Tacoma. The performance of Tacoma's collection
system matches the ideal of a contract collector who balances his budgets,
despite its having municipal collection. It is also worth mentioning that
both Sacramento and Burbank had contract collection immediately before they
instituted municipal collection, but found the arrangement unsatisfactory.
In Grand Rapids the city government proposed contract collection in 1972,
only to have the proposal defeated in a referendum.
Finally, it is sometimes said that one cost effect of user charges
under a municipal regime is that households may pay more because user
charges, unlike city tax, cannot be deducted from Federal income tax. In
part this depends on answers to empirical questions that can be ascertained
for individual cities only by knowing income distribution, land ownership
patterns, and local tax structures for which we lack data in the cities
under study. But in part it also raises questions of the equity of user
charges, a subject beyond the scope of this study.
SOCIAL AND POLITICAL ISSUES
It appears, then, that user charges in and of themselves may not
have the economic effects, in the narrow sense, traditionally associated
with market institutions. Quantity of solid waste produced is highly
inelastic with respect to incremental pricing, although other services,
such as carryout service, act as normal goods with price elasticities of
around 0.3. Consequently, the littering effect of user charges is nil. On
the supply side of the market, user charges appear to be instruments of
financial or social policy which may be used in conjunction with other
techniques to minimize costs, but do not necessarily have this effect. We
shall now look at some broader considerations which we feel are important
as pointing to areas of further investigation.
The presence of user charges for solid waste collection is
associated with an ideological commitment to the judgment that individuals
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should "pay for what they get," as opposed to the notion that solid waste
collection is a public good to be paid for jointly through general
taxation. Solid waste personnel offered this idea as an explanation for
why collection was financed through user charges in every city we visited.
In terms of financial structures, both Provo and Grand Rapids budget solid
waste departments as enterprise funds which stand separate from other
municipal agencies; in Tacoma the department is a classified utility,
indicating an even greater autonomy. Thus there is an effort to make the
solid waste system as much like private business as possible within the
confines of government; user charges lend credibility to this effort.
Note, however, that although consideration has on occasion been given to
going over to private collection in each of these cities, the single
instance in which this was seriously attempted — Grand Rapids — resulted
in voter rejection. Paradoxically, the opponents of contract collection in
that referendum claimed that it would have been tantamount to socialism
because it would have excluded private haulers who lacked franchises.
We observed several instances in which the adoption of a particular
fee structure brought unexpected problems due to constraints of a political
or technological nature often quite independent of market factors. The
prime example of this is the effect of the 1974 oil embargo which cut the
supply of plastic bags in Grand Rapids, resulting in the substitution of
collection tags and ultimately the present combination of the two. This
could only occur with a metered-bag system. Prior to that, the adoption of
a metered-bag system in the first place was the logical consequence of the
referendum we have alluded to, an episode described in Section 8. Where
user fees vary according to number of containers, as in Sacramento and
Tacoma, households sometimes leave extra garbage alongside but not in
containers. Metered bags are the obvious solution to this problem, but a
metered-bag system cannot easily be operated in conjunction with carryout
service, which Tacoma has. In Section 9 we discuss how this problem was
resolved there. Problems may arise when political decision—makers come
into conflict with solid waste managers, as in Provo, where collection
personnel would like to eliminate carryout service, but elected officials
regard the service (perhaps erroneously) as benefitting the aged and
invalid and hence a matter of social policy. In most of the cities we
studied we found evidence of modifications of fee structures (in addition
to price changes) from time to time. User charges seem to facilitate this
process, which from the consumers' point of view amounts to modifying the
range of choices available. This was especially true of Tacoma and Grand
Rapids, whose systems offer the broadest range of choices; true to a lesser
degree in Sacramento and Provo, whose structures are less complex; and
least true in Burbank, whose fee structure is simplest. On the other hand,
complex fee structures may be difficult to modify dramatically. In both
Tacoma and Grand Rapids certain experimental changes have been considered
and rejected because of uncertainty about their effect. Finally, the
passage of Proposition 13 in California may well make user charges more
attractive to municipalities in that state who do not have them, and lead
to closer scrutiny of the levels at which fees are set among those who do.
All of these observations point to issues that managerial and political
officeholders should be aware of when contemplating user charges.
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Although we indicated above that user charges may have little
effect on illicit disposal which would increase overall system costs, we
feel there may be an indirect relation between user charges and littering,
as well as between user charges and resource recovery. Each city we
studied has felt the impact of such changes in legal constraints as
restrictions on disposal techniques and waste burning by households.
Tacoma experienced a 70% increase in the quantity of waste (legally)
self-hauled from 1968 to 1969, after Washington's environmental law took
effect. Sacramento hauls more yard waste now that private burning is
illegal. Tacoma has built a $2.4 million resource recovery facility which
will eventually recycle 80% of its disposal-site waste, and Provo is
considering a similar program on a more modest scale. In these cases there
did not appear to be a direct link between the existence of user charges
and the programs. In Grand Rapids, however, a clear link was present.
Because the city must compete with private haulers for customers, and
because the sale of metered bags is a source of revenue, the Environmental
Protection Department has become involved in a marketing effort. This has
benefited relations between the Department and shopkeepers on the one hand,
since their cooperation is necessary for the sale of metered bags, and with
the public on the other, who are informed about the availability of bags,
clean-up drives, and other facilities through what amounts to an
advertising campaign.
For the most part it is difficult to relate the foregoing
observations to particular fee structures, but one generalization that
seems warranted is that the amount of information available to solid waste
managers and other decision-makers increases with the complexity of fee
structure. If complexity is loosely defined in terms of the number of
choices typically available to households, Burbank is the least complex of
our cities, followed by Provo, Sacramento, Grand Rapids, and Tacoma, in
that order. In similarly loose terms, record keeping is least extensive in
Provo and Burbank, and most extensive in Tacoraa, with Sacramento and Grand
Rapids falling between. More complex fee structures of course require more
information for administrative purposes, and this information in turn can
facilitate management techniques such as cost accounting and use of
electronic computers which more accurately determine and predict costs. On
the other hand, user charges are clearly not a prerequisite for such data
collection, and in some cases expenditure on data collection may be a
luxury. In Grand Rapids, where only residential waste is collected by the
city, allocational efficiency between residential and commercial collection
is not at issue; and in Provo, the smallest of the cities we studied, the
benefits of sophisticated management techniques might not outweigh the
costs.
These social and political issues have a clear relevance for cities
engaged in deciding whether or not to implement user charges for the first
time, as do the administrative issues discussed below. Although we had
hoped to investigate the implementation process more closely, it proved
impossible to do so while at the same time studying examples of1" cities with
each type of user charge. Tacoma has employed user charges for over 50
years. Burbank, Sacramento, and Provo have employed user charges for as
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long as anyone in the municipal solid waste offices of these cities can
recall. Grand Rapids introduced user charges in the early 1970's. The
experience of Grand Rapids is discussed in some detail in later sections,
as are issues related to implementing changes in fee structures which took
place in the other cities.
ADMINISTRATIVE ISSUES
Although formulation of generalizations is difficult on the basis
of only one example of each type of user charge, the case study approach
nevertheless has illuminated certain differences in the administration of
each type of fee structure which warrant further study. Here briefly are
the outstanding administrative difficulties encountered with each fee
structure in common use, along with some of the solutions devised in the
cities we studied.
Location-based fee structures are particularly unpopular with solid
waste managers. In Provo the Sanitation Department would like to eliminate
the service because at current prices having it is thought to cost the
Department more than the added revenues it brings in. Finding a city which
has a variable fee based exclusively on pickup location was itself
difficult because many cities have abandoned location-based fees in favor
of flat fees in recent years. Carryout service remains labor intensive as
the technology of collection changes, with the result that the cost of
providing carryout service increases relative to other collection services.
In Tacoma, where a greater effort is made to see that the price of carryout
service covers its cost, we observed an historical increase in relative
price of the service and a decrease in percentage of households demanding
it. Hence, carryout service is not inherently inefficient in an economic
sense, but it may become so if social and political criteria replace
economic criteria in deciding whether to offer the service. This seems to
be the case in cities where solid waste managers oppose the carryout
option.
The principal administrative problem with the metered—bag system is
distribution of bags. In both Tacoma and Grand Rapids an initial attempt
was made to distribute bags through fire stations. In both cases this
solution was inadequate because of staffing problems at fire stations and
because people prefer the convenience of buying bags at supermarkets, drug
stores, etc. The problem with these is the reluctance of retailers to take
responsibility for distribution. Faced with these problems, Tacoma decided
not to adopt the bag system. Grand Rapids responded by giving distributors
(firemen and shopkeepers) a commission on sales. The cost of commissions
mitigates the advantage of eliminating billing costs, but carries the
advantage of improving relations with shopkeepers and customers.
Finally, we note two related issues associated with container-based
fee structures — stuffing, and large marginal increments. In both
Sacramento and Tacoma about three quarters of all households receive
single-can service, and fewer than three percent demand more than two cans.
Both cities also require households to pay for a non-zero minimum level of
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service (one can per week) . Households demanding more than the mandatory
level of service are therefore offered a price incentive to produce less
waste, while those demanding less than the mandatory level (evidently a
majority) are offered no such incentive, but rather are in a similar
position to households paying a flat fee. Households at the margin —
those whose waste generation fluctuates around one can or two cans per week
— may find the transaction cost of changing service levels so great that
they prefer to stuff cans more full. The metered-bag system eliminates the
transaction cost, but not the temptation to stuff bags more full. Data in
Sacramento and Grand Rapids were inadequate to test hypotheses about
stuffing behavior, but evidence from Tacoma indicates that stuffing did
occur there until the introduction of a charge for extra bags, which seems
to have solved this problem as fully as can be expected short of weighing
each separate container of waste. It has been suggested in a similar vein
that user charges may lead to increased use of household compactors or sink
disposals; we were unable to find data suitable for testing this
hypothesis.
10
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SECTION 3
RECOMMENDATIONS
The underlying question of this and many similar studies is whether
solid waste managers can affect quantity of waste generated through devices
such as user charges. In the next section we shall demonstrate that tests
used to estimate price elasticities of waste generation have been deficient
for one reason or another; we shall further demonstrate that there have
been no adequate tests of the hypothesis that user charges have an effect
on littering behavior. The lack of conclusive evidence, both from earlier
studies and from the present study, stems largely from the inadequacy of
available data in cities employing user charges. Our recommendations,
therefore, are twofold. First, there should be further clarification of
how user charges are related to efficient management of solid waste
systems, so that further research may be directed to areas which hold
greatest promise. And second, improved methods of testing economic
hypotheses about solid waste generation should be developed which do not
rely so heavily on the kinds of data bases which have previously been used.
11
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SECTION 4
THE ECONOMIC EFFICIENCY OF USER FEES: THEORY AND EMPIRICAL RESULTS
Attention directed toward the "efficiency" of alternative
organizations for the management of solid waste systems has resulted in a
large literature on the subject. For the most part, the literature reflects
concern with what we would call the "technical efficiency" of a solid waste
management system. That is, it addresses the question of the
characteristics of a solid waste management system that minimize the cost
to the solid waste management system itself. In this study, we have as our
concern a somewhat different concept of efficiency. We are interested in
the role of user fees in increasing "economic efficiency." Later in this
section, we define more carefully the concept of economic efficiency.
Basically, however, it refers to the minimization of costs to society
(where society may be defined in terms of the nation, the state, the city,
or any other group) while technical efficiency refers to a more narrow
concept.
One of the consequences of using economic efficiency as the
criterion for judging alternative structures of solid waste management
systems is that, a priori, no particular system organization can be called
"inefficient." For example, it is often said that backyard systems are
"inefficient." The reason for this statement is that, of course, more
collector time is required to serve as many households than in a curb/alley
system. What the statement neglects to consider, however, is that labor is
still required to move the waste from the backyard to the curb. From
society's point of view, there remains the question of the least-cost
method of getting the waste to the curb.
After defining and discussing the concept of economic efficiency,
we shall define alternative user fee structures and evaluate their
(theoretical) effects. Then we shall review some of the empirical
literature that has attempted to assess, from an economic viewpoint, the
effect of user fees on household waste generation. We shall be
particularly interested in findings in the following three areas: the price
elasticity of waste generation; the income elasticity of waste generation;
and the effect of user fees on littering behavior. We find in the
literature that, despite some claims to the contrary, there is no evidence
of any statistically significant price elasticity with respect to waste
generation. Rather, the tests used to estimate the price elasticity are
deficient for one reason or another. With respect to income elasticities,
there is some statistical support for income elasticities in the range of
.3 to .7. Finally, with respect to littering behavior, there is one study
12
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that finds a positive correlation with a particular surrogate for litter
and the presence of a user fee. However, for the reasons discussed below,
the evidence is sufficiently weak to say that there have been no findings
and indeed no test, of the hypothesis that the presence of user fees leads
to increased littering. Before reviewing the literature, it will be
important to define what we mean by economic efficiency and how it might be
measured.
ECONOMIC EFFICIENCY
In making decisions, all economic entities from households (or
individuals) to national governments perform, if only implicitly, some form
of economic analysis. Included in the analysis are the benefits (e.g.,
revenues in the case of a business) to be derived from a particular action
and the costs (e.g., fixed and operating costs) associated with particular
actions. This decision process can be used as the basis for the definition
of economic efficiency.
No matter what the economic unit, each decision involves the
comparison of costs and benefits to be incurred by the unit. In its
decision, the decision making unit considers only those benefits accrued
and those costs incurred by the unit. For example, an individual deciding
whether to litter does not (generally) take into account the costs imposed
on others in terms of reduced aesthetics. These costs (or benefits as in
the case of innovations) which are incurred by those outside of the
organization making the decision are called "external costs," "external
benefits," or simply "externalities."
The nature of these externalities leads to confusion when
discussing the efficiency of a particular organizational format for a solid
waste system. From the viewpoint of, say, the solid waste authority, one
particular set of actions may lead to the lowest cost from among a set of
possible actions. However, when the decision making organization is
expanded to include others (e.g., the residents of the city) a different
set of actions may become cost minimizing.
Throughout the report, we shall refer to the economic efficiency of
various user charge schemes. By this we mean the difference between the
benefits and costs of particular fee structures. Both the costs and the
benefits are measured in reference to society. In the case of solid waste
systems, society can be the local city or municipality. In general,
actions taken by the solid waste authority in one city do not affect
citizens in other areas. While not always true (for example, when the
disposal decisions by one city affect other cities through, say, effects on
river water quality), this can be taken for granted in the cases we will be
examining. What should be avoided, however, is the notion that only the
costs incurred by the solid waste authority are important or that they are
the only costs to be considered. In fact, as we shall see below, schemes
that are often thought to be inefficient from the agency's point of view
may actually lead to improvements in social welfare.
13
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The meaning of economic efficiency can be easily illustrated.
However, we first need to decide upon a method to measure the benefits
associated with particular decisions. For a business, the measure of
benefits is relatively straightforward. For the municipality engaged in
providing a service, however, the measure of net benefits is less clear.
Consumer's Surplus as a Measure of Benefits
Determining the economic benefits of a decision to a private
individual or firm is relatively easy. First, the revenues are computed,
then the costs. Finally, the costs are subtracted from the revenues to
determine net financial profit on which a decision can be based. Because
so many activities of governmental units affect people in ways not measured
by market transactions, some other method for measuring the net benefit of
a course of action must be developed.
The measure that has been developed for use in social cost-benefit
analyses is that of "consumer's surplus." Briefly, consumer's surplus is a
measure of the individual's willingness-to-pay for a particular service.
For example, a particular individual may be willing to pay $6.00 per month
for carryout service. The net consumer's surplus, or what the consumer
would be willing to pay less what he actually pays, is the measure
traditionally used in cost-benefit analyses. Continuing the example, if
the service were provided at no additional charge to the resident, then the
consumer's surplus would be $6.00 (i.e., $6 - $0). This measure of
willingness-to-pay can be directly related to other concepts in economics
— concepts which can be measured more easily than willingness-to-pay.
To begin, let us examine the economic benefit that a typical
consumer derives when he purchases, say, carryout service. Suppose that
the price of carryout service is $6 per month. We may infer that each
purchaser of carryout service is willing to pay at least $6 to have it
rather than to go without it. Actual expenditures on such service thus
represent a lower bound on the sum of purchaser's willingness-to-pay to
have the service. We can infer from their behavior that they are willing
to pay at least this much. In fact, of course, they may be willing to pay
much more.
To be able to infer actual willingness-to-pay from observed
behavior, we clearly require more information. Exactly the needed
information is provided by the fact that the marginal buyer (i.e., the
buyer who would not buy if the price were any higher) is willing to pay
exactly the price he pays and no more. By using this fact, we can find a
differential equation representing market behavior which, in principle, can
be used to measure willingness-to-pay exactly.
Letting x denote the total number choosing carryout service per
unit of time, and letting p(x) represent the price corresponding to x,
we note that marginal willingness-to-pay when (say) XQ units are
purchased is:
14
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(1)
where W is willingness-to-pay. Total willingness-to-pay may then be
found by solving this differential equation for W, which yields
X0
J p (x) dx = W (2)
0
Equation (2) provides the fundamental relationship that is used to
translate the willingness-to-pay principle of benefits measurement into
practice. What equation (2) says is that, if we can find the market
relationship between price, "p," and number choosing the service per unit
of time, "x," (this is the relationship we have denoted by p(x)) we will
have exactly the relationship that we need to measure willingness-to-pay.
This relationship between p and x is nothing other than the
"market demand function," which relates price and quantity demanded for
goods and services. Of course, variables other than price and quantity are
important in this relationship. For example, we know that income,
household age, and many other factors influence demand. These other
factors are taken into account in the analysis, as will become clear in
later chapters in which we use equation (2) to make actual benefits
estimates. For ease of exposition and notation here, however, we shall
retain our convention of expressing explicitly only price and quantity.
Once we have the demand function (we shall illustrate in subsequent
sections how it is estimated), we obtain an estimate of the sum of
individuals' willingness-to-pay to have an activity with an output level of
(say) Xfj per year by integrating under the demand function up to X«, as
shown in equation (2) above.
This procedure for estimating individuals' willingness-to-pay may
be easily illustrated. In Figure 1, we have drawn a demand curve. At
quantity XQ, total willingness-to-pay is given by the area under the
demand curve up to XQ, which is the shaded area in Figure 1.
Frequently in cost-benefit analysis, willingness-to-pay is measured
net of any charges levied upon customers. When this is done, the result is
called "net willingness-to-pay," or more frequently, "consumers' surplus."
The "consumers' surplus" measure represents what customers would be willing
to pay over and above what they do pay. This concept is illustrated in
Figure 2, where it is assumed that a price of PQ is charged for each of
the XQ units purchased. The shaded area in this figure represents
consumers' surplus (or net wi11ingness-to-pay). The total
15
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Price
Per Unit
Market Demand
Curve
Qua_ntity Per
Unit Time
Figure 1. Illustration of the calculation of total
willingness-to-pay.
Price Per
Unit
o
Quantity P-
Unit Time
Figure 2. Illustration of the calculation of consumers surplus.
16
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user-expenditures that have been netted out of willingness-to-pay are given
by the rectangle OP0AX0. (Compare Figure 2 with Figure 1.)
A Graphical Depiction of Economic Efficiency
The measure of consumers' surplus described above is, of course,
only one half of the information required to assess system efficiency. The
other part is the cost of implementing the system. Still, it is possible
to depict graphically the assessment of economic efficiency for a
particular service.
Consider, for example, the question of carryout service. If refuse
collection services are provided by the city, it has three basic
alternatives: provide the service to all residents; provide the service to
no residents; or charge a fee for the service and let the individual
resident choose. The comparative benefits can be assessed using the
information presented in Figure 3.
Price [A.
B
• AC
O
W
N
Figure 3. Evaluating economic efficiency.
In Figure 3, the demand for carryout service is drawn as a linear
function of price only for simplicity. It is assumed that at zero price,
all residents will demand the service. While this may not be true (if, for
example, some residents feel there are additional costs to having
collectors enter their yards) nothing in the analysis is altered by this
assumption. Suppose that, if the service is offered for a fee, the fee
(p) will be set to equal the average cost of providing the service.
(Average costs are drawn as constant in Figure 3, implying that average
costs equal marginal costs. We have excluded from the figure any
administrative costs which might be associated with the use of a fee.)
17
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Consider now the net social benefits associated with the three
alternatives outlined above. The first alternative, providing the service
to everyone without an explicit charge, is equivalent to charging a zero
price. Therefore, the demand is 100%. The total willingness-to-pay (i.e.,
the area under the demand curve) is equal to the area OAD. The total cost
of providing the service is N x AC where N is the number of residents.
(There are no administrative costs since the service is provided to
everyone and no monitoring is required.) Therefore, the net benefits to
society is equal to the total benefits less net costs and depends on the
relative size of the two shaded areas in Figure 3.
Now consider the net social benefits of not providing the service
at all. In this case the net benefits are zero — no costs and no
benefits.
Finally consider charging a price p (assumed to be equal to
average cost). The percentage choosing the service will be W. The total
benefits are OABW. The total costs are AC x W. The net social benefits
are the vertically shaded area pAB. Note that the net benefits associated
with this arrangement are greater than those associated with providing the
service to everyone by the amount WBD (the horizontally shaded area)
neglecting the administrative costs.
Similarly, the net benefits associated with the charge system are
greater than providing no service (again neglecting administrative costs)
by the amount pAB. The question of the best choice depends then on the
level of administrative costs. If they are less than pAB, a charge system
is, on net, beneficial when compared to providing no service. If they are
less than the area B(AC)D, then the charge system has greater net benefits
than the free provision. Of course, the level of the administrative cost
and the relative magnitude of the shaded area are empirical questions.
Suppose, however, that in the process of trying to evaluate the net
social benefits associated with a particular fee structure, that the
analyst is missing some of the crucial information; e.g., the actual demand
curve for the service. Is there some necessary condition that must hold
for a user fee to improve economic efficiency (not equity)? The answer is
yes. Basically, it must be true that the demand curve has a negative
(i.e., non-zero) slope. Consider what happens if the slope (or elasticity)
is zero in the relevant range. If the elasticity is zero, the waste
presented for collection remains the same and, therefore, total costs
remain the same. All that happens is that there is a transfer payment from
residents to the solid waste system, and under traditional cost-benefit
analysis such a transfer has no effect on benefits.
A CLASSIFICATION OF FEE STRUCTURES
As discussed above, the approach followed in this study has been a
case study approach of five cities each of which has a different type of
fee structure. By "different" we mean merely that the resident in each of
these cities is faced with a different set of choices concerning the
18
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disposal of the solid waste generated within the household. Before
discussing the findings in each of the five cities, however, it is useful
first to discuss the classification systems used to develop the five
different fee structures. Once this has been done, we can analyze what the
qualitative effect of a fee increase would be.
The Categories of Fees
The nature of the fee systems used by communities that employ user
fees for the collection of residential solid waste vary greatly. However,
within that variation, there is one characteristic which is (a) easy to
identify, (b) useful in the development of hypotheses concerning
behavior, and (c) restricted in relevant possibilities. This
characteristic is, therefore, a sound one to use for the classification of
user fee systems for the purposes of selecting the case study cities. This
characteristic is the set of decision choices the resident has for the
disposal of solid waste. Although there may be others, we have selected
five different possibilities as including (hopefully) most of the fee
structures currently used. Below we identify and define each type.
Uniform Fee Structure—
The first, simplest, and by far the most widely used fee system is
the uniform or flat fee structure. In this system, the resident (assuming
service is mandatory) has no choice over his disposal options. (By choice,
we mean an alternative offered by the provider of the service. Certainly,
every resident can choose to dispose of waste illegally.) The goal of this
type of fee structure is basically one of revenue raising. In other words,
the flat fee service is an alternative to general tax revenues for
supporting the solid waste system. Burbank, California, is an example of a
city which uses a flat fee structure.
Container-Based Structures—
A more complex system is one in which the resident has the option
to choose the number of containers (i.e., the capacity) for waste for
pickup. While it is not necessary to assume that the resident must remain
within that constraint once and for all, it is assumed that there are some
transaction costs associated with frequent changes from one number of
containers to another that are sufficiently large to discourage such
changes. Sacramento, California, is an example of a city which uses a
container-based fee structure.
Service-Based Structures—
A third type of fee structure is one in which the resident can
choose alternative levels of service. Service levels can be defined in
terms of the point of collection (curbside or backyard) or frequency of
collection (e.g., weekly or semi-weekly). Most cities that have a
service—based system provide a choice in terms of location. In this
system, the number of containers that can be presented is specified
(although it may be unlimited)- Provo, Utah, is an example of a city which
uses a service-based fee structure.
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Metered-Bag Systems—
Metered-bag systems are those systems where the resident presents
all waste to be collected in a specially-marked bag. This system is quite
similar to the container-based system discussed earlier. The difference,
an important one, is that the resident can alter, at no transaction cost,
the number of containers presented each time. While this system is not a
true quantity-based system, where the resident pays for collection
according to the weight or volume of the waste presented, it is a step
closer to that ideal because of the freedom offered at each collection
period. Grand Rapids, Michigan, is an example of a city which uses a
metered-bag system.
Combination Systems—
Finally, some municipalities employ fee structures that are
combinations of two or more of the other systems. For example, in Tacoma,
Washington, the resident chooses both the number of containers and the
level of service (in terms of pickup location). This type of system may be
expected to be more expensive yet more flexible than a system offering only
one choice. Tacoma, Washington, is an example of a city which uses a
container- and service-based fee structure.
Analyzing the Effect of Different Fee Structures
In theory, one could qualitatively analyze the impact upon resident
behavior from the change in the level of the fee for any type of fee
structure by first developing a utility maximization model with each of the
fee features. Then, comparative statics could be used to determine the
direction of change in the amount of waste generated. Such an approach was
used, for example, by Kenneth Wertz (1976) [2] when analyzing the effect of
price and service level upon waste generation behavior. The problem with
such an approach is that, of necessity, most of the fee systems that exist
include in their rate structures such large discontinuities (e.g., the
number of containers) that applying such methods may not be particularly
fruitful when examining actual systems. The results of the Wertz analysis
are helpful, however, in that they provide a check on a more simple
approach to the qualitative determination of the effects of a fee change.
Consider, for example, an "ideal" fee structure where the resident
pays by the pound (or cubic foot, or both) for the amount of waste
disposed. Further, suppose that he is allowed to choose the point of
collection (for a fee). A system of demand equations that might be
expected to model such a decision process adequately would be like the two
equations:
- f (PW, y, s) (3)
20
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+
s = g (PO» y) (4)
where q represents the quantity of waste disposed, p the price per
unit of waste, y is disposable income (after payment of any flat fee for
disposal), s is a measure of service level (e.g., the number of feet from
the curb), and pg is the unit price of the service level. Signs above
each of the independent variables in both equations indicate the effects we
would expect changes to have. For example, following elementary theory we
would expect that an increase in the price of either quantity or service
would decrease the demand for the respective good. Similarly for income.
In equation (2), we show that the effect of an increase in service level
leads to an increase in the amount of waste generated. This is consistent
with the findings of Wertz. Intuitively, its justification is quite
straightforward. The disposal of residential solid waste requires the
labor of two different units: the collector and the household. Use of
household labor is costly just as is the use of the collectors' labor.
Increasing the service level while holding all other variables constant
means that the cost to the household of presenting an additional unit of
waste has fallen. We would therefore expect to observe an increase in the
amount of waste presented.
We can now use equations (3) and (4) to determine the effect of a
change in the price of either quantity or service level. The effect of a
change in the price of quantity is known directly from the demand curve.
The effect of a change in the price of service level upon the amount of
waste generated depends on the indirect effect of the service price on the
level of service. Thus,
= |SL . f£_ < o (5)
ds dp
The effect of an increase in either price therefore is expected to be a
decrease in the amount of waste presented for collection. These results
were for the ideal system. We now evaluate the five types of fee
structures described above.
Uniform Fee—
In the case of the uniform fee, the only equation is:
q = f (y) (6)
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Note that the only variable influencing the amount of waste presented for
collection is the disposable income. This fact has an important
implication that has, on occasion, been overlooked in previous analyses.
Suppose we define,
y = m - p (7)
where m is household income before the fee and p is the flat fee. It
follows immediately that:
= -1 and
dp am
Combining (6) and (7) and substituting these results, we have:
q = f(y) = f(m - p)
dp dy dp
.
dy dm
= -I3- (8)
p dm
That is, the effect of an increase in the flat fee is equivalent to an
equal decrease in income. The income elasticity of quantity of waste
generated is:
. or _ = .
m am q dm m m
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Multiplying both sides of (8) by p/q and substituting this result, we have:
. _ . 2
dp q 9m q
_ _ _a . £
dp/p 'mm q
77 = - rj -2. (Q)
' p mm v 7'
As indicated in equation (9), this is the elasticity of quantity of
waste generated with respect to the flat fee. This means that if the
income elasticity of waste generation is on the order of .30, then the
elasticity with respect to a change in the level of the flat fee is
insignificant.
Container-Based Fees—
The container-based system differs from the ideal because the
resident does not face an explicit charge each collection. Rather, he
faces what is essentially a capacity constraint. Therefore, his choice can
be modeled as:
q = f (c, y) (10)
= g (pc, y) (in
Again, the signs above the independent variables represent our assumptions
about the effects of changes in these variables on the amount of waste
generated. In these equations, C represents the number of cans selected.
Ignoring the discontinuity of the number of cans, we can analyze the effect
of an increase in the price per can on the amount of waste generated. It
is:
£
-------
The product of the two terms in (12) is negative by virtue of the effect of
price on the number of containers selected. However, if the first factor
is less than one, it can moderate any inhibiting effect of the price
change. The factor 9q/9c measures the density change resulting from a
change in price. That is, if 3q/9c is less than one, part of the effect
of the change in price is to have residents packing each container slightly
more full. This would be a perfectly rational thing to do since, as the
price of an additional can increases, the cost of the residents own labor
in the preparation of his waste for collection falls.
This suggests that one of the weaknesses of the container-based
system is that it provides a means for the resident to avoid the full
effect of the price increase. Of course, the quantitative importance of
this effect is an empirical question.
Service-Based Fee —
The model for a service-based fee would be
q = f (s, y) (13)
s = g (ps, y) (14)
Here we are interested not so much in the effect of changes in the level of
the fee on the choice of service level by the resident, which we know must
be negative, but the effect on the quantities of waste generated. But this
is just,
Therefore, we see that the effect is dependent upon two quantities — the
effect of price on service and the effect of service on quantity.
The Metered-Bag System—
The bag system differs from the capacity-based system in two
important respects. First, the resident is free to put out a different
number of bags (including zero) each collection period. The second is the
fact that each bag used includes the cost of the bag. With a container,
the container was not "consumed" when filled (although it must eventually
be replaced). With the bag it is. Therefore, the system of demand
equations for this system is
-f- +
q = f (pB, B, y)
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B = g (pB, y) (17)
where pg is the price of a bag. Note that the price of bags affects the
demand for two separate goods, the collection of waste and the number of
bags. Therefore, we would expect the effect of a price change to be
somewhat more complicated. In fact, it is,
+ £L • B_ < o (18)
Combination Systems —
The combination systems combine the effects of two or more of the
types of fee structures. Assuming that the combination is for the number
of containers and the location for pickup, the model for analyzing the
effect of fee changes might be
q = f (c, s, y) (19)
= g (Pc, y, s) (20)
s = h (p , y, c) (21)
S
Measuring the effect of a change in the price per container of waste
collected is now complicated by the fact that the container fee will affect
both the number of containers selected and the level of service chosen.
The effect of a change in the fee for a container is
(22)
3pc 5c 3pc 3s
the effect of the change in the fee for level of service is
3s
25
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We see that in both cases the impact of an increase in either fee is to
reduce the amount of waste presented for collection.
We now review some studies of the effect of user charges on
residential solid waste concentrating on those which are empirically
oriented. The purpose of this is to: (1) provide a background for
comparison of our results, and (2) assess the previous findings which have
been used in discussions of policy related questions.
REVIEW OF THE LITERATURE
While there have been innumerable studies of the cost of providing
solid waste collection services (along with many prescriptions for
decreasing agency, though not necessarily social, costs with particular
technologies), there have been relatively few studies that empirically
address the effects of pricing on residential solid waste behavior.
Without such information, however, analyses of the "optimal" type of fee
structure are impossible to perform in terms of cost-benefit analysis. The
purpose of the following is to provide a brief, and admittedly selective
review of the empirical literature that has developed in this area. The
reason for focusing on the following studies is that they form the basis
for much of the current discussion regarding solid waste pricing, and
because they provide numerical estimates of elasticities and other economic
data that have come to be relied upon in policy—making discussions. It is
important that they be assessed critically in order that due faith be
placed in the results.
Wertz
The Wertz (1976) [3] article is primarily concerned with the
development of qualitative implications for the effects of changes in
service fees or quantity fees. However, throughout the paper are several
brief examples which provide some support for the theoretical propositions
developed. The ones which we are primarily interested in are those
relating to income elasticities, price effects, and service level effects.
With respect to income effects, Wertz used data from 10 suburbs of
Detroit which had similar financing and collection policies. Using a
linear functional form for the demand curve, Wertz found implied income
elasticities of .279 and .272 (depending on the actual sample used) . In a
rather odd conclusion, Wertz states "The foregoing mixture of theory and
observation suggests the expected: residential refuse quantities should
decline as t (price) increases."
The "foregoing mixture" of theory appears to be the derivation of
the usual classification of effects into substitution and income effects.
This is perfectly straightforward following usual demand theory. What is
not clear is how the observations about income elasticities from cities
employing no incremental user charge can support expectations about price
elasticities.
26
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Following this conclusion, Wertz examines some additional evidence
bearing on the price effect. He cites the fact that per capita generation
in San Francisco, which employs an incremental user fee, was substantially
less than "for all urban areas where general financing prevails." As Wertz
notes, there are too many variables to place too much belief in the
numerical accuracy of the implied price elasticity of .15. In addition to
the lack of data, moreover, the comparison was between two different years
made comparable by applying "an average growth rate." Also, the growth
rate was applied to a figure which was composed of measured tons and
estimated (from volume) tons.
The estimates Wertz provides on income elasticities appear to be
consistent with the findings of others and are based on statistically sound
methods. However, the empirical evidence of a non-zero price elasticity is
not soundly based.
Tolley, Hastings, and Rudzitis
In an updated version of an earlier study, Tolley, et al. [4],
provides estimates of income elasticities based on cross-sectional data
from several wards in Chicago. Because waste collection services are
financed out of general revenues in Chicago, no estimates of price
elasticities were possible.
The findings of Tolley, et al., were consistent with the earlier
work. Namely, the estimated income elasticity in Chicago appears to be .3
and .7, depending on the season.
Again, these findings are consistent with those of others: there
is an income elasticity which is positive but less than one.
McFarland
One of the most oft-cited studies concerning the existence of a
significant price elasticity is the study by the University of California
on solid waste practices in that state [5]. Chapter IV, which formed the
economic basis of the report was authored by McFarland and has come to be
known by that name. We will therefore continue to use it.
The approach McFarland used to estimate the price elasticity was to
apply ordinary least squares to the following equation:
ai a2 a3
Qd = aQ Xj X2 X3
where Qj is the annual per capita quantity of waste generated, Xj was
average revenue, %2 was Per capita income, and X3 was population
density. The results of the regression were:
27
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In Q = 6.9 - . 455 InX + . 1781nX, - . 2121nX,
d (3.2) L (.36) " (1.5)
where t-statistics are noted in parentheses. McFarland used this result to
state: "This indicates that people will definitely respond to price
incentives or disincentives in their use of the service." There are,
however, several problems with these results — problems which essentially
vitiate the conclusions.
First, the price proxy used was average revenue. Unfortunately,
McFarland did not specify the actual 13 cities used in the analysis.
However, since only two out of the 58 in the entire study appear to employ
an incremental user fee, the majority had to be flat-fee cities. In that
case, there is no price elasticity. A second problem is that many flat-fee
cities impose quantity limits. This means that service levels are not held
constant.
Finally, there is undoubtedly a good deal of simultaneity. Most
municipal systems are designed so that the flat fee is, at least somewhat,
related to system costs. But system costs are related to the level of
waste collection. Therefore, by not including a second equation in the
model and using simultaneous methods, the results do not have the usual
desirable properties.
These problems may also have led to the finding of an income
elasticity not significantly different from zero, a finding which conflicts
with other studies.
McFarland goes on in the analysis to discuss the effect of
incremental fees on litter — the primary externality to be expected from
the imposition of a user fee. To do this, she classifies cities as
internalizing or externalizing depending upon their mode of financing and
quantity limitations. Externalizing cities are those cities not "providing
unlimited or generous service at zero marginal costs." She then found a
significant difference in the costs associated with the solid waste
management system between the two types of cities. She attributed this
difference to the extra costs of litter cleanup in the externalizing
cities.
The results of this analysis have been criticized elsewhere (see,
e.g., Goddard, Hudson) [6], [7]. However, an important point that we have
not seen mentioned is that flat-fee cities imposing quantity limits (while
perhaps by definition incremental fee cities, i.e., with an infinite
incremental fee) are not what is generally meant by incremental fee cities.
The empirical results of McFarland, therefore, do not appear to be
sufficient evidence of the existence of non-zero price elasticities either
directly from demand equations or indirectly through litter effects.
28
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Stevens
In an unpublished research paper, Barbara Stevens has presented
some results, both theoretical and empirical, concerning service level
pricing [8], In the theoretical analysis she extends the results of Wertz
by explicitly including frequency of collection and location of pickup
simultaneously. She concludes the theoretical section with:
All mandatory collection systems, whether the fee is
explicit or implicit, cause the consumer to generate less
wastes when prices are increased (provided that either the
price elasticity of demand for service or that the relation
between refuse generation and income is small) and cause
the consumer to generate more wastes in response to a
costless increase in service level. Only the service level
fee pricing scheme has the joint advantage of encouraging
consumers to value goods implicitly according to the
disposal cost of their refuse component and of
implementability.
It is important to note her qualifier "mandatory." She goes on to
assert:
When a pricing scheme does not requir-e mandatory
participation of households, none of the above conclusions,
with respect to any of the pricing models, can be stated
with any confidence. In any such non mandatory
arrangement , increases in the price of refuse collection
services can lead to a decrease in the proportion of
households selecting organized collection services. The
households opting out of the system may choose to self haul
to legal disposal site (sic), to dump in illegal locations,
to burn refuse, etc. Any or all of these alternative
disposal technologies may result in a perceived increase in
disposable income and a consequent increase in refuse
generation. In addition, some of these alternate disposal
technologies may result in increased total costs of
collection and disposal of refuse to be borne by 'the
society as a whole.
The reason for such a counterproductive effect, at least in terms of
economic theory, is unclear. For if the resident could make use of the
alternative facilities at lower cost than at the new user fees, the same
alternatives were available at the lower fee level. That is, let p(Q) be
the price of disposing of an amount Q through "conventional" means. Let
L(Q) be the "price" of some alternative form of disposal (e.g.,
littering). Then the resident will choose quantities Qc, Qj^to be
disposed of conventionally and littered so that
29
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TC = p(Q)Q + L(Q)Q
c L>
is the total cost of waste disposed. Total waste generated is just Qc +
Now suppose there is a price increase in conventional disposal. Regardless
of the functional forms of the price functions p(Q) and L(Q) , the total
cost cannot fall. (It is true, however, that there will be more waste
disposal through illegal means, and, therefore, social costs may increase.
The difference between mandatory and non-mandatory systems is that,
under the latter, the resident has more substitution possibilities
available. Thus, in such systems, we would expect the inhibiting effect of
any price increase to be more moderate than with a mandatory system.
With respect to the empirical portion of the paper, Stevens
provides a useful test of the efficiency of service-based fee systems.
Stevens estimates three demand curves: one for waste presented for
collection; demand for service frequency; and demand for service location.
Using the latter two, we can estimate consumers' surplus and, hopefully,
say something about the effect on economic efficiency of service-based
plans.
For example, the demand curve for location of pickup is
BY = 271 + . 00297Y - 142. 9QH + . 381FRE - 3. 279P,,,, - . 0282DEN (24)
-D Y
where BY = percent of households selecting backyard service
Y = mean annual family income
QH = annual tons of refuse per household
FRE = percent selecting higher frequency of service
P = price (per month) for carryout service
DEN = persons per square mile
Significant explanatory variables were income and price.
Substituting the mean values for each of the variables except price
into (24), we get
BY = 34 - 3.279PT3V (25)
Equation (25) can be used to estimate consumers' surplus. First note that
(25) is linear and therefore consumers' surplus is just the triangle
bounded by the demand curve, the price, and the price such that BY is
just zero. That is,
CS = BY x (p - PRV) x -5 (26)
Vimax BY
30
-------
where Pmax is the price such that BY is just zero. From (25) p
$10.36. Using mean values for BY and p , we find
CS = (.01 x 28) x (10. 36 - 1. 88) x . 5 = $1. 18/month = $14. 14/year
Note that BY is measured JLn percentage points (of households), so it is
necessary to multiply BY by .01 to compute consumers' surplus per
household.
To determine whether the offering of such a service leads to an
increase in efficiency, we need to compare this benefit to the
administrative costs of providing the optional service (we assume that the
price includes the additional collection costs). While there are no data,
we can infer likely effects. Stevens' sample generated 1.71 tons per
household annually. Assuming average collection and disposal costs of $30
(see Savas) [9], total annual costs per household would be $51. The
benefits of $14.14 annually per household represent 28 percent of this.
Edwards and Stevens [10] found administrative costs to be between 3% and
18% of collection and disposal costs. Thus, it appears that providing the
optional service increases economic welfare.
Because of the assumptions necessary to estimate consumers' surplus
in this case, we would not want to say that the Stevens' results can be
used to infer that service—based plans increase economic efficiency. We
would argue, however, that they are highly suggestive.
2. An alternative method which produces the same result is to integrate
the function given by equation (25), thus:
10.36
CS = .01 f (34 - 3.279PBY) dPBy
1. 88
10.36
= .01 [34PBy - 1.64P2BY J
= $1. 18 per month = $14. 14 per year,
31
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CONCLUSIONS
Based on theory and previous studies relatively little can be said
about the magnitudes of price and income elasticities. There exists no
statistically valid evidence of a quantity effect. Stevens' result
suggests that price is important to the resident in selecting service
levels.
In the next five sections, we present our analyses of the effects
of five different types of fee structures. As with the studies reviewed
here, the results (or lack of results) depends on the amount and quality of
data available.
32
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SECTION 5
BURBANK, CALIFORNIA
The City of Burbank, California employs a flat-fee system for
residential solid waste collection. Service from the City is mandatory for
all residents in single family dwellings (60% of all housing units) and is
available to residents in multifamily housing. Service is offered
once—weekly and there is no limit to the amount of waste that may be
presented for collection provided only that it is properly packaged. (See
the Figure 5 for the relevant regulations.) These two factors mean that
City residents have no incentive either to reduce the amount of waste
generated or to engage in illegal disposal methods in response to a fee
increase.
As we show below, the income elasticity estimated for Burbank,
given the assumptions and model specification .discussed below, is
consistent with previous findings. Recall that with a flat fee there is no
non-zero price which the resident faces but rather a one-time charge that
operates through an income effect. Therefore, there is no price elasticity
to be estimated.
In this section, the features of the solid waste management system
for Burbank are first described and the data that we were able to collect
are discussed. We then describe the theoretical model used in conducting
the empirical analysis followed by the empirical results from the Burbank
study. Finally, the conclusions that may be drawn from the Burbank case
study are presented.
THE BURBANK SOLID WASTE SYSTEM
Collection of residential solid waste in Burbank is accomplished
primarily with side-loading trucks using one-man crews. Three two-man
crews are used in areas where, for safety reasons, the one-man crews would
be undesirable. Waste is hauled to and disposed of at a sanitary landfill
located on city property in the Verdugo Mountains. (The Verdugo Mountains
form the Eastern boundary of the city.)
Billing for the city service is done through the city-owned Public
Service Division which operates the electric utility for the city. The
refuse bills are sent out along with the electricity bills to each
customer. The equipment for the refuse department is leased from the
Equipment Division, which, like the Sanitation Division, is .part of the
Public Works Department.
33
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Collection of Residential Solid Waste in Burbank
The collection of residential solid waste in the City of Burbank is
accomplished by fourteen side-loading refuse trucks using one-man crews and
three rear-loading trucks using two-man crews. The rear loaders are used
on routes where, because of narrow streets, the truck would be required to
back out. The switch to one-man crews was made in 1971 after a study of
the existing collection practices.
In addition to residential collection, the city provides service to
commercial establishments and multifamily housing if requested by the
customer. City offices and local schools are also served by the Sanitation
Division. As of February 3, 1978, the city was serving 1,188 commercial
accounts.
There are approximately 31,000 residential customers, of whom
18,000 are in single-family dwellings, and the remaining 13,000 in
multifamily housing. In 1970, there were a reported 35,000 customers
although this figure was not broken down by type of housing.
Service is provided to each residential customer once per week.
The particular day of pickup is determined by the customer's location in
the city. The map shown in Figure 4 provides the schedule. The
regulations for refuse service are shown in Figure 5. If a particular
customer is in violation of these regulations, he is notified by the
attachment of a red tag to his container.
Collection in Burbank is based on the curb/alley type of system.
With the one-man crews, the collection system in Burbank has many of the
features normally associated with a technically efficient refuse collection
system. However, lack of data prevents us from estimating cost functions
for either collection or disposal.
The refuse collectors in Burbank are not unionized. They work on a
group incentive basis. That is, when each crew is finished with its route,
it is dispatched to help crews that have not finished their routes. In
this way, all personnel leave at the same time. The number of personnel
for residential solid waste collection totals 22.5 and has remained at this
level since 1971. This figure is composed of:
One-man operators 14
Two-man operators 6
Foreman 1
Superintendent 1
Clerk .5
Disposal of Residential Solid Waste in Burbank
The residential solid waste collected in Burbank is hauled to a
sanitary landfill located on city property in the Verdugo Mountains. The
approximate location of the current landfill site is shown on the map in
34
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Figure 4. Bui-bank residential refuse routes.
35
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REFUSE COLLECTION RULES
COMPOSITION OF REFUSE: Refuse includes gar-
den and grass trimmings, wrapped garbage, news and waste
paper, tin cans, wood and glass. Dirt and rocks are acceptable
for collection only if the weight limit of 60 pounds is not
exceeded. No item shall exceed 2'x2'x4'. Break up if larger.
STORAGE: All refuse may be stored together in the same
containers. Trees and shrubbery trimmings and lumber must be
tied securely in bundles. Bundles must not exceed two feet in
diameter and four feet in length. Tree limbs must not be larger
than 6 inches in diameter or four feet in length. Large tree
stumps must be cut in two-foot lengths. Newspapers must be
tied or placed in containers. AH grocery sacks must be placed
in tapered containers.
DO NOT OVERLOAD
CONTAINERS: Refuse must be placed in 20 to 45 gallon
tapered container, with handles, constructed of durable metal,
pressed fiberboard or heavy-duty plastic material. When manure
is put out, tapered durable metal containers are required. All
containers must not exceed 60 pounds in weight when filled
and 20 pounds when empty. Any steel drums or cardboard
barrels of type or size will not be emptied because they are
unsafe for collectors to lift. Cardboard and wooden boxes or
small containers are not acceptable as permanent containers and
will not be emptied except buckets or pails with handles con-
taining rocks or dirt.
PICK UP TIME: Containers should be placed at the
curb or alley the night before the scheduled collection day and
taken in within 12 hours after they have been emptied. Con-
tainers should be placed away from parked cars and near
driveways when possible.
HOLIDAYS: Refuse will be picked up on all scheduled
days, including holidays.
If container is ro be thrown away, please put note on or notify
the driver.
GARBAGE
1. Must be wrapped securely in paper. Plastic may be used.
2. Must be placed in tapered container with tight-fitting cover.
3. When securely wrapped, garbage may be placed in refuse if
container has tight-fitting cover.
4. MATERIALS NOT ACCEPTABLE FOR COLLECTION:
(a) Materials unsanitary and offensive
(b) Weight is not to exceed 60 pounds
5. Dog droppings must be wrapped or placed in a plastic sack
inside a container.
YOUR COOPERATION IS DESIRED IN ORDER TO ASSURE YOU OF
SAFE, ECONOMICAL AND EFFICIENT REFUSE SERVICE. PLEASE
CORRECT THE FOLLOWING ITEM WHICH HAS BEEN CHECKED:
1. NEW CONTAINERS REQUIRED:
(a) Container hazardous to workman
(b) Container worn out
(c) Container too small
Minimum size 20 gallons
(d) Container too large
Maximum size 45 gallons
(e) Improper container. Must be tapered, with han-
dles, constructed of durable metal, pressed fiber
or heavy-duty plastic material
(f) Manure: Tapered, durable metal container re-
quired
2. OVERWEIGHT-60 pound limit
3. TRIMMINGS OR LUMBER TOO LONG
OR NOT PROPERLY BUNDLED. MUST
be 2'x2'x4'.
4. GROCERY SACKS MUST BE PLACED
IN CONTAINER. HEAVY-DUTY PLASTIC
OR PAPER SACKS ARE PERMISSIBLE
FOR LIGHT-WEIGHT MATERIALS.
5. GARBAGE AND DOG DROPPINGS
MUST BE SECURELY WRAPPED IN
PAPER OR PLACED IN PLASTIC BAG
AND PLACED IN CONTAINER WITH
TIGHT-FITTING COVER.
6. ASHES-MUST BE DAMP
7. SAWDUST AND VACUUM CLEANER
DUST MUST BE WRAPPED OR PLACED
IN PLASTIC OR PAPER BAGS.
8. BUILDING MATERIALS FROM PRIVATE
CONTRACTORS WILL NOT BE
ACCEPTED.
9. ACID, EXPLOSIVES OR OTHER DAN-
GEROUS SUBSTANCES ARE NOT COL-
LECTED.
10. UNACCEPTABLE CONTAINERS:
(a) Wooden boxes
(b) Garden Carts
(c) Wheelbarrows
(d) Tarpaulins
(e) Washtubs
(f) Wastebaskets
(g) Containers weighing more than 20 pounds empty
CITY OF BURBANK
PUBLIC WORKS DEPARTMENT
STREET DIVISION
For information: 847-9622
Figure 50 Burbank collection regulations.
36
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Figure 4 above. As required by California state law, the waste is covered
daily (hazardous or infectious waste is covered immediately) at the
landfill site.
The current landfill is part of the Burbank Reclamation Fill
Project which began in 1949. Only city vehicles are permitted to dispose
of wastes at the landfill and salvage is not permitted.
Operating personnel at the disposal site consists of four people:
a foreman, an equipment operator, a water truck driver, and the weigh
station worker.
Administration of the Burbank Solid Waste System
The City of Burbank has a City Manager who is responsible for the
operations of the city government. The City Manager reports to an elected
council. Under the Manager are several operating and staff departments.
One of the departments is the Public Works Department. Each department is
composed of divisions. The division responsible for the collection and
disposal of residential solid waste is the Sanitation Division. (The
operation of the landfill is also the responsibility of the Sanitation
Division, while the planning of the landfill site is the responsibility of
the City Engineer's office.)
The fee system used in Burbank is a uniform or flat fee. That is,
for a single fee, the customer can present for collection an unlimited
amount of waste. Currently, the fee is $2.75 per month for a customer in a
single-family dwelling and $2.25 per month for an apartment dweller
choosing city service. Apartment complexes not electing city service are
charged $.90 per month per unit. The last fee change was instituted on
August 1, 1974. The city is currently (as of May, 1978) considering
another fee increase.
Billing for the service is included on the resident's electric
bill. (Electricity in Burbank is provided by the municipality.) Although
sufficient data were not available to estimate exactly the incremental cost
of billing for the refuse service, it is undoubtedly small since monitoring
costs are virtually nil (customers do not have the choice of alternative
service levels) and refuse service is mandatory (for those in single-family
dwellings).
DATA COLLECTED
As a part of the case study approach, as much data as was available
was collected from each of the case study cities. In general, it would be
expected that the more complex the fee structure, the greater the amount of
data available. That merely reflects one of the administrative costs of a
complicated fee structure.
The data available in Burbank can be classified into three
categories: data on quantities disposed, data on collection, and data on
37
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administration. These categories are described below. The data that are
used in our estimation of the effects of user fees are also presented
below.
Data on Quantities Disposed
From an analytical viewpoint, the single most important piece of
data collected consists of records of the amounts of waste disposed of in
Burbank over particular time periods. The City of Burbank has been keeping
records of tonnages since June, 1970 and, with the exception of one month
when the scale was being relocated, those records are available through
December, 1977.
Table 1 presents the data on the quantities of waste disposed at
the city owned landfill. As shown in the table, waste quantities are
divided into commercial wastes and residential wastes. We were told that
30% of the commercial waste should be classified as residential waste.
However, the data shown in the table do not include any adjustment to the
figures.
Figure 6 presents the data on residential waste quantities
(excluding the 30% commercial waste) over the time period. Note that while
the monthly fluctuations are rather great, the mean amount of waste is
fairly constant over the period. Two other factors are important, however,
in assessing the month-to-month fluctuations in waste generation. First,
the number of households from which the waste is collected will influence
the totals. Second, the number of working (or actually, collection) days
in the month will affect the totals.
Unfortunately, with respect to the number of households, the city
was not able to provide figures for more than two points in time. A
January, 1970 report states that the number of households was 35,000. A
current estimate is that there are approximately 31,000 households. This
implies that while total waste generation remained fairly constant,
generation per household has been increasing over the period.
Census data on the population of Burbank are obviously not
available on an annual or monthly basis. However, population estimates
are. Estimates of population for Burbank are shown in Table 2. Using
these figures and the annual totals for residential waste generation
(excluding and including the 30% factor) provides us with annual per capita
estimates as shown in Table 3. As can be seen from the table and the graph
of the data in that table (Figure 7), there appears to be a slight increase
in the amount of waste per capita over time. (Of course, such analyses
will be done more rigorously in the following sections.)
38
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TABLE 1. TONNES OF SOLID WASTE COLLECTED, BURBANK
OJ
Date
Jun.
Jul.
Aug.
Sep.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
Jun.
Jul.
Aug.
Sep.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
Jun.
Jul.
Aug.
Sep.
Oct.
Nov.
Dec.
70
70
70
70
70
70
70
71
71
71
71
71
71
71
71
71
71
71
71
7Z
72
72
72
72
72
72
72
72
72
72
72
Residential
2,948
3,074
2,737
2,671
2,718
2,486
2,667
2,495
2,462
2,524
2,798
2, 742
2, 860
2,886
2,885
2,901
2,483
2, 598
2,627
2,428
2,508
3,081
2, 590
3, 044
3,065
2,672
2,664
2,691
2, 720
2,999
2, 333
Commercial
1, 165
1, 207
1, 152
1,159
1, 146
1, 114
1, 185
1,002
1, 101
1,076
1,257
1,205
1,236
1,223
1, 103
1,079
922
1, 112
1, 147
993
937
1,240
958
1, 100
1,166
1, 065
990
1, 005
1,038
1, 185
952
Date
Jan.
Feb.
Mar.
Apr.
May
Jun.
Jul.
Aug.
Sep.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
Jun.
Jul.
Aug.
Sep.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
Jun.
73
73
73
73
73
73
73
73
73
73
73
73
74
74
74
74
74
74
74
74
74
74
74
74
75
75
75
75
75
75
Residential
2,617
2,451
2, 999
2,910
3,552
2, 868
3,078
3, 117
2, 509
2, 784
2,511
NA
2, 114
2,553
2,639
2,857
2,910
2,461
2,636
2, 817
2,596
2,902
2, 374
2, 834
2, 812
2, 437
2,943
2,976
3,275
3, 101
Commercial
1,097
999
1. 121
1,013
1, 351
1, 082
1, 131
I, 052
912
1,060
1,001
NA
916
1,011
1, 071
1, 139
1,030
1,016
1,045
879
865
974
795
721
1, 007
681
912
1,028
1,060
845
Date
Jul.
Aug.
Sep.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
Jun.
Jul.
Aug.
Sep.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
Jun.
Jul.
Aug.
Sep.
Oct.
Nov.
Dec.
75
75
75
75
75
75
76
76
76
76
76
76
76
76
76
76
76
76
77
77
77
77
77
77
77
77
77
77
77
77
Residential
3, 390
2, 927
3, 067
3, 207
2,667
2, 794
2, 548
2,505
3, 077
2,990
3,023
3, 091
3, 131
2, 898
3, 214
2, 730
2. 701
2,962
2, 818
2, 559
3,016
2, 8 '3
3, 116
3, 230
2, 897
3, 218
2, 940
2, 693
2, 703
2, 765
Commercial
859
732
767
802
667
751
729
646
870
791
813
794
776
766
939
974
1, 001
887
885
737
881
783
895
932
750
896
810
803
833
841
Source: Burbank Public Works Department.
-------
4000
3500 _
3000 _
2500
2000
Price
Change
4-
4000
_ 3500
_ 3000
- 2500
1970 n1971r 1972 ' 1973 ' 1974 ' 1975 ' 1976 ' 1977'
2000
Figure 6. Tonnes of household solid waste collected, Burbank.
-------
TABLE 2. BURBANK POPULATION ESTIMATES
Date Population
April 70 88,871
July 70 88,600
July 71 87,700
July 72 86,800
July 73 85,800
July 74 85,900
July 75 86,000
July 76 86,100
July 77 86,200
Source: Current Population Reports, U.S. Department of Commerce,
Bureau of the Census.
TABLE 3. ANNUAL PER CAPITA RESIDENTIAL WASTE, KGS.
Excluding Including
Year 30% commercial 30% commercial
1970
1971
1972
1973
1974
1975
1976
1977
373.3
368.0
378.2
399.3
369.0
413.9
404.9
403.9
420.5
414.0
421.8
444.3
409.0
449.2
439.7
438.9
The number of days on which waste was collected was not available
for all months for Burbank. However, assuming that collectors receive the
same holidays as are usual, an estimate of the working days in each month
can be made. Using these estimates of working days in the month, we can
estimate daily averages for waste collected. These estimates are shown in
Table 4.
In addition to data on quantities for commercial and residential
refuse service, data on quantities of waste disposed by other city agencies
were available for the two years 1976 and 1977- These quantities are shown
in Table 5. While much of these additional data are not used in the
analysis, the data on the deposits from the city parks are used in the
analysis of the litter question. The remaining data in Ta'fole 5 are
resented for comparative purposes with the other case studies.
41
-------
460
440
420
400
380
360
1976
1970 1971 1972 1973 1974 1975
Shaded area includes 30% of commercial waste.
Figure 7. Annual per capita residential waste, kgs.
1977
42
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TABLE 4. AVERAGE DAILY RESIDENTIAL WASTE, BURBANK
Date
Jun.
Jul.
Aug.
Sep.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
Jun.
Jul.
Aug.
Sep.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
Jun.
Jul.
Aug.
Sep.
Oct.
Nov.
Dec.
70
70
70
70
70
70
70
71
71
71
71
71
71
71
71
71
71
71
71
72
72
72
72
72
72
72
72
72
72
72
72
Tonnes
157
149
147
144
139
148
137
140
140
124
144
155
147
155
146
147
131
147
135
130
133
150
144
153
155
150
129
142
138
168
131
Date
Jan.
Feb.
Mar.
Apr.
May
Jun.
Jul.
Aug.
Sep.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
Jun.
Jul.
Aug.
Sep.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
Jun.
73
73
73
73
73
73
73
73
73
73
73
73
74
74
74
74
74
74
74
74
74
74
74
74
75
75
75
75
75
75
Tonnes
134
138
152
153
171
152
163
149
139
135
141
NA
109
128
141
145
146
138
134
140
136
139
138
153
142
132
153
149
163
168
Date
Jul.
Aug.
Sep.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
Jun.
Jul.
Aug.
Sep.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
Jun.
Jul.
Aug.
Sep.
Oct.
Nov.
Dec.
75
75
75
75
75
75
76
76
76
76
76
76
76
76
76
76
76
76
77
77
77
77
77
77
77
77
77
77
77
77
Tonnes
166
150
150
150
159
144
132
135
145
147
163
151
160
142
159
144
150
154
147
139
143
148
161
160
156
152
145
140
148
144
Source: Burbank Public Works Department.
43
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TABLE 5. TOTAL WASTE COLLECTED FROM MUNICIPAL AGENCIES
Date
Tonnes
Date
Tonnes
Jan.
Feb.
Mar.
Apr.
May
Jun.
Jul.
Aug.
Sep.
Oct.
Nov.
Dec.
76
76
76
76
76
76
76
76
76
76
76
76
1,503
1,142
1,834
1,309
1,128
1,110
1,101
1,581
1,470
1,304
1,279
1,906
Jan.
Feb.
Mar.
Apr.
May
Jun.
Jul.
Aug.
Sep.
Oct.
Nov.
Dec.
77
77
77
77
77
77
77
77
77
77
77
77
1,161
833
2,005
1,447
6,622
1,185
1,253
2,010
840
1,332
1,618
1,358
Source: Burbank Public Works Department,
Data on Collection
The data on collection of wastes in Burbank consists of data on the
wages of the personnel, the inventory of trucks, and a route map. The
purpose of presenting these data is to provide some basis for comparison
with the other cities in the study. However, because we do not have
detailed cost records by month, cost functions cannot be estimated and
marginal costs or average costs cannot be derived.
As noted earlier, waste is collected five days per week in Burbank.
The schedule for collection is as shown in the map in Figure 4 above. The
landfill is in the area noted on the map.
The number of personnel and their current salary is shown in Table
6. Personnel staffing has been constant since 1971.
TABLE 6. TOP MONTHLY SALARIES FOR SOLID WASTE PERSONNEL
DURING FISCAL YEAR (ENDING 30 JUNE) SHOWN
Year
1973
1974
1975
1976
1977
Collector,
Collector,
Sanitation
Foreman
Clerk
1-man crew
2-man crew
superintendent
$1
1
1
1
,123
,065
,427
,216
677
$1
1
1
1
,199
,137
,523
,298
727
$1
1
1
1
,283
,217 -
,694
,405
778
$1
1
1
1
,347
,278
,894
,597
816
$1
1
1
1
,417
,348
,970
,649
887
Source: Burbank Public Works Department.
44
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Data on Administration
Administrative data collected from Burbank include fee, budgets,
and regulations. The fee schedule for Burbank for the period 1971-1977 are
shown in Table 7. Note that there has been one fee change for the period.
As we noted in our description of the Burbank solid waste system above,
residents are permitted to dispose of unlimited quantities provided only
that they are properly containerized. Other regulations that are relevant
are shown in Figure 5.
TABLE 7. FEE SCHEDULES
Before After
August 1, 1974 August 1, 1974
Single-family housing
Multiple— family housing
Apartments not using service
$2.00
$1.50
$ .90
$2.75
$2.25
$ .90
Source: Burbank Public Works Department.
EMPIRICAL RESULTS
The Model
Burbank is a flat-fee city. As such, there is no "price" in the
sense of an incremental payment for additional service. In theory, a flat
fee (or at least a mandatory flat fee with no quantity limits) affects the
quantity of waste disposed only through the income effect. That is,
payment of the fee reduces the consumer's income but does not affect the
relative prices of goods and services.
We hypothesize that the demand for waste collection (measured by
quantity disposed), is a function of household income (after collection of
the fee) and, possibly, seasonal effects. In other words,
Q = f (y, s) (27)
where Q is the amount of waste disposed, y is the disposable (after
payment of the flat fee) income, and s is a (possibly a vector of)
seasonal factor(s).^ Note that equation (27) contains no socio-economic
3. We are, at this point, intentionally avoiding the dimensionality of the
variables. This is discussed further below.
45
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data other than income, since this study, unlike most previous studies, is
a time-series analysis. Therefore, shifts in most of these variables are
likely to be small over the period.
In a city such as Burbank, where revenues from the waste collection
agency at least cover accounting costs, we would expect that the fee is, at
least to some extent, dependent upon the amount of waste collected. That
is, there exists a "supply function" where the fee per household represents
the average cost per household. We may write this relationship as:
FEE = g (Q, w)
where FEE represents the flat fee and w the wage of the collectors.
Equations (27) and (28) represent the system of equations for Burbank.
Estimation
As they are written, equations (27) and (28) are not amenable to
econometric analysis. In this section, we specify functional forms for the
two relationships and choose some hopefully reasonable surrogates for the
variables for which data are not available.
Equation (27) represents the demand for waste collection services.
The quantity (Q ) can be expressed either as: (1) tons per month; (2)
monthly tons per capita; or (3) daily tons per capita. All three will be
used in the estimation. For the income variable (y), we would like to
have total personal income for the City of Burbank. However, at the city
level, personal income computations are made only once every five years.
Therefore, we have used, as a surrogate, retail sales in the City of
Burbank. Data on this series from the State is recorded quarterly, and are
provided in Table 8. In the results reported below, the quarterly figure
(deflated by the Consumer Price Index) was used in each of the months for
the quarter.
TABLE 8. RETAIL SALES TAX COLLECTED, BURBANK
Year 1st Qtr. 2nd Qtr. 3rd Qtr. 4th Qtr.
1971
1972
1973
1974
1975
1976
1977
$45,634
57,446
61,692
66,977
67,694
75,609
90,143
$49,141
62,054
73,182
69,946
72,082
84,762
98,134
$53,834
63,683
64,839
71,044
71,178
83,444
97,276
$61,227
69,354
70,528
77,375
83,753
94,285
107,563
Source: Research and Statistics Division, California Board of
Equalization.
46
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Because we do not have data on personal income, either by household
or total, an appropriate surrogate for disposable income is unclear. The
question becomes: how can the concept of disposable income (i.e., income
which reflects the influence of the flat fee) be incorporated into the
analysis?
If, for example, a linear demand curve is hypothesized, then the
econometric specification of equation (27) is straightforward. It is
simply:
by + cs
which can be written as:
Q = a + b (m - FEE) + cs (29)
where m is before fee disposable income and FEE is the flat fee. Note
that in equation (29), we have restricted the coefficients on the income
and fee variables to be equal and opposite in sign — a restriction that
follows directly from economic theory.
A second possible, and frequently-used, specification is the
double-log formulation. This formulation has the advantage of providing
elasticities directly in the form of the coefficients. Such a
specification would be as follows:
Q = aybse (30)
Expressed in this form, the difficulty of using the surrogate for income
becomes immediately apparent. Unlike the linear form, equation (30) cannot
incorporate the separate effects of the fee and income and still retain the
properties of the double-log formulation.
Because the fee in Burbank is reasonably small relative to income
and because we know, by theory, that the income elasticity reflects the
impact of fee changes, we will use equation (30) with retail sales as the
surrogate for disposable income (after fee) for our estimates of the effect
of income (recall that there is no price effect) on waste generation in
Burbank.
The use of retail sales as a surrogate for personal income must
also be viewed with at least some caution. A finding of a significant
relationship between the quantity of waste disposed and the level of retail
sales may simply reflect the fact that as people buy more goods, more waste
47
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will be generated. Therefore, the usefulness of the retail sales surrogate
depends on how closely it reflects personal income for the residents of
Burbank.
Empirical Estimates
The basic equation to be estimated for the Burbank case study is:
Q = a(RS)b sc
where RS represents retail sales. Taking logs on both sides,
ln(Q) = ln(a) + bln(RS) + cln(s) (31)
In this section, we present the results of the econometric investigation of
equation (31).
The first step is to select seasonal variables. For the estimation
of the equation, we have used dummy variables to represent winter
(December, January, and February), spring (March, April, and May), and
summer (June, July, and August).
Using monthly totals on waste collected and including the 30% of
"commercial" waste that is estimated to be residential, the results of
applying ordinary least squares to equation (31) gives (standard errors in
parentheses):
InQ = 6. 1 8 + . 1 8 InRS - . 06WIN + . 08SPR + . 07SUM (32)
(.11) (.025) (.025) (.024)
R = .38
N = 83
As shown in the results of the regression, the coefficient in the
income surrogate is not significant at the 95% confidence level (the
t-statistic is 1.62, which is, however, significant at the 90% level).
Before failing to reject the null hypothesis of no income effect, some
additional investigation should be performed. The most plausible reason
for the finding of no effect is that the effects of income are felt on
waste generation only after some lag. Therefore, we use lagged (by one
month) retail sales in the next regression. The results (standard errors
in parentheses) are:
48
-------
InQ = 5. 73 + . 221 ln(RS) - . 058WIN + . 081SPR + . 068SUM
(.106) " (.024) (.024) (.023)
R2 = .36
N = 81
The coefficient on the income variable implies an income elasticity
of .22 and is statistically significant at the 95% level. This figure is
consistent with previous findings (Wertz, Tolley, et al. , McFarland,
Stevens) as discussed in Section 4.
To check the sensitivity of the results to the particular quantity
variable, the results of several other regressions are summarized in Table
9. As shown there, none of the implied income elasticities are
significantly different from one another. (All regressions include the
same set of independent variables as in equation (32).)
Table 9. ALTERNATIVE INCOME ELASTICITES
Estimated
income
elasticity t-statistic
Excluding 30% of .221 2.05
commercial waste
Daily totals including .272 2.83
30% commercial
Daily totals excluding .273 2.81
30% commercial
Per capita monthly .233 2.13
including 30%
R2
.36
.28
.27
.35
NOTE: For all regressions, the number of observations was 81. The period
was February, 1972 to December, 1977 excluding December, 1973 and January,
1974.
These results lend support to previous findings in the range of
.2-.4 for the income elasticity. Because this analysis is a time-series
analysis we would expect the elasticities estimated to be short-run
elasticities and, therefore, somewhat lower than those estimated by
cross-sectional analyses.
49
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4
LITTER EFFECTS AND ADMINISTRATIVE COSTS
Litter
As discussed above, a city which employs a flat-fee structure
should not, theoretically, encounter any changes in littering resulting
from a fee change. While data on litter were not separately available,
data on deposits at the landfill from the Parks Department were available
for 1976 and 1977 and were presented in Table 5 above.
A simple test of the litter hypothesis is to see whether these
deposits are significantly related to the level of the real fee.
Therefore, the following equation was estimated:
ln(L) = bQ + b][ In(FEE) + b2s (33)
where L is the monthly total of Parks Department deposits and FEE is
the fee.
The result of the estimation of equation (33) is (standard errors
in parentheses):
ln(L) = 5.2 - .21 In(FEE) + . 01 WIN + . 13SPR + . 04SUM
(1.2) (.12) (.12) (.12)
R2 = .11
N = 24
As is obvious from the results of the regression, and as would be expected
on theoretical grounds, litter is not affected by the fee.
Administrative Costs
One of the advantages of a flat-fee system is its low
administrative costs. While data are available on the administration cost
with the Sanitation Department (in the form of personnel loading and
salaries), the marginal costs of billing are not available.
4. Since there was no fee change in the period 1976-77, a test of the
impact of a nominal fee change cannot be made.
50
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CONCLUSIONS
The Burbank, California, case study provides an opportunity to
empirically assess the incone effects on waste generation. Because of the
lack of an incremental price, there will be no price effect.
Using the data available from existing records in Burbank, an
income elasticity of approximately .22 was estimated. This estimate was
found not to be significantly affected by alternative definitions of the
waste measure (i.e., per capita vs. total, daily vs. monthly, and including
or excluding commercial). An income elasticity of this magnitude is
consistent with findings of other studies and is not, _a priori,
unreasonable.
51
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SECTION 6
SACRAMENTO, CALIFORNIA
The City of Sacramento, California allows residents to choose, for
a fee, the number of cans to be picked up during regular collections. We
have referred to such a system as a container-based fee. Service from the
city is mandatory for all residents. The basic service is offered once per
week with special collections, for an additional fee, available upon
request. As we have discussed above, such a fee system may induce
residents to dispose of less waste by imposing an additional fee. In
addition, it may induce additional use of illegal disposal methods.
Unfortunately, the data in Sacramento are not detailed enough for
us to make a definitive statement about these hypotheses. While good data
on the amounts of waste disposed over time are available, other important
data items, such as the number of containers chosen, are not. A new
management information system was introduced in February 1977, but the data
contained in those early months are suspected of being unreliable by
individuals in the City's Finance Department.
An important difference in the Sacramento system is the provision
for the pickup of lawn and garden refuse. Lawn and garden refuse in
Sacramento is not collected by refuse crews. Residents are not required to
place their garden waste in containers (except those in areas without
curbs), but are required to pile the waste in the street no sooner than one
day prior to the scheduled pickup. Thus all references to the solid waste
system in this section exclude lawn and garden trash.
The solid waste management system in Sacramento is first described,
and the data collected during and since our site visit are then presented.
Following that, we discuss the theoretical models to test the hypotheses
which we have formulated and which can be tested given the data available.
The results of the empirical tests are then reported. Finally, we present
the conclusions we can make concerning the effects of container-based fee
structures.
THE SACRAMENTO SOLID WASTE SYSTEM
Collection of residential solid waste in Sacramento is accomplished
by three-man crews using rear-loading packer trucks. Backyard service is
provided to all customers, although some residents do bring their cans
outside of the backyard gate to avoid the noise, etc., that accompanies
waste collection. The collected waste is hauled to the city landfill
located adjacent to the American River, which is reasonably close to the
geographic center of the city.
52
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Billing for the service is done by the Utility Billing System of
the city. Included on the bill are the water, sewer, refuse, and garden
refuse bills.
Collection of Residential Solid Waste in Sacramento
For purposes of residential collection, the City of Sacramento is
divided into 32 different routes. (There are a total of 52 routes in
Sacramento but some are commerical routes and others are special routes.)
One crew is responsible for each route and collects in different segments
of the route each day (Monday through Friday) of the week. The majority of
trucks used for residential waste collection are 16 to 20 cubic yard rear
packers. On most days the collection crew consists of three people,
although on days of high absenteeism there may only be two people. One
crew member is in charge and has the responsibility of driving the truck
between yard (collocated with the landfill) and the route. Once on the
route, however, crew members rotate driving. This is because the
collectors use 65 gallon tubs and collect from more than one household
before returning to the truck.
The reason for the three-man crews is the provision of carryout
service to all residents — a service included in the basic fee. While
one-man crews have been considered in Sacramento, resident resistance to
placement of refuse on curbs is sufficiently strong to prevent this.
(Recall, however, that this does not imply inefficiency in the economic
sense of the term.) The passage of Proposition 13 in California, with the
consequent search for new sources of revenues (or, equivalently cost-saving
alternatives), may change this at some point in the future.
The collection crews are on an individual incentive basis. That
is, once the route is completed, the crew may return to the landfill for
dumping, park the truck, and leave for the day. Thus, most crews finish
early. Collectors are unionized and are members of the Operating Engineers
Union.
Currently, about 102,000 households are served in Sacramento. The
majority (about 73%) have elected single-can service. Very few elect to
have more than two cans collected. Further, while the fee structure allows
for cans in excess of 31 gallons, for all practical purposes residents do
not choose larger sizes.
Disposal
As noted above, the waste collected by city crews is deposited at
the city-owned landfill. The waste is covered daily (with infectious or
hazardous waste covered immediately). The garage for the refuse trucks is
adjacent to the landfill so that there is little dead time between disposal
and return to the garage at the end of the day.
Most routes include two trips to the landfill, with the second at
the end of the day. The first trip is also often used for a lunch break.
53
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Administration of the Sacramento System
Responsibility for residential waste collection in the City of
Sacramento resides with the Division of Waste Removal, which is part of the
City Engineer's Office. A functional organization chart is shown in Figure
8. The chart shows the separate responsibility for collection of
residential waste and lawn and garden refuse. While it has been proposed
to consolidate the functions, voters in the city have rejected such a
proposal.
WASTE REMOVAL
Refuse Collection Superintendent
Waste Removal
Forty-nine residential
routes with collections
once a week.
Two commercial collection
routes with collections
five or six times per
week.
Two paper routes with vary-
ing collection schedules
Garden Refuse Pickup
Weekly curbside pickup
of lawn trimmings,
leaves, limbs, branches,
and other garden and
lawn refuse.
Figure 8. Functional organization chart.
The fee system in Sacramento is based on the number of containers
presented for collection. The current fee schedule is shown in Table 10.
While it appears complex, most of the options are designed for commercial
customers. Residential customers do not have the option of selecting the
number of pickups per week (other than through "special" pickups which
require a separate request each time).
Billing for the service is handled by the Utility Billing System.
The bill includes water, sewer, refuse, and lawn and garden billings. A
sample bill is shown in Figure 9. When new residents enter the city, they
select a level of service. The Utility Billing System is then updated. In
addition to keeping records for billing, the collection crews must be
notified. One clerk in the superintendent's office updates the route books
daily, noting starts, stops, and changes in levels of service.
54
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TABLE 10. SACRAMENTO FEE SCHEDULE
MONTHLY C'" RATES
31 gals or less
32 to kO gals
hi to 50 gals
51 to 60 gals
1 can pickup
once a week
$3.25
3-70
14.65
5.30
NKW IIATKS - EFFKCTTV JULY 1., 397V
2 can pickup*
once a week
5-20
5.85
6. hO
1 can pickup
twice a week
$6.80
7.50
8.35
9.05
3 coll. per
week per can
$7.50
0.35
9.10
9.75
5 coll. per
week per can
17.140
20.145
23. hS
6 coll. per
week per can
$18.70
22.60
25-90
30.50
•*Eaeh additional can (31 gallons or less) $1.15 per month
MONTHLY BIN RATES
1 Cubic Yd. Bin
Loose
2 Cubic Yd. Bin
Loose
1 pickup
per week
$20.85
27.35
2 pickups
per week
$3(4.10
Mi. 00
3 pickups
per week
60.65
5 pickups
per week
$7l|.CO
93.65
6 pickups
per week
$93.90
118.75
Included In all of the above rates is a charge of $7.00 per month for rental of a one cubic yard bin and $10.00
per month for rental of a two (2) cubic yard bin.
MONTHLY COMPACTOR RATES
1 pickup
per week
3 cubic yard
h cubic yard
5 cubic yard
6 cuoic yard
$1(2.60
56.80
71.00
05.20
MONTHLY BLANKET RATES
No.
of Blankets
1
2
3
h
5
1 pickup
per week
6.00
8.90
12.00
15-00
2 pickups
per week
$714.55
99. ho
1214.25
l!49.10
2 pickups
per week
$6~.95'
13-95
20.1)0
26.10
32.00
3 pickups
per week
$106.60
1142.15
177.70
213•20
3 pickups
per week
$10.1)0
20.1)0
29-05
37.75
1)6.50
1) pickups
per we ek
$lli9.10
198.60
21)8.5-0
298.JO
5 pickups
per week
$17.50
32.00
16.50
60.95
75.50
5 pickups
per week
$170.55
227.1)0
281). 25
3141.10
6 pickups
per week
$22.75
ld.55
60.1,0
79.30
98.15
6 pickups
per week
$218.30
291.10
363.90
1(36.60
(continued)
-------
TABLE 10 (continued)
MONTHLY HOI .E HOME FARK RATES
1 can pickup 2 can pickup 1 can pickup 3 coll. per
once a week once a week twice a week week per can
31 gals or loss ~W^ $O5 " ^-55 *j>-°°
32 to kO gals 2.50 3.U5 5.00 5-60
hi to 50 gals 3.15 3-90 5.60 6.10
"for Mobile Home Parks with more than 100 units and cans placed within 25 feet of roadway.
SPECIALS - EFFECTIVE JULY 1. 1977 COMPACTORS
Weekday Sat/Sun
CAMS
3 cu. yd. $12.25 $16.50
Route Special U cu. yd. 15-25 23-00
5 cu. yd. 18.25 27-50
$1.15 per can (31 gallons or less) 6 cu_ yd_ 21.25 31-50
Special
$3.25 for first can (31 gallons or less;
and $1.15 for each additional can (31 gallons or less)
Bins
Route Special
1 cubic yard - $5.15
2 cubic yard - 7-30
Deliver Bin and Pickup
1 cubic yard - $ 7-00
2 cubic yard - 10.25
3 cubic yard - 13-50
Special Cleanup
$8.65 pr. hour - Time required to remove, load in a truck
and haul away rubbish or waste matter.
Source: Sacramento Public Works Department.
-------
THE CITY UI< bACKAMi^MU
.--^ MUNICIPAL SERVICE BILL
A x^ >i v o— — ""'9 ISISTREET n i*^ p n o r* r*
. ^jjLs~-f^- WbVJbb
' 1
~ Your Account Number - O °O / - (3OODO - OOO C— -
t Ptnod Covered From x/_ / x 7S7 T° ,jj — /.}', 7,J
5
"~T.'W1,';l,l . 'M'l''i'VJI'Tr'l>l'( —
PLEASE PAY
THIS AMOUNT
PLEASE
! Oo Cot Fold
S'.ap'e or
M, *->..
5i«, - L _ _• _q ?_•?A _- _A_-_ _ ^±-_. j
THIS CARD
Maki Check Payibll To: CITY OF SACRAMENTO
^smit To1 Room 104 City Ha"
Sacfamtnto, Calil. 958M U, ^
ol
<£>(
IjoHll
tf oo
i
Jtvo
1
i^5"V<7
i
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£i-SO
1
*-j\ o 0
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;
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THE CITY OF SACRAMENTO
MUNICIPAL SERVICE BILL
915 I STREET
From
PLEASE PAY
THIS AMOUNT
/
'Your Account Numbir
Period Covered
for Your Records '
PLEASE HELP US to maintain
the best possible service at the most
reasonable rates by paying this bill
promptly. Thank you. Should you
have any questions regarding this
account call 449-5454.
y
'-•••• J-»T .,.,.,.,.-....— . ,--.—-^,.;> ••' ——•— —. '• •• >.» y— -•-'• — ' • -L, v ri'»
UTILITY SERVICES
This bill is due and payable upon presentation and will become delinquent after the ending date ol "period covered". ACsounti
CHARGES CONSTITUTE A USN
Delinquent '.Vater. Sewer, ana V.'as
sity.
WATER AND SEWER SERVCSS
Ten djyj after v/riticn nonce ol clinquant Water and Sewer fee*, water service may be shut oil. Tha water shnH not bo turned on
Water icrvic* may be disconnected and genalnei invoked lor wilful or ncgiigeni waste of water.
other
t occurt, In addivon to
Vacancy Credit - If premises aro vacant, a credit for nqn-uie of water and icwcr service may be grante-1 upon the following condiuoni:
ID Written request 10 the Utilny Servicci Oivmon,
121 Psymeni of a S25.00 service fee.
(3) Payment of current utility bill in full, tnd
(4) Water service ii turned off.
All payrrwnu rtceivtd trull be applied to the oldcif balance, Partial paymenti will be proraud-Toial due-on Garbaot fiat balanct
prorated between Waier ind Sower imounri duo. '
Codg Deicnpnon Codei Description
01 Current Amount! 06 Air Conditioning
02 Delinquent Amounts- 07 Irrigation
03 CredM Amounts .08 Swimming Pool
CM Adjust muntj 51 Penalnei
05 Fire Proiecnon 53 Turn-on Fe«
'--'—' _ i-__ - ..L .^*J-*- _ _ .r-n. ~r. ^-*-*-_ j T_.
Figure 9. Sample bill, Sacramento.
57
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DATA COLLECTED
The amount of data available in Sacramento was somewhat greater
than for Burbank. This is probably due to two factors: first, the fee
system is more complex and therefore requires more information for
operation (and, in turn, generates more information); and, second,
Sacramento is substantially larger in population than Burbank. Below we
present much of the data collected during the site visit related to
residential solid waste collection.
Data on Quantities Disposed
Data were available by collection route in Sacramento. The totals
for the city, expressed in tonnes per day, are shown in Table 11. The
chart in Figure 10 shows the pattern of collection over the period July,
1975 to April, 1978. (Recall that these totals do not include lawn and
garden refuse and cannot, therefore, be directly compared with the other
case study cities.)
TABLE 11. HOUSEHOLD WASTE COLLECTED, SACRAMENTO
Date
Tonnes/day
Date
Tonnes/day
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
1975
1976
1976
1976
1976
1976
1976
1976
1976
1976
1976
1976
1976
1977
1977
241
230
223
229
227
235
222
228
NA
NA
226
232
228
235
226
March
April
May
June
July
August
September
October
November
December
January
February
March
April
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1978
1978
1978
1978
NA
220
NA
227
228
234
238
228
235
246
270
247
245
242
Source: Sacramento Public Works Department,
Garden refuse collections are shown in Table 12. These are
monthly, not daily. Also shown in Table 12 are deposits in the landfill
from the city parks. These data will be used below to address the question
of the externality effects of capacity-based fee structures.
58
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Ul
280
260 -
240 -
\
220 -
200
1975
Price
Change
280
- 260
- 240
220
200
1976
1977
1978
Figure 10. Sacramento household solid waste collected.
-------
TABLE 12. TONNES OF WASTE DISPOSED, SACRAMENTO
Date
Jul
Aug
Sep
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
1975
1975
1975
1975
1975
1975
1976
1976
1976
1976
1976
1976
1976
1976
1976
1976
1976
Garden
waste
2978
2987
3379
4018
4534
5230
3214
2499
3888
4246
4290
3624
3438
3506
3775
3574
5050
Parks
418
531
438
444
462
544
504
454
463
365
294
388
396
377
335
331
286
Date
Dec
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Jan
Feb
Mar
Apr
1976
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1978
1978
1978
1978
Garden
waste
4205
4466
2732
2892
3299
3337
3019
2715
3115
3164
3247
5131
6267
3780
3670
4466
5287
Parks
271
415
439
502
NA
NA
262
354
373
350
365
295
305
625
600
355
325
Source: Sacramento Public Works Department.
Collection Data
The data obtained on the collection of waste in Sacramento consists
of data on labor used. Table 13 lists the current salary schedule for the
Sanitation Division. Below, the personnel loading for the Division is
given.
Administrative Data
A functional organization chart was given in Figure 8 above. The
detailed chart for the Division of Waste Removal is shown in Figure 11.
Note the separation between the collection of residential solid waste and
yard and garden waste (collected under the heading of street refuse). As
shown in the figure, there are 199 people directly associated with the
collection of solid (including commercial) waste. The current salary
schedules are shown in Table 13.
As noted above, the fee system in Sacramento is one in which the
resident chooses the number of containers to be picked up each week. For
all practical purposes, the choice is between one can and two and, of this,
almost all residents choose containers of 31 gallons or less. The fee
schedules for recent years is shown in Table 14. Note that fee changes
have been more frequent than in Burbank.
60
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TABLE 13. SALARY RANGE FOR WASTE REMOVAL PERSONNEL
REFUSE COLLECTOR
REFUSE COLLECTOR TRUCK DRIVER
STEP A
$ 383.00
$ 995-00
STEP C
$ 97h-00
$105lj.OO
STEP E_
$1075-00
$1165.00
WASTE REMOVAL FOREMEN
STEP A
STEP B
1232.00
STEP C
S117U.OO 1232.00 $1295-00
WASTE REMOVAL ASSISTANT SUPERIOTENDENT
STEP A STEP 3 STEP C
$1768.00 $1656.00 $1550-00
WASTE REMOVAL SLTERIMTEMDEOT
STEP A STEP 3 STEP C
$2272.00 $2387.00 $2506.00
JUNIOR TYPIST CLERK
STEP A STEP_3 STEP C
$ 659-00 $ 692.00 $726.00
IMTERMEDIATS TYPIST CLERK
$ 723.00 $759-00 $797-00
SE.NIOR TYPIST CLERK
$ 823.00 $865-00 $908.00
FIELD REPRESENTATIVE
STEP A STEP B STEP C
$ 920.00 $ 967.00 $1016.00
ADMINISTRATIVE ASSISTANT I
$1236.00 $1298.00 31352.00
STEP D
$1359.00
STEP D
$20ti7-00
STEP D
$2631.00
STEP D
$763.00
$837.00
$953-OC
STEP D
$1066.00
$11,30.00
STEP E
$1U27.00
STEP E
$21U9-00
STEP £
$2763-00
STEP S
$ 879-00
$1002.00
STEP E
$1120.00
$1501.00
Source: Sacramento Public Works Department.
61
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WASTE REMOVAL
Supe rintendent
Asst. Superintendent
Total
HOME & INST. REFUSE
1
2
3
2
3
5
16
Supervisor
Equipment Serviceman
Watchman
Ref. Coll. Tr. Dr. (Weighmaster)
Refuse Collectors (Extra Bd. )
Refuse Coll. Tr. Dr. (Ex. Bd. )
Total
1 Foreman
CsJ
30 Total
CENTRAL CITY ROUTES
1
11
28
~40
Foreman
Ref. Coll. Truck Drivers
Refuse Collectors
Total
ENGINEER - DIVISION OF
WASTE REMOVAL
ADMINISTRATION
[Administrative Asst. I
[Senior Typist Clerk
Ijr. /Inter. Typist Clerk
Field Representative
|Senior Clerk
,Total
SOUTHEAST AREA ROUTES
1
11
22
34
Foreman
Ref. Coll. Truck
Refuse Collectors
Total
Drivers
STREET REFUSE
1 Street Cleaning Supervisor
1
DA ROUTES
Truck Drivers
lectors
EAST AREA ROUTES
1
11
22
34
Foreman
Ref. Coll. Truck Drivers
Refuse Collectors
Total
SOUTH AREA ROUTES
1
12
25
38
Foreman
Ref. Coll. Truck Drivers
Refuse Collectors
Total
NEIGHBORHOOD CLEAN-UP
1
3
3
7
Maint. Man III
Ref. Coll. Truck
Utility Man
Total
Drivers
GARDEN REFUSE PICKUP
3
51
to
0.5
3
9
76.5
St. Cleaning Foreman
Maint.
Maint.
Maint.
Maint.
Maint.
Man (Truck Driver)
Man-I
Man-I (Relief)
Man (Tr. Dr. ) Relief
Man (Tr. Dr. ) Seasonal
Total
STREET
1
7
3
1
5
5
1
0. 5
23.5
SWEEPING
Foreman
Motor
Maint.
Maint.
Maint.
Maint.
Maint.
Maint.
Total
Sweeper Operators
Man
Man
Man
Man
Man
Man
IV
(Truck Driver)
I
II (3 Wheel)
(Tr. Dr. ) Relief
I Relief
TOTAL STAFF 311
Figure 11. Detailed organization chart, Sacramento.
-------
TABLE 14. RESIDENTIAL COLLECTION FEES, SACRAMENTO
Effective
date
16 Aug. 1974
1 Oct. 1975
1 July 1977
1 Can
per week
$2.30
3.00
3.25
2 Cans
per week
$3.40
4.00
4.40
Source: Sacramento Public Works Department.
The fee change of October 1975 actually resulted in a reduction of
the incremental fee from $1.10 per month to $1.00 per month. The growth in
the nominal fiat fee (or basic one-can charge) has averaged about 12%
annually over the three years 1974-1977, while the growth in the
incremental fee has averaged about 9% annually.
EMPIRICAL RESULTS
The Model
With a container-based fee, the resident makes two distinct,
although related, choices. First, based on the fee per can the decision on
the number of cans of service is made. Second, given the number of cans
selected, the decision about the amount of waste to generate is made.
These related decisions can be modeled as follows:
Q = f (C, y, s) (34)
C = g (p, y) (35)
where Q is the amount of waste disposed, C is the number of containers
selected, y is disposable income (again after payment of any mandatory
fee), s represents seasonal factors, and p is the price of an
incremental can of service.
As with Burbank, it would normally be expected that the level of
the fee would depend on the amount of waste generated. Such a dependence
could be symbolically described by:
p = h (Q, w) (36)
where w represents, say, collectors' wages.
63
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In theory, then, the set of functions (34) - (36) provides a system
of equations for assessing the effects of an incremental user fee.
Estimation
Given sufficient data, the system of equations (34) - (36) could be
estimated using two-stage least squares. In this section, we describe the
modifications to the theoretical specification which are necessary to make
because of data limitations. To make these modifications, we first specify
functional forms for the equations.
With respect to the waste generation equation (equation (32)), we
again hypothesize a double-log specification, or
Q = aCbyCsd (37)
Similarly, a double-log specification is assumed for the demand for
containers. Given that assumption, equation (35) becomes:
C = epfyg (38)
The first modification which is required is caused by the lack of
data on the number of residents choosing two-can service. We therefore
substitute equation (38) into (37) to obtain
Q = a (epfyg)byCsd (39)
or
Q = a'p£'yg'sd (40)
where the primes indicate the new constants.
The second modification we make deals with the simultaneous nature
of the system (34) - (36). Theoretically, simultaneous methods (e.g.,
two-stage least squares) should be applied to incorporate the simultaneity.
However, given the data available in Sacramento, little error is introduced
by applying ordinary least squares to (40) alone. The reason is that
quantity data are available for only a relatively short period (July, 1975
to April, 1978). During that time, the staff of collectors has remained
constant. Therefore, the cost associated with refuse collection is tied
almost directly to wages and only slightly, if at all, to the* quantity of
waste disposed. For this reason, equation (40) alone will be .estimated to
derive elasticities.
64
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Empirical Estimates
The basic equation to be estimated is:
^ bed
Q = ap y s
Taking logs on both sides gives:
ln(Q) = ln(a) + bln(p) + cln(y) + dln(s) (41)
Again, as with Burbank, we use dummy variables to represent three of the
four seasons. Further, a surrogate is required again for the income
variable. We use retail sales in the City of Sacramento. The basic
one-can fee is not subtracted since it is small relative to income and no
true income variable is available.
The data on retail sales in Sacramento are shown in Table 15. For
purposes of the analysis, we assume that the monthly totals are equal to
the value in the quarter.
TABLE 15. RETAIL SALES TAX COLLECTED: SACRAMENTO
Year
1974
1975
1976
1977
1st Qtr.
$202,993
216,400
250,440
288,050
2nd Qtr.
$235,733
251,632
279,356
324,500
3rd Qtr.
$253,034
265,955
293,017
345,745
4th Qtr.
$249,047
269,443
305,091
350,153
Source: Research and Statistics Division, California Board of
Equalization.
Using the data described above, the following results (standard
errors in parentheses) were obtained after applying OLS to equation (41):
InQ = 4. 7 + . 09 ln(RS) + . 23 ln(p) + . 01 WIN - . 01SPR - . 02SUM (42)
(.07) (.09) (.01) (.02) (.01)
R2 = .40
N = 26
65
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There are three important results to note. First, the income elasticity,
while positive, is insignificantly different from zero (significant at the
20% level). Second, the price elasticity is positive — and significant.
Third, the seasonal dummies are not significant.
Because the existence of a positive price elasticity is
theoretically implausible, the results in equation (42) are analyzed
further. The first step is to recognize that, as with Burbank, the effect
of income and price changes may occur only after some lag. The effect of
lagging retail sales and incremental price by one period (standard errors
in parentheses) is:
InQ = 3. 6 + . 20 ln(RS) + .22 ln(p) + . 01 WIN + . 02SPR - . 01SUM (43)
(.08) ~L (.10) (.01) (.02) (.01)
R2 = .50
N = 22
Performing this regression results in a positive and significant income
elasticity of .20. However, the problem of a positive and significant
price elasticity remains.
Because the price variable used is deflated by the CPI and, for
several months, the nominal fee is unchanged, the effect is that the
incremental fee may be acting as a trend variable. To check this
possibility, we substitute a trend variable for the seasonal dummies. When
this is done, the results (standard errors in parentheses) are:
ln(Q) = 1. 95 + . 35 ln(RS) + . 09 ln(p) - . 002T
(.12) (.09) (.001)
R2 = .48
N = 22
The effect of this substitution is to retain the positive, and significant
income elasticity (although the magnitude of the elasticity has increased
to .35). While the price elasticity is again positive, it is
insignificantly different from zero. Further, while the R2 is about the
same as before, the number of variables is fewer making the regression
significant at a higher level.
LITTER AND ADMINISTRATIVE COSTS
Litter
Elasticities estimated above are related to quantity of waste
presented for collection. The price effects on quantity of waste generated
66
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will be the same as on quantity collected only if no substitutes for
municipal collection are available. It has sometimes been argued that user
charges which vary according to quantity of waste collected provide an
incentive for households to seek substitutes such as littering which have
unacceptably high social costs.
No direct measure of quantity of litter was available in
Sacramento. Quantity of waste disposed at the landfill by the Parks
Department (Table 12) was used as a proxy for quantity of litter on grounds
that some illicitly disposed household waste will be dumped in parks and
show up over and above some base quantity. To test the hypothesis that
quantity of litter is related to the level of the user fee, the following
equation was estimated:
ln(L) =
ln(p)
(44)
where L is the monthly total of parks waste, p is the real price of an
incremental can, and s is a seasonal dummy variable.
The results (standard errors in parentheses) were:
ln(L) = 6.7+1.53 ln(p) + . 1 TWIN + . 15SPR + . 04SUM
(.77) (.10) (.12) (.10)
R = .19
N = 28
The coefficient for real price is significant here at the 95% confidence
level (t-statistic = 1.99) and of the expected sign. However, this may be
due to a time trend such as improved litter collection operations. To test
this further hypothesis, the following equation was estimated:
ln(L) =
ln(p)
(T)
(45)
where T is a time series and other variables are as before. The results
(standard errors in parentheses) were:
ln(L) = 6.9 + .74 ln(p)
(.65)
. 012T + . 14WIN + . 09SPR + . 03SUM
(.004) (.09) (.11) (.08)
R = .43
N = 28
67
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In this case only the coefficient for the time variable is significant.
Another test of the litter hypothesis would be to suppose that
household refuse was disposed with garden waste in response to price
increases. To test this hypothesis the following equation was estimated:
ln(G) = bQ + bj ln(p) + b2s (46)
where G is monthly quantity of garden waste and other variables are as
before. The mandatory fee for garden refuse collection remained constant
throughout the period considered and hence does not enter into the
equation. The results (standard errors in parentheses) were:
ln(G) = 8.4 - .05 ln(p) - .01 WIN - . 08SPR - . 22SUM
(.80) (.11) (.12) (.10)
R2 = .17
N = 30
Only the dummy variable for summer was significant.
Sacramento does not allow individuals to haul their own household
refuse to the landfill and dispose of it there. While the two alternative
methods of disposal we have explored here are imperfect measures of illicit
disposal at best, the conclusion indicated — that price changes in the
user fee affect neither quantities of refuse generated nor quantities
presented for collection — are not contradicted by observations or remarks
of personnel in the Division of Waste Removal.
Administrative Costs
Administrative costs associated with the container-based fee
structure include costs of billing, including both maintaining billing
records and processing bills, and keeping records of starts, stops, and
changes in service levels. Unfortunately, data were not available in
Sacramento which would allow an evaluation of these costs.
68
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SECTION 7
PROVO, UTAH
Among cities with user fees for household solid waste collection,
relatively few employ a variable fee structure based exclusively on pickup
location, charging one rate for backyard service and another rate for
curbside service. According to the data in Appendix A, only two percent of
the cities employ such a structure. Provo, Utah, is one such city.
Residents of Provo may choose between paying $2.50 per month for curbside
collection and paying $5.00 per month for backyard collection.
In the model for a service-based fee presented in Section 4, the
price of service affected the amount of waste generated via service level.
Unfortunately, the lack of data in Provo makes it impossible to estimate
the model's demand specifications. Available data lend some support to
existing evidence of positive income elasticities for solid waste services,
although this support is weakened by the nature of the data.
In this section we first describe the solid waste system in Provo
and the data which were collected. These data are then used to test some
hypotheses generated by the service-based model and the results are
presented.
THE PROVO SOLID WASTE SYSTEM
Solid waste collection and disposal in Provo is carried out by a
department of city government, the Department of Sanitation, and residents
are billed monthly for this service along with electric, water, and sewage
bills. Frequency of residential collection is once a week, and city
collection is mandatory for all residents. Most commercial firms also use
city collection, although the largest single producer of solid waste,
Brigham Young University, hauls its own waste to the city landfill, paying
only for disposal.
About a third of Provo's residents are students and their families;
this has at least two important consequences for solid waste collection.
First, extra solid waste is produced at the beginning and end of each
school term as students move in or out. Quantitative figures are not
available on the degree of this effect, but it is noticeable to personnel
in the Sanitation Department. Second, a high proportion of housing units
are rented rather than owner-occupied in order to accommodate students
living off campus. Out of 16,725 units in Provo in January 1978, 7,694
were owner-occupied and 9,031 were rented. About 40% of the rental units
are duplexes, and most of the rest are apartments. In some apartment
69
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blocks refuse is collected from each unit separately, while in others
collection and billing is consolidated through using large containers.
Again, exact figures are not available, but it appears that apartment units
are evenly divided between the two arrangements. Altogether there are
11,000 residential collection accounts out of a total of 13,000.
Collection of Residential Waste in Provo
Collection is made by a fleet of eight packer trucks owned by the
Sanitation Department and operated by three-man crews. Three of the trucks
are front-loading packers serving commercial customers who rent containers
ranging in size form 2 to 8 cubic yards, including apartment blocks. The
other six trucks are 25 cubic yard, front-loading packers, one of which
serves a commercial-industrial route, and five of which serve residential
routes. Crews on residential routes work a five day week and are not
affiliated with any union.
The choice between backyard and curbside service was originally
intended as a way of providing the elderly and disabled with the
convenience of carryout service. In practice, however, any resident with a
backyard has this choice. About 3% of all household customers elect to pay
the higher fee. Households demanding backyard service do not generally
include the elderly and disabled; rather, they are found in areas with
higher property values and household incomes. Such residents are widely
regarded in the Sanitation Department as taking advantage of a service
intended for others, one of several complaints the Department has about the
service.
Carryout service is felt to be costly in terms of time —
Department Superintendent John Farley estimates that between 10 and 15
curbside pickups could be made in the time it takes to make one backyard
collection. The service' is also felt to increase the risk of property
damage or personal injury borne by both customers and collectors due to
such things as parked cars and other obstacles in the collector's path and
mistaken removal of items left near rubbish containers. Consequently, the
Sanitation Department does not particularly encourage households to ask for
carryout service, although it does not refuse such requests. The
Department has been trying unsuccessfully for at least four years to end
the carryout option, but the three-member City Commission responsible for
such policy decisions has declined to eliminate the service.
Disposal of Solid Waste in Provo
The Sanitation Department maintains a sanitary landfill for solid
waste disposal located in an industrial area near the south freeway
entrance to the city. All solid waste collected by the Department is
deposited in the landfill, and it is open to households and commercial
users, but only solid waste originating in the city may be deposited.
Households and firms on contract with the city may use the landfill free of
charge; all others are charged a fee of $1.96 per cubic meter or $6.61 per
tonne.
70
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Although scales are not operated at the landfill, it is estimated
that about 218 tonnes of solid waste per working day are deposited at the
landfill. At this rate, given current operating methods, the site is
expected to reach capacity in two or three years. After that time refuse
material would have to be stacked above the present ground level; in
addition, the water table is high near the site and ground water
infiltration is a continual problem. At present there is no functioning
resource recovery system in Provo or Utah County, and any alternative
landfill site would probably be outside the city. The solution which is
prsently being pursued is the acquisition of a baler which will compact
solid waste to a greater density, allowing the life of the landfill to be
extended by five to eight years beyond current estimates.
Administration of the Solid Waste System
Provo is the smallest city among our five case studies, and the
administrative structure of its solid waste operations is correspondingly
the least complex. Only three or four employees act in an administrative
or supervisory capacity, and they are closely involved with day-to-day
operations of collection, disposal, and equipment maintenance. The
advantage of this type of operation is a probable reduction in
administrative costs. For example, collectors do not use route books to
remind them which customers subscribe to carryout service; locations of
carryout residences are simply memorized. Billing is done by a separate
office of municipal government together with billing for water, sewage, and
electricity. The Sanitation Department is charged $924 a month for this
service, although the billing office estimates its cost for this service to
be about $150 a month more than that. Depending on whether the lower or
higher figure is used, this translates to an annual cost of 8.4^ or 9.8^
per household, which is 3.2% to 3.8% of revenue from households.
Unfortunately, the same administrative features mean that data
available in other cities about solid waste systems are not available in
Provo. Surveys of quantities collected are undertaken periodically,
usually in connection with reviews of rates for collection service, and
surveys of the location of residential customers are undertaken when a need
for re-routing is perceived, but continuous records are not kept and
outdated survey data are not systematically preserved.
Occasionally the absence of data may result in added costs. In
1976 a study undertaken by one member of the Department revealed that the
city was losing about $12,000 per year from its solid waste collection
operations at BYU. This was pointed out to the city commission in a report
containing alternative price schedules for collection operations at BYU
which would cover the city's costs. As a result, BYU rejected the higher
prices and now hauls its own rubbish, paying the normal commercial rate for
disposal.
71
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DATA COLLECTED
No permanent records are kept in Provo of quantities of solid waste
collected or disposed. However, between August 1976 and January 1977
estimates were made of tonnage collected. Information available from these
estimates are shown in Table 16 for residential collection routes. Figure
12 shows districts of the city from which collections are made on each
working day. Cross-sectional comparisons may be made by comparing these
districts with census tracts shown in Figure 13. Because of rapid growth
between 1970 and 1977, such analysis may be misleading, however.
TABLE 16. TONNES/DAY, RESIDENTIAL COLLECTION
AUGUST 1976 THROUGH JANUARY 1977
Truck no.
Mon.
Tue.
Wed.
Thu.
Fri.
Total
831
832
833
834
835
8.29
3.26
9.97
10.71
9.90
4.87
3.10
7.00
5.66
5.68
7.35
1.67
8.89
6.65
4.85
8.78
2.29
8.75
7.61
9.39
9.09
3.03
8.82
7.58
10.18
38.38
13.34
43.42
38.22
40.00
Source: Provo Sanitation Department.
Data on numbers of households selecting curbside and backyard
service were available from billing records. Table 17 shows numbers of
households in single-unit structures receiving each level of service for
each of four billing districts during the last week of April 1977 and 1978.
Location of the billing districts is shown in Figure 14.
TABLE 17. NUMBER OF CUSTOMERS SELECTING CURB AND BACKYARD SERVICE
Billing district:
II
III
IV
Total
1977
1313
157
10.7
2670
104
3.7
1389
26
1.8
2180
24
1.1
7552
311
4.0
{Curb
Rear
% Rear
1366 2811 1344 2341 7862
143 98 46 31 318
9.5 3.4 3.3 1.3 3.9
Source: Billing Department, Provo City Power Company.
72
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Figure 12. Prove collection districts.
73
-------
Figure 13. Census tracts.
74
-------
Figure 14. Provo billing districts.
75
-------
Table 18 shows user fee prices for residential service in recent
years.
TABLE 18. PRICE OF RESIDENTIAL SERVICE, MONTHLY
Effective
date
Feb. 1970
Sep. 1974
Jul. 1977
Curb
service
$1.25
2.00
2.50
Rear yard
service
$2.00
3.00
5.00
Source: Billing Department, Provo City Power Company.
EMPIRICAL RESULTS
In Section 4 above, a demand model for a service-based fee was
presented. In that model, the amount of waste generated was seen to be
affected by the price of the service through the effect of service levels
on waste generation. Unfortunately, the lack of data in Provo makes it
impossible to estimate the demand specifications presented in Section 4.
Other data that were available and that were given above provide an
opportunity to indirectly test for an income effect in the demand for
increased service levels. These data are the breakdown by billing tracts
of the percentage of residents choosing backyard service. While these
tracts do not correspond to Census Tracts, they can be identified in terms
of "housing value" which might be taken as an (albeit imperfect) surrogate
for income.
We specify, therefore, the following equation:
BY = aQ PRy l + a2 TRACTj + &3 TRACT2 + a4 TRACT3 (47)
where BY is the fraction of residents choosing backyard service, PBY ^s
the price of backyard service, and the TRACT^'s are dummy variables
representing the different areas of the city. There are 57 observations
for each of the dates. April 1977 and April 1978. These represent dates
before and after the fee change.
Applying ordinary least squares to (47), we obtain (standard errors
in parentheses):
76
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BY = 5. 14 + . 13 P + 2.7 TRACT +1.7 TRACT- + . 82 TRACT
(.13) CI (8.3) ! (5.3) L (2.5)
R2 = .43
N = 114
We see that each of the tracts, which are in declining order of housing
value, are significantly and positively related to BY. Note, also, that
both the magitude and significance of the estimated coefficients decline
with TRACT number.
Conclusions
These findings in Provo lend some support to the existing evidence
of positive income elasticities for solid waste services. However, because
of the nature of the data, the support is not strong.
77
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SECTION 8
GRAND RAPIDS, MICHIGAN
Grand Rapids, Michigan operates a non-mandatory solid waste
collection service which participating households pay for by purchasing
plastic bags or tag cards whose price includes payment for municipal
collection and disposal. Specially marked city garbage bags are sold for
$2.30 per dozen; tags are sold for $1.25 for 10. The city also sells
special plastic trash cans whose contents are collected upon payment of an
annual fee of $4.35 or $6.50, depending on the container size. In
addition, several privately owned collection firms operate in Grand Rapids,
and residential households may arrange service with these rather than pay
for municipal service.
The metered-bag system is a hybrid between the ideal
quantity-based system discussed in Section 4 and the container—based system
discused in Section 6. The empirical evidence from data collected provides
little or no support for non-zero price elasticity.
This section describes the Grand Rapids solid waste system and the
data collected, followed by a description of a model for the metered bag
system and the results of testing hypotheses generated by the model.
Finally, litter and administrative costs are analyzed and conclusions about
the metered-bag system presented.
THE GRAND RAPIDS SOLID WASTE SYSTEM
Municipal collection of solid waste in Grand Rapids is done by one-
or two-man crews on a once-weekly basis. Collectors take only refuse left
at curbsides in city bags or with city collection tags attached. Waste is
hauled to and disposed of at a sanitary landfill in Plainfield Township
northeast of the city.
Municipal collection is under the authority of the Grand Rapids
Environmental Protection Department, a department of city government;
trucks and other equipment are leased from the city's motor pool. Because
purchase of bags or tags by residents represents payment for collection, no
billing or other administrative procedure dealing with households is
required. In place of such procedures the Environmental Protection
Department carries out distribution of bags and tags for retail sale.
5. Cf. pp. 96 below.
78
-------
Origins of the Grand Rapids Fee System
Before 1970 most households in Grand Rapids dealt with two separate
solid waste collection systems. Food wastes (garbage) were collected by
the city's refuse department, and other solid wastes (trash) were collected
by an assortment of large and small private hauling firms. A major impetus
toward combined collection occurred when inexpensive plastic bags became
available to households in shops. When the city allowed households to
present garbage for collection in these rather than in cans, residents
discovered it was a simple matter to slip trash in with garbage, thereby
obviating demand for separate trash collection. In short, availability of
plastic bag technology was bound to lead to combined collection, resulting
in the city taking on a share of trash collection and/or private haulers
taking an increased share of garbage collection.
In January 1970 the City Manager presented a plan to divide the
city into ten collection districts which would be allocated between the
city and private haulers on the basis of contract bids. Under this plan
collection would have been financed through a monthly flat-rate user fee of
$0.90 or $1.50 per household, depending on whether the fee would cover the
added costs in the city budget or the full cost. The plan was strongly
opposed by small private haulers who anticipated loss of business and who
instigated a referendum in May of 1972 in which the plan was defeated by a
margin of two to one. The city commission then instituted the bag system,
which featured combined collection, but on a voluntary basis without an
imposed user fee and hence without contracted collection districts.
The initial impact of the user fee bag system in 1972 was a
re—allocation of households between the city and private haulers. There
were about 65,000 households in Grand Rapids in the early 1970's. House
counts early in 1972 showed that the city was collecting garbage (and an
unknown quantity of trash) from 43,000 houses. The refuse department
estimated that this figure would drop to 30,000 households presenting
refuse for combined city collection when bags were introduced. Two months
after the bag system was introduced the house count was 33,000 and private
haulers reported an increase in business. It appears that the larger
private haulers capable of combined collection benefited more than small
haulers, many of whom ultimately went out of business. In 1970, 120
licensed private haulers were operating in Grand Rapids, only 25 of whom
were equipped with trucks capable of hauling garbage or combined waste. A
spot check of some 30 private haulers listed in the Grand Rapids telephone
directory in 1978 indicated that the majority now deal only with commercial
or industrial customers or else operate exclusively outside Grand Rapids.
In February 1974 the "oil crisis" caused the supplier of Grand
Rapids' bags to cut production because the manufacturing process relied on
oil products suddenly in short supply. Responding on quite short notice,
the Environmental Protection Department substituted a system of refuse tags
for bags. These 3" x 6" tags could be attached to any refuse container to
indicate payment for city collection. Within six months the "city lost 21%
of its previous customers, a shift attributed to the relative unpopularity
79
-------
of tags. This led to the re-introduction of bags in June 1975, by which
time plastic bags were found to be no longer in short supply, although they
were available only at a much higher price than before. No house counts
have been conducted since that time, so it is difficult to know whether the
combined availability of bags and tags had the desired effect of increasing
the number of households choosing city collection.
Collection of Solid Waste in Grand Rapids
Grand Rapids is geographically divided into five collection
districts from which household refuse is collected on the same day each
week along 12 to 14 routes in each district. The location of collection
districts is shown in Figure 15. The city maintains a fleet of 23 trucks,
including nine rear-loading packers with a capacity of 25 cubic yards, six
31 cubic yard rear-loaders, and six 37 cubic yard side loaders. Two front
loaders are used for collection from municipal buildings and a small number
of public housing sites. All trucks are operated by two-man crews except
the six Shupak side-loaders acquired in January of this year, which are
operated by one-man crews and which are felt to have increased efficiency
on routes where they are used.
Municipal collection is non-mandatory, and private haulers may
operate in Grand Rapids so long as they comply with public health
regulations and are licensed by the Environmental Protection Department.
Industrial and commercial solid waste is not handled by the City. About 30
private haulers are listed in the Grand Rapids phone directory. It appears
that most of these collect from commercial customers or else serve suburbs
of Grand Rapids. Periodic surveys are made to determine the number of
households choosing municipal collection as opposed to private collection
or self-haul. The latest survey (July 1975) shows about 36,000 of the
city's 65,000 households using city collection. The bag system has the
advantage of not requiring records to be kept for each household. This
also makes provision of back-yard service impractical because with no
record of service requested collectors would have to enter every back yard
to search for city bags. Private haulers usually offer back-yard service,
and this appears to be the major reason why those opting out of city
collection do so.
Residents may purchase refuse bags or tags in any of 80 retail
outlets throughout the city including the city's 13 fire stations, 5 city
government offices, and 65 commercial outlets such as supermarkets. City
bags are made of thick plastic, have a 30 gallon capacity, and have a
distinctive bright orange color with the city seal imprinted for
identification. Refuse for collection must be left, properly bagged, on
curbsides no earlier than 7.00 p.m. on the evening before the scheduled
collection day. Only refuse in city bags or with city tags attached is
collected. At present about 40% of refuse collected is in city bags, 60%
is left with tags. During the fall 50-gallon city leaf bags and city leaf
tags are also sold for $0.10 and $0.05, respectively, which may be used
only for leaves and yard waste.
80
-------
Figure 15. Grand Rapids collection districts.
81
-------
Disposal of Solid Waste in Grand Rapids
Residential solid waste in Grand Rapids is hauled to a sanitary
landfill owned and operated by Kent County in Plainfield Township northeast
of the city (see map in Figure 16). Prior to 1970 the city operated
landfills within its boundaries. Increased quantities of refuse and lack
of open space eventually made this unfeasible. To a large extent the
suburban towns adjacent to Grand Rapids have faced the same problem; the
result has been increased cooperation on the regional level to develop a
disposal system operated by Kent County.
In 1970 Grand Rapids entered into an agreement with Kent County
whereby the city agreed to make exclusive use of county landfills for waste
disposal. The current Plainfield site has been in use since 1977, and is
expected to reach capacity in 1985. The city pays $6.16 per tonne for use
of the landfill.
Administration of the Grand Rapids Solid Waste System
Perhaps the biggest administrative change brought about by the bag
system has been the tendancy to become involved in marketing. The system
has two distinct advantages over other types of user fee systems that are
immediately apparent: there are no administrative costs associated with
billing or keeping track of the level of service to each customer; and
marketing activity provides a direct public relations link between the
Department of Environment Protection and the public. The latter is
difficult to quantify, but Department officials speak highly of this aspect
of the system. Bags were initially distributed through the city's 13 fire
stations and two downtown municipal office locations. The distribution
network was later expanded to include commercial outlets, retailers being
given four percent of the revenue for bags sold to cover purported handling
costs.
Besides the Refuse Collection Division, the Environmental
Protection Department has divisions dealing with waste water treatment, air
pollution contol, and sewer maintenance. Refuse collection is administered
autonomously through an enterprise fund financed out of revenues from sale
of bags and tags and a transfer from general funds. Revenue from sales
covers about half of costs (see Table 25 below). Disposal operations are
under the jurisdiction of the county. The Department director reports to
the City Manager, who in turn reports to the City Commission consisting of
the mayor and six council members.
DATA COLLECTED
Data on Quantities Disposed
The Kent County Department of Public Works records quantities of
waste delivered to landfills which it operates and bills its customers,
including the Grand Rapids EPD, monthly. Quantities hauled by municipal
trucks serving residential routes were available for the period from August
82
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SITES A Proposed Sites
Figure 16. Location of waste disposal sites.
83
-------
1972 to February 1978. These quantities are listed in Table 19 and
illustrated in Figure 17. Quantities billed to the Grand Rapids Parks
Department, which we used as a proxy for rate of litter, are listed for
most months in Table 19 as well. Because several different disposal sites
with different measurement methods were in use during this period, some
quantities were recorded in short tons and some in cubic yards. A
conversion factor of 0.29 tons per cubic yard (or 0.344 tonnes per cubic
meter) was used to compute total tonnage of municipally collected
residential and parks waste. The location of county disposal sites is
shown in Figure 16.
Grand Rapids residents may dispose of self-hauled residential waste
at county landfills for a charge of $3.50 per carload or truckload.
Quantities of self-hauled residential waste originating in Grand Rapids are
not separately available from other cash transactions, however. Quantities
collected by private haulers and delivered to county landfills were
available, but it was impossible to know what proportion of total
privately-hauled waste this represented or what proportion of this total
originated from Grand Rapids households without examining records of
private haulers and landfill operators.
Quantity of residential waste collected appears to show some
seasonal variation. Quantities are greater from April, when the snow cover
melts, though October, when leaves are collected, and about 20% less from
November to March. Apart from seasonal variation, quantities have remained
fairly constant over the five-year period with two exceptions: A
three-week strike by sanitation workers in July 1974 reduced quantity
collected for that month, and quantities reported before March 1973 appear
to be suspiciously low. Neither the 1976 price change nor the transitions
between bags and tags in 1974 and 1975 appear at first glance to have had
an effect on quantity collected.
Data on Collection
In addition to quantity of waste collected, Table 19 shows
quantities of metered bags and tags sold by the Refuse Department and an
estimate of weight per bag. The latter is illustrated in.Figure 18.
Figures for number of bags sold are derived from revenue records similar to
those in Table 20, which shows how revenue from tag sales were distributed
during the last six months of 1974. Note that 63% of tags sold (providing
62% of revenue) are distributed through retail outlets. Because these
figures represent wholesale sales, they are a poor approximation to numbers
of bags left for collection in any given month. Much of the variation
displayed in Figure 18 may therefore be due to stocking policies by
retailers. For example, an especially large number of bags and tags were
sold in June 1976, immediately before the price change took effect, and an
especially small number were sold in July 1976 as stocks were depleted.
Presumably, merchants and/or households were stocking up at old prices.
84
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TABLE 19. QUANTITIES OF WASTE COLLECTED AND CONTAINERS SOLD
Date
Aug. 72
Sep. 72
Oct. 1Z
Nov. 72
Dec. 72
Jan. 73
Feb. 73
Mar. 73
Apr. 73
May 73
Jun. 73
Jul. 73
Aug. 73
Sep. 73
Oct. 73
Nov. 73
Dec. 73
Jan. 74
Feb. 74
Mar. 74
Apr. 74
May 74
Jun. 74
Jul. 74
Aug. 74
Sep. 74
Oct. 74
Nov. 74
Dec. 74
Jan. 75
Feb. 75
Mar. 75
Apr. 75
May 75
Tonne s
collected
712
427
515
774
470
378
545
2, 608
3, 014
2, 331
1, 826
2, 048
2, 329
1, 807
2, 257
1,989
1, 357
1,908
1, 240
1, 435
1, 849
2,571
1, 994
805
2,053
1, 500
2, 065
1,503
1, 107
1, 685
1, 152
1,289
2,216
2,269
Cu. m.
collected
0
0
0
0
0
0
0
0
233
1, 141
767
3,026
4, 165
3,493
4, 632
4, 122
3,243
2,612
2, 541
2,950
3, 360
4, 293
3,453
891
4, 749
2,934
3,548
2,336
2,669
2,934
2, 190
2,452
2, 971
3,067
Total
collected
712
427
515
774
470
378
545
2, 608
3, 093
2, 723
2, 106
3, 089
3, 762
3, 009
3, 851
3,407
2,473
2, 807
2, 115
2,450
3, 006
4,048
3, 182
1, 111
3,687
2, 510
3,286
2,307
2,025
2,694
1,906
2, 133
3,239
3,324
Bags
sold
591,088
114, 352
288, 328
311,488
244, 345
306, 600
961, 187
263, 842
288, 202
363, 059
333,448
47, 830
266, 555
266, 028
359, 310
379,310
267, 037
NA
226, 051
314, 086
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Tags
sold
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
126, 970
14, 250
NA
132, 190
221, 846
Z67, 784
274,484
186, 556
251, 946
246,418
232, 602
155,658
236, 910
263, 064
Kgs. /
bags
1.2
3. 7
1.8
2. 5
1.9
1.2
5. 7
9.9
10. 8
7. 5
6.3
64.6
14. 1
11.3
10. 7
9. 0
9. 3
NA
9.3
7.8
23. 7
284. 1
NA
8.4
16.6
9.4
12.0
12.4
8.0
10.9
8.2
13.7
13.7
12.7
Parks
\va s t e
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
3.96
7. 94
6.43
7.03
9. 33
4. 81
0.99
0.20
NA
NA
NA
1.49
5.53
9. 04
(continued)
85
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TABLE 19 (continued)
Date
Jun. 75
Jul. 75
Aug. 75
Sep. 75
Oct. 75
Nov. 75
Dec. 75
Jan. 76
Feb. 76
Mar. 76
Apr. 76
May 76
Jun. 76
Jul. 76
Aug. 76
Sep. 76
Oct. 76
Nov. 76
Dec. 76
Jan. 77
Feb. 77
Mar. 77
Apr. 77
May 77
Jun. 77
Jul. 77
Aug. 77
Sep. 77
Oct. 77
Nov. 77
Dec. 77
Jan. 78
Feb. 78
Tonnes
collected
2,055
2,170
1,861
1,735
2,049
1,220
1,623
1,338
1,289
1,851
1,847
1,779
0
0
0
0
0
0
0
0
0
0
0
0
3,656
3,012
3,799
2,996
2,928
3,596
2, 194
1,831
1,960
Cu. m.
collected
2,567
3,499
2,994
Z,889
3,996
1,905
3, 136
2,097
2,454
3,939
3,801
3,284
9,296
8, 389
8, 314
9,984
7, 396
7, 342
8,304
6, 683
6,769
10,500
9,821
10,000
405
0
0
0
0
0
0
0
0
Total
collected
2,938
3,374
2,891
2,729
3,425
1,876
2,703
2,054
2, 134
3,206
3,155
2,909
3,199
2,887
2,860
3,436
2,545
2,528
2,858
2, 300
2,329
3,613
3,397
3,441
3,796
3,01Z
3,799
2,996
2,928
3,596
2,194
1,831
1,960
Bags
sold
4,308
33,089
102,168
56,668
93,296
113,056
109,781
99,845
NA
92,297
115,512
NA
264,931
35,463
108,913
91.Z68
107, 101
114,347
96,147
96,303
11,500
94,031
113,064
134,513
Z07.Z5Z
34,945
44,270
206, 147
122,555
135,287
113,890
106,248
110,760
Tags
gold
337,690
121,438
232, 346
190,364
111, 330
220,032
124, 108
175,638
NA
136,086
154, 160
NA
377,666
68, 63Z
187, 176
151,264
123,776
190,640
152, 832
114,216
168.148
179, 840
173,608
202,984
344,752
53, 762
79,352
328,504
212,976
203,728
149,088
139,304
168,360
bag
8.6
21.9
8.7
11. 1
16.7
5.6
11.6
7.4
NA
14.0
11.7
NA
5.0
27.7
9.7
14.2
11.0
8.3
11.5
10.9
8.3
13.2
11.8
10.2
6.9
34.0
30.8
5.6
8.7
10.6
8.3
7.4
7.0
Parks
wa ste
20. 09
30.50
12.51
6. 11
3.47
0.73
NA
NA
1. 57
6.34
17. 83
44.03
95.42
242.67
311.07
253. 29
88.99
39.46
35. 83
14.70
21.23
88. 36
79. 38
67.03
41.59
26.98
27.71
13.75
3.87
1.68
2.70
1.35
1. 11
Sources: Kent County Department of Public Works, Grand Rapids Environmental Protection Department.
86
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4000 -
3000 -
2000 -
1000 -
1972 T973 1974 F9751976
1977
Figure 17. Tonnes of solid waste collected, Grand Rapids.
87
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30 -
25 -
20 _
15 -
10 -
5 -
1972
1973
1974
1975
1976
1977
Figure 18. Kilograms of waste collected per bag (or tag) sold.
88
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TABLE 20. CITY REFUSE TAG SALES, 1974
MONTH
July
August
Sep cember
October
November
December
January
7. of Sales
FIRE
STATIONS
4,198
7,582
9,755
6,850
6,954
7,657
6, 5A2
49,538
32.03
TOTAL August - January
AVG. MONTH
ESTIMATED
ESTIMATED
August January
February June
FY 75 Revenues
RETAIL
SALES
7,776
12,806
14,938
18,298
10,310
15,494
16,426
96,048
62.10
- 141
23
- 118
- 273
CITY
HALL
592
804
951
862
571
819
935
5,534
3.58
, 761
,627
, 344
.000
COMPLEXES
329
459
512
676
391
579
590
3,536
2.29
TOTAL
12.895
21,651
26,156
26,686
18.226
24,549
24,493
154,656
FY 75 REVENUES BY LOCATION
Fire Stations: 87,442
Retail Sales 169,533
City Hall 9,773
Complexes 6,252
273,000
FY 75 REVENUE FROJECTION-From Sale of Tags 404,000
FY 75 REVISED PROJECTION-From Data Listed Above.... 273,000
Difference 131,000
Percent Difference 32. 437.
Source: Grand Rapids Environmental Protection Department.
During the first few years after introduction of the metered bag
system, the Refuse Division periodically conducted counts of the number of
residences where bags were left for municipal collection. The results of
these surveys are shown in Table 21. These figures are probably somewhat
less than the number of households receiving municipal service, since
households which generate less than a full bag of waste per week and which
are willing to leave refuse on their premises an extra week will not set
out a bag for collection every time, and since households on vacation are
not counted. The number of households receiving municipal collection
appears to have been decreasing, but there is a great deal of variation in
these figures, and the Refuse Division estimates that there has been an
increase in the number of households using municipal collection since 1975
due to the availability of both bags and tags and to the increased number
of retail outlets. They estimate that refuse is currently collected from
between 33,000 and 37,000 residences.
89
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TABLE 21. HOUSEHOLDS RECEIVING CITY COLLECTION
Date (week) Number Date (week) Number
28
27
19
18
25
20
Aug.
Nov.
Mar.
Jun.
Feb.
May
72
72
73
73
74
74
33,
34,
34,
36,
34,
35,
,072
,388
,183
,090
,700
,314
20
16
6
17
24
3
Aug.
Dec.
Jan.
Feb.
Feb.
Mar.
74
74
75
75
75
75
34
24
32
24
25
29
,151
,861
,473
,358
,070
,135
Source: Grand Rapids Environmental Protection Department.
Indirect support for the view that providing customers a choice
between bags and tags increased the number of households using municipal
collection may be inferred from Figure 19, which illustrates the relative
percentages of sales of bags and tags, together with figures from Table 19.
Re-introduction of bags in 1975 resulted in an overall increase in sales
from about 230,000 tags to about 300,000 bags and tags together. Most of
the increase came in the first three or four months as new customers were
attracted by availability of bags. After this initial period of adjustment
a pattern was established of about 40% of customers buying bags and 60%
buying tags. Note that although the price change increased the price of
tags relative to bags (cf. Table 26), there does not appear to have been a
shift to bag use as a result.
Finally, information on household income was taken from data on
personal income tax collected quarterly in Grand Rapids. These data are
shown in Table 22.
TABLE 22. INCOME TAX COLLECTED
1972-3
1972-4
1973-1
1973-2
1973-3
1973-4
1974-1
1974-2
1974-3
1974-4
$1,604,659
1,926,198
2,161,064
2,825,324
2,105,873
1,834,541
2,276,754
2,860,191
2,191,150
2,000,372
1975-1
1975-2
1975-3
1975-4
1976-1
1976-2
19.76-3
1976-4
1977-1
1977-2
$2,484,686
2,936,075
2,059,131
2,068,592
2,707,630
2,232,262
2,419,450
2,327,898
2,899,118
3,522,284
Source: City of Grand Rapids.
90
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50
45 -
40 -
g 35
o
IH
0)
30 -
25 -
20
1975
1976
1977
1978
80
Figure 19. Relative popularity of bags and tags,
91
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Data on Administration
Computerized cost accounting procedures were introduced in Grand
Rapids in July 1976. This system produces monthly statements listing
quantity of waste hauled and various cost components for each collection
vehicle. A summary of data from these statements is given in Table 23.
Quantities are presented here in metric tonnes; the original data were in
cubic yards from July 1976 to May 1977 and in short tons thereafter. Total
direct costs are total costs less overhead costs. Average and marginal
costs of collection were estimated monthly from these figures as shown in
Table 23; derivation of these figures is discussed below.
During the period for which cost data are available, three changes
occurred which might be expected to affect collection costs significantly.
In June 1977 collected waste began to be hauled to the Ten-Mile Disposal
Site (site no. 2 in Figure 16) instead of to the Sparta Disposal Site (site
no. 1 in Figure 16). The Ten-Mile site is somewhat closer, but comparative
figures on haul time, access, and dumping time are not available. In July
1977 collectors received a six percent wage increase, as shown in Table 24.
And in February 1978 six new side-loading vehicles were added to the fleet
of collection trucks. Average and marginal costs per ton both appear to be
rising over time, but there is considerable variation, and insufficient
data is available to attribute this increase to one particular factor. The
fact that figures for tonnes collected do not match figures in Table 19
indicates a combination of measurement error and the fact that some
landfill hauls billed to the Refuse Division were for special collections
not included in the computer reports.
We noted earlier that revenues from sales of bags and tags has
never been adequate to cover operating expenditures of the Refuse Division.
The relation of these two items is shown in Table 25 for the past five
years. In this table figures are taken from annual budgets. Table 26,
taken from various historical sources, shows the retail price of bags and
tags and the cost to the city for these items.
Finally, Table 27 presents an historical summary of changes in
household collection. Population estimates are based on Bureau of Census
data for Grand Rapids. In 1970 the city had 63,507 occupied housing units,
including 58,987 (86.6%) in structures of one, two, or three units. On the
assumption that the ratio of each of these figures to population has
remained constant, an estimate was made of the number of households
eligible for municipal collection. Estimates for number of households
receiving collection were available for the years 1972 to 1975. For 1976
and 1977, high and low estimates are shown, based, respectively, on a
projection of the trend for the first four years and on the Refuse Division
estimate of around 35,000. Bearing in mind the high level of uncertainty
in many of these figures, most of the subsequently derived figures seem
stable. Note that when the Refuse Division estimate for number of
households since 1975 is used, results are more in line with those before
that year, lending further credibility to this higher estimate.
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TABLE 23. COST OF COLLECTION, GRAND RAPIDS
Date
Jul 76
Aug 76
Sep 76
Oct 76
Nov 76
Dec 76
Jan 77
Feb 77
Mar 77
Jun 77
Jul 77
Aug 77
Sep 77
Oct 77
Nov 77
Dec 77
Feb 78
Average
Tonnes
collected
2,904
2,817
2,423
2, 860
2,388
2, 151
2,105
2,083
2,878
3,198
2,947
3, 064
3, 120
3,074
3, 103
1,951
2, 109
-1 2,692
Number
of trucks
14
15
14
15
15
14
14
14
14
16
16
16
18
16
16
15
19
15
Tonnes
per truck
207
188
173
191
159
154
150
149
206
200
184
192
173
192
194
130
111
178
Total
direct
cost
$40, 816
45, 312
41. 138
52, 132
42, 663
42, 749
46, 770
41, 202
50,668
57, 231
62, 132
67, 645
64,560
60.092
61, 123
51, 382
63,651
51, 726
Direct
cost per
tonne
$14.06
16.09
16. 98
18.23
17. 87
19.87
22. 22
19. 78
17.61
17.90
21. 08
22. 08
20. 69
19. 55
19. 70
26. 34
30. 18
19. 22
Estimated
marginal
cost
$12.63
13.06
17. 11
18. 84
17.93
18.62
21.41
21.73
18. 30
12.26
1 8.47
13. 72
16. 83
14. 29
11. 59
16. 17
26.48
16.44
R2 of
estimation
. 70
.88
.89
.98
. 76
. 80
.98
.86
.60
.64
. 81
. 43
.86
.84
.79
. 80
.94
I/ Average excluding February 1978.
Sources: Kent County Department of Public Works, Grand Rapids Environmental Protection Department.
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TABLE 24. BASE ANNUAL WAGE OF COLLECTION EMPLOYEES
Year FY 75
Sanitary worker $9,060
Refuse packer operator
Equipment operator I 9,503
(Fiscal year extends
FY 76
$ 9,610
10,400
10,005
from 1 July
FY 77
$10,400
11,190
10,795
to 30 June.)
FY 78
$11,024
11,856
11,440
Source: Grand Rapids Environmental Protection Department,
TABLE 25. RELATION OF OPERATING COSTS TO SALES REVENUES
1973
1974
1975
1976
1977
Total operating
expenditure
$ 978,811
829,385
817,356
969,295
1,117,527
Revenue
bags and tags
$507,081
437,514
290,575
441,360
507,354
% of expenditure
covered by
bag and tag sales
52.8%
52.8%
35.5%
45.5%
45.4%
Source: Grand Rapids Environmental Protection Department.
TABLE 26. PRICE OF BAGS TO HOUSEHOLDS AND COST TO CITY
Effective
date
Jun.
Mar.
Jan.
Jun.
Jun.
Mar.
1972
1974
1975
1975
1976
1977
Price
of bags
$.125
-
-
.167
.192
.192
Price
of tags
$ -
.10
.10
.10
.125
.125
Cost
of bags
$.0248
-
-
.0536
.0536
.0598
Cost
of tags
$ -
.0049
.0038
.0038
.0038
.0038
Source: Grand Rapids Environmental Protection Department.
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TABLE 27. ANNUAL COST AND COLLECTION FIGURES
xD
ui
Year
Population, July 1 (est. )— '
Eligible households
Households with city collection
% of eligible with city collection
Tonnes disposed
Bags/tags distributed
Kgs. /bag
Bags /household /week
Kgs. /household /week
Revenue from user charge
Revenue/ tonne
Revenue/ household /month
Total operating expenditure
Expenditure/ tonne
Expenditure/household/month
1972 1973
193,400 191,500
55,000 54,400
33,700 35,100
61.3 64.5
31,044
4, 102,408
7.57
2.25
16.99
$507,081
16. 33
1.20
978,811
31.53
2.32
1974
189,700
54,000
32, 300
59.8
32,534
2,419, 396
13.45
1.44
19.40
$437,514
13.45
1. 13
829, 385
25.49
2. 14
1975
188,000
53,400
27, 800
51.9
33, 232
2,984, 326
11. 14
2.07
16.09
$290, 575
8. 74
0.87
817,356
24.60
2.45
1976
186,200
53, 000
27, 200
51.2
33, 771
3,412,433
9.90
2.42
23. 91
$441, 360
13. 07
1. 35
969, 295
28. 70
2.97
1977 -^ 76-77 -1
184, 500
52,500
25.200 35,000
47.8 66.9
37,401
3,524,715
10.61
2.69 1.91
28.51 19.55
$507, 354
13.57
1. 68 1.13
1, 117, 527
29.88
3.69 2.48
I/ Low estimate of households receiving collection.
2_l High estimate of households receiving collection.
J}/ Current Population Reports, U.S. Department of Commerce, Bureau of the.Census.
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EMPIRICAL RESULTS
A metered-bag system might be expected to effect residential solid
waste behavior in two ways that are different from container-based systems.
First, the capacity of the bag is generally less than that of a waste can.
Therefore, a metered-bag system faces the resident with a smaller increment
of waste allowed between addition of a new bag. Second, because the bag is
consumed when filled, the resident faces a direct charge for each
additional bag used. With a container-based system, once the number of
containers is selected, there is no direct charge to the customer for using
the number of cans chosen. Thus, the metered bag appears to fall in
between the "ideal" system outlined in Section 4 above and a
container-based system such as that in Sacramento.
This hybrid nature of the metered-bag system makes the modeling of
consumer demand for waste collection services somewhat more tricky than
that of the container-based system. Fortunately, it turns out that it is
possible with a single equation model to estimate the effect of price on
waste generation regardless of whether the bag system is considered to be
more like the "ideal" system with a charge varying with weight or more like
the container system with the unit of capacity being the bag rather than
the can.
The Model
Recall that with an ideal system, the resident is charged for the
weight presented for collection each collection period. If the metered-bag
system were a reasonably good aproximation of the ideal system, the
resident would not be able to "hide" additional waste by filling each bag
with more waste. Therefore, the ratio of bags to waste would be constant
and the "price per pound" would be calculated as:
P = P-RAr- / (q/BAGS) (48)
JjALj /
where p is the price per pound, PBAG is c^e Pri-ce Per bag, q is the
weight of the waste presented for collection, and BAGS is the number of
bags used.
If a double log specification is assumed for the waste generation
function, it will look like:
al a2 a3 ....
q = aQ p y s (49)
where s represents seasonal factors, y is income, and q and p are
as before. Substituting (48) into (49) gives:
96
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cL- ~ 3, _ 3.« 3* Q
J. i I -•--i A f—+ C^ \ / C O \
= anP-DAr' (q/BAGS) y S (5U)
U £3-tt.Lj
If the assumption concerning the constancy of the waste to bags
ratio is correct, we can rewrite (50) as:
= a
o PBAG
where: an' = an (q/BAGS) :
Thus, we needn't make use of the data we have on the number of bags sold in
Grand Rapids. This is fortunate since, as we noted above, the data is on
wholesale sales and, therefore, incorporates inventory phenomena not on the
part of the household but on the part of the retail outlets.
Suppose that instead of being viewed as an ideal system, the
metered-bag system were merely another capacity-based system with a
different representation of capacity. Then, from the discussion in Section
4 above, the theoretical representation of the demand model would be
(assuming again a double-log specification):
b b b
q = bQ BAGS X y * s (52)
c c c
BAGS = cn p_.Af, X y 2 q 3 (53)
where the variables are defined as above. Substituting (53) into (52) and
simplifying gives:
' bl b2 b3
q = bo PBAG ^ s
Note that the specification in (54) is the same as that in (51).
Therefore, estimation of either equation provides information about both
97
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possibilities. The difference is that if the metered-bag system is
actually a capacity-based system, then the coefficients estimated for (54)
no longer represent elasticities of price and income. Assuming some
reasonable restrictions on the elasticities in (52) and (53),^however, will
allow us to determine the sign on the price and income terms if the actual
elasticities are to be negative and positive respectively.
For example, if it is assumed that:
0 > b > - 1, 1 + ax (1 + b3) > 0,
alb2
< a.
then the signs on bi and b2 would be as expected from economic theory.
Note, however, that the numerical estimates of b\ and b2 in (54) cannot
be construed as elasticities since they are complicated functions of other
parameters and are not identifiable.
Estimation
In order to estimate Equation (51) (or, equivalently, Equation
(54)), we must first transform it into the linear form:
ln(q) = In(b0') + b1' ln(pBAG) + b2' ln(y) + b3 a (55)
The next step is to identify the actual data series used in the estimation.
For total waste disposed, the series presented above was employed. Because
some of the data is expressed in cubic yards and some in tons, it was
necessary to make the two comparable. To do this, we used the formula:
TONS = 0.29 YARDS
The factor of .29 is based on experience from Grand Rapids. For the "price
of bags," we use the price of a bag. For the period during which bags were
not available and tags were used, we use the implicit price of -a bag based
on the price of a tag. That is, we see from Table 26 above that when tags
were $0.10, bags were $0.167. Therefore, when bags were not available but
tags were and they cost $0.10, we use .167 as the price of a bag.
Therefore, the series on the price of bags that was used is reproduced in
Table 26.
For income, a better substitute was available in Grand Rapids than
in any of the other cities. Grand Rapids has a city income tax whose rate
has not changed over the period estimated. Therefore, we use quarterly
observations on income tax collections as the surrogate for personal income
with the quarterly observation being used in all three months.
98
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For the seasonal variable, we use a dummy variable for Spring.
This dummy serves two purposes. First, as is evident from Table 19 above,
there is a high generation point in the months of May and June. Second,
personal income tax collections peak in April with the filing of returns.
Both of these factors are held constant by the use of the Spring dummy.
Applying ordinary least squares to (55) gives the following result
(standard errors in parentheses):
ln(q) = 7. 1 - . 33 ln(pRAr) + . 02 ln(y) + . 14 SPR (56)
(.46) ^ U (.25) (.10)
R2 = .08
N = 50
As we can see, the coefficients on price and income have the expected signs
but are insignificantly different from zero.
It is unlikely, however, that the effect of price or income changes
are felt immediately. If a distributed lag model is specified and
estimated the results are (standard errors in parentheses):
ln(q) = 5.4 - 1 2 ln(pBAG) + . 50 ln(pBAG)_ J + .29 ln(pBAG)_2
(.07) 11 • 1^)
- . 0009 ln(y) - . 43 ln(y) + . 70 ln(y) 2 + . 23SPR
(.37) (.42) -1 (.31) " (.11)
R2 = .20
N = 46
None of the coefficients on price are significant and only the second
lagged income term is significant (and of the proper sign).
The positive price elasticities are somewhat troubling even though
they are insignificant. Therefore, we next specified and estimated the
model (standard errors in parentheses):
ln(q) = 4. 4 - . 50 ln(pw A„)+ .40 ln(y) - + . 1 7SPR
(.49) (.21) ~L (.09)
R2 = .15
N = 46
99
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The signs of the coefficients are all as expected. The coefficient on the
income term is insignificant at the 95% level, although it is significant
at the 90% level.
LITTER AND ADMINISTRATIVE COST
Litter and Resource Recovery
The empirical evidence presented above for Grand Rapids provides
little or no support for a non-zero price elasticity. An indirect test for
such an effect would be to look for an increase in litter related to price
increase. No direct measure of litter was available; as a proxy variable
we used quantity of waste collected in city parks (see Table 19). A simple
test of the litter hypothesis is to see whether this quantity is
significantly related to the level of the real price of bags. The
following equation was therefore estimated:
ln(L) = b + b ln(P) + bs (57)
where L is the monthly total of parks refuse, pgj\G is rea^- Price Per
bag, and s is a seasonal dummy.
The following results were obtained (standard errors in
parentheses) using ordinary least squares:
ln(L) = 14. 0 + 5.4 ln(PR. ) - 0. 65WIN + 0. 95SPR + 1. 65SUM
(5.5) *AU (0.69) (0.67) (0.65)
R2 = .28
N = 45
The signs of coefficients are as expected, but only the dummy coefficient
for summer is significant at the 95% level, although the price coefficient
is significant at the 83% level. Hence the available data make it
impossible to reject the null hypothesis that price changes have no effect
on littering. Although the quality of these data is poor, the conclusion
is supported by impressions of Refuse Division personnel that price
increases have not resulted in noticeable increases in litter.
Although there are several firms in Grand Rapids which deal in
scrap metal and recycling of industrial and commercial waste, we discovered
only one, Recycle Unlimited, which deals primarily with recycling of
household solid waste. This small, non-profit organization maintains
thirteen locations in Grand Rapids where residents may deposit papers and
glass, plastic, or metal containers for recycling. The Refuse Division
cooperates with this organization, but neither the Division nor the Kent
County Public Works Department, which operates county landfills, has
recycling programs of its own. Two years ago, at the insistence of a group
100
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of citizens, a program was started to compost leaves collected by the city,
but this was discontinued when no market or other outlet could be found for
the composted product.
Administrative Costs
In comparison with other types of user charges, a metered-bag
system results in the elimination of administrative costs associated with
billing, monitoring service levels, and recording service starts and stops
to households. On the other hand, the system incurs costs of order,
delivery, and the sale of bags and tags, which are not present in other
systems. All user charge systems involve some costs of clerical labor.
Direct personnel commitments in Grand Rapids include one-third of a
secretary's time and that of a full-time clerk, a total cost of about
$15,000 annually. Tags cost the city $3.83 per thousand, or about 3% of
their retail price. Bags cost the city $59.75 per thousand. In addition,
commercial retailers, who account for about 62% of sales, are given 4% of
these revenues.
In 1976 a Columbia University study [11] found that on average 3.1%
of total collection costs were due to billing costs in cities with
municipal collection and user charges, based on a sample of 39 cities. If
we assume that, as with tags, 97% of the price of a bag in Grand Rapids
constitutes payment for service plus the value of the bag, then payments in
lieu of billing are about 5.5% of the user fee (.03 + .04 x .62). However,
there are at least two reasons why the "billing" costs of the metered-bag
system may be considered somewhat less. First, the public relations
benefits alluded to earlier mitigate some expenses, although these benefits
are difficult to quantify. And second, revenues from sales cover only
about 45% of total operating costs, so that "billing" costs are only about
2.5% of this total.
This brings us to another interesting aspect of the metered-bag
system — the fact that in general service is non-mandatory, although in
Grand Rapids some payment is mandatory for all households. Municipal
service is non-mandatory in two senses. First, licensed private haulers
are allowed to compete for customers and do so successfully, a particular
characteristic of Grand Rapids; and second, households are not required to
present any minimum number of bags each week as is the case with other
types of user charges, a general characteristic of the bag system. At the
same time, most of the costs of municipal service are financed out of
general funds, so these costs are borne by all taxpayers as a mandatory
fee, albeit one which varies with the taxes paid by each invidual
household.
This situation arose historically because the user fee was
originally intended to cover only the added costs resulting from the change
to combined collection rather than to cover full costs. Initial estimates
on this basis were that user fees should cover about 40% of full costs, and
this proportion has been roughly maintained as we saw in Table 25. This
means, of course, that the user charge system is not self-financing and
101
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that apartment dwellers and other households with private collection may
subsidize municipal customers. On the other hand, it could be argued that
refuse collection is in some ways a public good and that since the
municipality must accept all refuse presented in city bags, it acts as a
"collector of last resort." The inefficiencies associated with having more
than one collector in a geographical area are therefore created by
households who demand collection by private haulers, according to this
argument.
These arguments were not articulated by the Environmental
Protection Department in Grand Rapids, however, and we shall not develop
them further because they raise questions concerning the equity of user
charges which are beyond the scope of this study. Our purpose in raising
the issue is to point out that a user charge system may be combined with
tax-based financing, but only at the risk of losing the alleged advantages
of a self-supporting operation, and that particular charge structures are
often rooted more deeply in historical circumstances than in economic
theory.
Cost data available in Grand Rapids after July 1976 were used to
estimate cost functions for the period since that date. This in turn made
it possible to estimate bag and tag prices which would be required in order
to equate marginal revenue to marginal cost. As an example, consider data
from the month of September 1977. Using cross-sectional data on eighteen
trucks used during that month, the following function was estimated:
InC. = In a + b In TONS. (58)
where C^ is the direct operating cost of truck i and TONSj is the
quantity in tonnes of waste collected by truck i. The results, using
ordinary least squares (t-statistics in parentheses), were;
InC. = 3. 8 + . 81 In TONS.
1 (9.5) (10.0) 1
R2 = .86
This implies an average cost of
and a marginal cost of
C/TONS = a TONS13"1 (59)
9C/3TONS = b a TONS13"1 (60)
102
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Evaluating average cost directly from total tonnage and direct cost gives
an average cost per tonne of $20.69; substituting in equation (60) gives a
marginal cost of $16.83 per tonne.
Revenue per bag sold through commercial outlets is $.192 x .96 =
$.184. From Table 27, average weight per bag in 1976 was 9.9 kilograms, so
one tonne of collected waste went into roughly 100 bags. Marginal cost for
collection and disposal of one tonne of waste, therefore, is $5.98 for cost
of bags plus $16.83 for collection plus $6.16 for disposal, a total of
$28.97. Marginal revenue for collection of one tonne of waste was $18.40.
To bring marginal revenue up to marginal cost would require a price
increase of about 57% to $.30 per bag or $3.60 per dozen. By a similar
argument, marginal revenue from 100 tags was $23.37. To bring marginal
revenue up to marginal cost in this case would have required a 95% increase
to $2.45 for 10.
The above calculations were carried out for each month for which
data were available; the results of this analysis are shown in Table 23.
During the time in question, the cost of disposal rose from $6.06 per tonne
to $6.16 per tonne in August 1977, and, as shown in Table 26, cost of bags
rose from $53.60 per thousand to $59.75 per thousand in March 1977. The
price of bags and tags remained the same throughout; no estimate of
marginal revenue was made on a monthly basis, however, because of lack of
reliable information on number of bags actually collected. Figures for
February 1978 are excluded from averages in Table 23 because of the effect
of introducing new side-loaders into the collection fleet.
A pooled estimate of the cost function shown in equation (56) was
made using data from 15 trucks used throughout the period July 1976 to
December 1977. The results (standard errors in parentheses) were:
InC = 4.2 + 0. 71 In TONS
(0.2) (0.04)
R2 = .52
N = 234
For these 15 trucks, average direct cost per tonne was $18.25, implying a
marginal cost of $13.01 per tonne. Tracing once again the argument above,
this indicates that a 37% increase in price of bags to $3.15 per dozen and
a 63% increase in price of tags to $1.65 for 10 would have covered marginal
direct cost during this period.
Finally, pooled data were used to estimate the following equation:
InC = a + bjlnCTONS) + b2ln(T) + b3ln(WAGE) + b4HAUL
103
-------
where T is a time trend,
collection personnel, and
WAGE is average monthly base wage paid to
HAUL is a dummy variable with the value 1 before
July 1977, when waste was hauled to a more distant landfill, and 0
thereafter. The results (standard errors in parentheses) of estimating
this equation were:
InC = -15.1
+ . 73 InTONS +
(.04) (
11 InT + 2.03 InWAGE + . 02 HAUL
04) (1.54) (.10)
R = .59
N = 234
The signs of coefficients are as expected, but only those for quantity
collected and time are significant at the 95% level.
104
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SECTION 9
TACOMA, WASHINGTON
Tacoma, Washington has some fifty years' experience in financing
solid waste collection and disposal through user fees. Over the years the
city has considered or experimented with most of the various fee systems in
current use around the country. The present fee structure is a combination
of a container-based system and a location- or service-based system. Hence
it provides an example of the effects of combining several fee structures.
Residential households living in single unit or duplex structures
(79 percent of all households) receive mandatory weekly collection at a
rate determined by the number of containers presented for collection and
the level of carryout service provided. Households presenting refuse
within 7.6 meters of a legal collection point (usually curbside, but alleys
in some districts) are charged $2.45 per month for the first can and $1.15
per month for each additional can. Those presenting refuse between 7.6 and
22.9 meters from collection points are charged $3.90 for the first can and
$2.65 for each subsequent can. Between 22.9 and 61.0 meters, and over 61
meters, the base prices are $5.30 and $6.65 and marginal prices are $4.05
and $5.45, respectively. A charge of $1.60 times the number of cans is
levied for each flight of up to six stairs between points of collection and
presentation. These are monthly fees paid along with electricity, water,
and sewage bills on a monthly or bimonthly basis. In addition, occasional
extra bags of refuse left alongside regular cans are collected for a charge
of $.75 each. Residents are required to pay for the minimum service of one
can at less than 7.6 meters; about 30% of residential customers demand some
level of optional service, a proportion that has steadily increased
historically. Records from 1954 and 1958 indicate that in those years
about 92% of all households received the minimum level of service; in
recent years the figure has been about 75%. Households living in
structures of three or more units are charged jointly for service at
commercial rates. Finally, Tacoma residents may haul extra refuse to the
city landfill and dispose of it at no charge; about 40% of all household
refuse by weight is disposed in this way.
Income elasticities consistent with previous findings were found in
Tacoma, although they were not significant at the 95% level. Tacoma was
the only city among those studied where significant price elasticities were
found; this finding is complicated by the fact that there are two price
elasticities of interest — one for incremental containers and one for
location. The finding of a positive price elasticity for container price
suggests that some type of specification error may be the cause.
105
-------
This section contains a description of Tacoma's solid waste system,
some of the data on the system which we collected, a theoretical model of
the user fee structure found in Tacoma, and our conclusions about the fee
system.
THE TACOMA SOLID WASTE SYSTEM
Tacoma's solid waste system is constituted as a classified utility
under the Department of Public Works. This is an intermediate form between
a department of local government and an independent public utility. Like
most public utilities, the solid waste system must finance its operations
(including capital expenditures, debt service, and taxes) out of revenue it
collects rather than from general funds, and its staff, equipment, and land
are autonomous from local government. But unlike many utilities, its
governing board is the city council rather than a separate utilities
commission. Following the taxonomy developed by Savas [12], this is very
close to a franchise system whereby the municipality is the service
arranger and a "private" firm is the mandatory service provider and fee
collector, except that the franchise must always go to a single firm
"owned" by the municipality.
Collection of Residential Waste in Tacoma
Tacoma operates a fleet of 33 collection trucks, 22 of which serve
residential routes. Refuse is collected once a week from each residential
customer, and crews work a five-day week. Trucks are operated by two-man
crews sharing driver and collector responsibilities. The driver carries a
route book listing name, address, and current service level of each
customer, and in which a record of extra bags collected and changes in
service are kept. Three route supervisors handle most customer requests
such as requests for extra collections or service changes, and reports of
unsanitary conditions. The route supervisor posts were created between
1973 and 1975 at the same time as collection crew size was reduced from
three to two. The change in staff structure is felt to have resulted in
reductions in paperwork, more efficient routing, and improvements in
safety, equipment operation, and customer relations and service. Employees
of the Refuse Utility have been organized in the Teamsters Union since the
1940's; labor relations have been good in recent years.
The last major restructuring of refuse collection took place in
1929 when the Refuse Utility was inaugurated. Refuse had previously been
collected by private haulers on a purely voluntary basis. Of some 24,000
households living in Tacoma at the time, only 6,000 subscribed to
collection services, and while most of the rest disposed of their own
refuse in a satisfactory manner, enough did not as to create a serious
health hazard from garbage left in streets, on vacant lots, and along roads
on the edge of the city. One of the first actions of the new utility was a
massive clean-up campaign. A dump site was created in the swamp on the
eastern edge of the city which was used for over thirty years, creating
land for industrial development over former tidal flats.
106
-------
While the absence of legal constraints on household behavior gave
rise to social problems and consequent reorganization of solid waste
collection fifty years ago, an increase in legal constraints, among other
factors, has stimulated discussion of changes more recently. Laws
prohibiting the burning of household refuse came into effect in 1968 which
resulted in an increased quantity of refuse presented for collection.
Total quantity of refuse disposed increased by about 70% from 1968 to 1969;
quantity of household refuse collected by the city and self-hauled by
residents grew at the same rate. In July 1968 consideration was given to
switching to a flat-fee structure on a trial basis. This plan was rejected
because it was felt it would be difficult to revert to a variable fee after
the trial period and because it was felt that a flat-fee would bring about
an even greater increase in the quantity presented for collection. The
experience of Seattle seems to have influenced Tacoma's decision: in 1964
Seattle had gone to a flat fee, and the number of containers presented went
from about 1.75 cans per household to between two and three cans. The
number of cans presented in Tacoma seems to have remained fairly constant
at about 1.25 since 1969.
From an historical perspective, user fees in Tacoma have gradually
shifted from being primarily location-based to being increasingly
quantity-based. The original fee structure introduced in 1929 varied only
according to pickup location; this was at a time when almost all households
probably demanded one can per week or less. Since the introduction of a
variable fee for quantity collected in 1938, the marginal rate for added
quantity has steadily decreased as a proportion of the basic rate for
minimum service, while the marginal rate for added location-based service
has increased relative to the basic rate.
Further evidence of increased orientation toward quantity—based fee
variations can be seen in the consideration between 1974 and 1976 given to
several versions of the "bag" system, whereby residents would be required
to place rubbish in bags whose price includes the cost of collection and
disposal. It was estimated in 1974 that $50,000 to $100,000 in revenue
were lost annually by not charging for bags left for collection by
households who had extra refuse occasionally, but not often enough to
warrant purchasing an additional can of weekly service. In April 1974 the
Utilities Service Director put forward a plan to sell plastic bags through
the city's fire stations for 50 cents each to be used for these "occasional
extras." Consideration was also given to making a complete changeover to
the bag system for all household refuse collection. This latter plan was
rejected for several reasons: it would require changing the city's
ordinances regarding the storage of refuse in rodent-proof containers; it
was felt that plastic bags were too expensive (13 to 14 cents each); and
paper bags would deteriorate in the damp climate. Because of problems with
arranging for distribution of bags, the more limited bag system for extras
only was rejected also — shops and supermarkets were generally
unenthusiastic about serving as retail outlets, and fire stations were not
always sufficiently staffed.
107
-------
An alternative to the bag system was finally adopted in May 1976.
Collection crews are given cards on which to note the address and number of
bags for any extra pickups; a clerk records this information along with the
account number of the address where the extras were left, and the customer
is billed at the end of the month. The price of this service is set at a
level that makes it cheaper for households to use an extra can all the time
if they have an extra bag of refuse at least twice a month on average.
During the first year this system was estimated to have cost about $17,000
while developing added revenues of about $69,000.
Disposal of Waste in Tacoma
Tacoma is fortunate in having a landfill inside its city limits,
less than thirteen kilometers from any point in the city (see Figure 20).
Hauling costs for both the city and private residents taking refuse to the
landfill are considerably less than they might be if the landfill were
outside the city, so a great deal of effort has been devoted to prolonging
use of the present site. The original landfill in former swampland on the
eastern edge of the city reached capacity in 1960, when the present
landfill, a ravine southwest of the city center, was acquired. The 81
hectare site of the ravine was originally expected to serve Tacoma's
disposal requirements for ten years, but cost estimates in the mid-sixties
indicated that to prolong the life of the landfill would result in
considerable savings since no more suitable sites were available in or near
the city. Consequently, several projects have been undertaken with the aim
of resource recovery and reduction of the volume of waste disposed.
In 1972 the city acquired a Williams model 680 shredder under an
Environmental Protection Agency grant to evaluate the effect of volume
reduction of solid waste. During the first year of ^operation the shredder
produced about $4,000 in revenue from the sale of scrap. By 1976 the
shredder was processing ten thousand tonnes of refuse (out of 135 thousand
tonnes received annually at the landfill) at a cost of about $200,000, and
yielding some $7,000 in revenue from sale of scrap. About half the
materials processed were demolition material; the remainder consisted of
household appliances, scrap metal, wood products, and automobile tires.
Operation of the shredder has been limited by lack of transport
facilities for removal of the shredded product, a situation expected to be
remedied when a more extensive resource recovery system goes into operation
this year. The Refuse Utility has recently acquired a $2.4 million
resource plant which separates the shredded waste into light and heavy
fractions (in proportions which can be varied) by means of a blower and
airstream baffles, separates ferrous metals magnetically, and deposits
separated materials into trucks via conveyor belts. It is estimated that
this sytem, which became operational in May, will produce about $400,000
annually in revenue from sale of paper, scrap metal, and hogged wood fuel,
as well as extending the life of the present landfill by thirty to forty
years — well into the next century. Out of 400 tonnes of refuse received
daily, it is expected that 320 tonnes of wood and paper, and 33 tonnes of
ferrous metals will be recovered for recycling.
108
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ARTEBlAl SlflEfilS
TACOMA. WASHINGTON
CITY PLANNING DEPARTMENT
Figure 20. Map of Tacoma, showing landfill location.
109
-------
Tacoma's solid waste disposal system serves a total population of
about 177,000 — 157,000 city residents and 20,000 outside the city who
either haul their own refuse to the landfill or receive collection service
from haulers who utilize the Tacoma landfill. About 3.5% of refuse
processed at the city landfill originates outside Tacoma; figures are
unavailable on how much waste originating in Tacoma is disposed outside the
city, but the amount is probably negligible. The city collects 56% of
Tacoma's total (residential and other) solid waste, and another 15% is
hauled by residents to the landfill. Since 40% of the city's collection is
from residential sources, household solid waste accounts for 37% of the
total generated in the city (15% + 40% x 56%), or about 1.2
tonnes/household annually. The user fee charged to Tacoma residents for
household collection includes a charge for disposal of $6.17 per tonne.
Commercial haulers are charged $6.17 per tonne for refuse originating in
Tacoma, with a $3.00 minimum charge and a $1.25/hour dumping fee for each
hour per load over 1 hour. Commercial haulers are charged $9.92 per tonne
for refuse originating outside Tacoma, with a $5.00 minimum, and private
residents from outside Tacoma are charged $3.15 for refuse brought to the
landfill in cars and $5.00 for refuse brought in pickups. Tacoma residents
are allowed to bring household refuse to the landfill in cars or pickups
free of charge.
Administration of the System
Tacoma's experience in finding a way to collect user fees for extra
bags of refuse illustrates some of the advantages and disadvantages of a
combination fee structure. Because the fee structure has several parts
(varying according to number of containers, distance from collection point,
etc.) it has been possible to adjust the relative prices of these items as
costs have changed. In turn, this has meant that economic constraints have
been available in place of legal constraints such as limits on quantities
or pickup location. One of the alleged benefits of the bag system is the
elimination of administrative costs associated with recording service
levels and billing each household separately. But in order to realize this
benefit Tacoma would have to eliminate location-based variables from its
fee structure, since these require separate records and billing in any
case. For Tacoma, the card system involved few additional administrative
costs while still yielding most of the benefits of the bag system. In
general, once a particular fee structure is adopted it may be costly to
modify service or price. A combination fee structure is flexible in
allowing piecemeal changes in one of several areas independently, but
inflexible because changes must be compatible with all parts of the current
system.
Judging from press reports, records of council debate, and numbers
of complaints, the Tacoma Refuse Utility has caused little public
dissatisfaction or political controversy in recent years. The price of
collection and disposal in Tacoma has been consistently less than in
Seattle and elsewhere in the region. There have been press reports of
sharp debate over municipal vs. private collection and of allegations of
inefficient service in some communities in the region, but none in Tacoma.
110
-------
Since 1970 the Refuse Utility has recommended two fee increases and one fee
modification (the addition of the charge for extra bags). On each of these
occasions the council was satisfied that the fee increases were justified.
Each time, the council considered one proposal to abolish the Refuse
Utility in favor of private collection on a contract basis and another
proposal to apply reduced rates for carryout service to elderly and
disabled residents; both proposals were rejected each time by votes of 8 to
1. No conclusions can be drawn from such fragmentary observations except
to say the the Refuse Utility is at worst not sufficiently inadequate for
another system to be seriously considered and at best superior to any
available alternative.
DATA COLLECTED
Data on Quantities Disposed
Records are kept on a monthly basis of quantities disposed at the
Tacoma landfill. Tonnage of waste self-hauled by city residents and
tonnage collected by city packer trucks is shown in Table 28 for the years
1974 through 1977. During these years packer collection accounted for 40%
to .50% of total city collection, the remainder coming from commercial
sources. Packer routes covered all households with individual accounts
(those paying user fees), multi-family residential dwellings with
caster-box service, and a small amount of waste from commercial sources.
These figures therefore over-state the quantity of waste generated by
households paying user fees, and to a lesser extent they over-state the
quantity of collected residential waste. However, it was impossible to
estimate precisely the extent of this inflation from data available. When
annual totals were compared with estimated quantities listed in annual
reports of the Refuse Utility (see Table 32 below), it appeared that
between 6% and 15% of refuse hauled by packers came from commercial sources
and that between 62% and 92% came from households paying user fees.
Figures 21 and 22 illustrate the information from Table 28 on
quantities of household waste collected and self-hauled. Quantity of waste
collected appears to be rising very slightly over time, although most of
this increase is due to population growth, so that waste collected per
household is rising less sharply (cf. Table 32). Quantity of waste
collected also shows very little seasonal variation. Most of the annual
drop in February stems from its having fewer days. Quantity self-hauled
clearly exhibits seasonality, rising from a minimum in January to a maximum
in June. Quantity self-hauled also appears to be increasing over time, at
a rate larger than the increase in quantity collected. Figure 22 combines
the two lines in Figure 21 to show the relative proportions of household
waste collected (reading from the lower edge) and self-hauled (reading from
the upper edge). Seasonality due to variation in quantity self-hauled is
apparent.
Because disposal of self-hauled waste is free of charge to city
residents, the only costs to households for this option are transportation
111
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TABLE 28. QUANTITY-/ OF WASTE DISPOSED, TACOMA
Date
Jan.
Feb.
Mar.
Apr.
May
Jun.
Jul.
Aug.
Sep.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
Jun.
Jul.
Aug.
Sep.
Oct.
Nov.
Dec.
74
74
74
74
74
74
74
74
74
74
74
74
75
75
75
75
75
75
75
75
75
75
75
75
Residential-/
waste collected
3, 879
3,408
3, 772
4,499
4, 243
3,734
4,068
3, 820
3, 767
3,786
3, 752
3,882
4, 098
3,395
3, 550
3, 777
3,890
3,639
4, 042
3, 704
3, 984
4, 167
3,604
4,072
Self -hauled by
city residents
555
759
1,263
1, 972
2,166
2,381
2, 008
1, 945
1,686
1,428
1,005
757
1, 046
1, 007
1, 545
2, 334
2, 510
2,448
2, 052
2, 153
1,863
1, 313
1, 132
776
Other waste collected
from non-commercial
sources _'
126
54
77
71
83
50
83
86
119
230
33
83
252
98
151
96
99
90
75
94
258
72
65
90
(continued)
112
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TABLE 28 (continued)
Date
Jan.
Feb.
Mar.
Apr.
May
Jun.
Jul.
Aug.
Sep.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
Jun.
Jul.
Aug.
Sep.
Oct.
Nov.
Dec.
76
76
76
76
76
76
76
76
76
76
76
76
77
77
77
77
77
77
77
77
77
77
77
77
Residential
waste collected
3,786
3,253
3, 810
3, 871
3,646
4, 071
3,948
3, 895
4,069
3,488
3,819
3,797
3,413
3, 142
3, 755
3, 604
3,687
4, 131
3, 700
4, 167
4, 133
3, 727
3,771
3, 770
Self -hauled by
city residents
877
971
1, 505
2, 243
2,432
2,580
2,594
1,955
1, 779
1,423
1, 267
1, 055
953
1, 171
1, 313
2, 356
2, 280
3, 050
2, 278
1, 701
1, 680
1, 805
997
1, 004
Other waste collected
from non-commercial
sources
40
37
46
19
20
16
16
22
39
2
18
27
20
52
16
13
22
41
23
30
33
14
11
16
_!_/ All quantities in tonnes.
y Total collected by all packer trucks.
V Used as proxy for litter.
Source: Tacoma Refuse Utility.
113
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4000 -
3000 -
2000 -
1000 -
1974
1975
1976
1977
Top line - quantity collected.
Bottom line - quantity self-hauled.
Figure 21. Tonnes of household solid waste, Tacoma.
114
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Prrr.-nl
CoHrctrd
90%
Perr rut
Srlf-
Hanlrd
10%
B0%
70%
C,0%
SELF-HAULED
30%
40%
COLLECTED
Jan.
1974
1975
1976
1977
Figure 22. Percent of household waate collected and self-hauled.
115
-------
costs and opportunity costs of alternative activities. Assuming these
remain fairly constant, we would expect one effect of a price increase for
marginal cans to be an increase in self-hauling. In rough terms this
appears to be the case. In 1974, 73.2% of household waste was collected
rather than self-hauled. After a price increase in January 1975 the figure
fell to 70.2% for that year. The only other price change was a charge of
$.75 for extra bags introduced in April 1976, just before the summer months
when self-hauling peaks. Further substitution of self-hauling for
collection seems to have ensued — percentage of total waste collected
dropped again to 69.6% for 1976 and remained at that level for 1977-
Further effects on household behavior can be seen in Figures 23 and
24, which illustrate, respectively, the number of extra bags collected each
month and kilograms of waste per container (can or bag) each month. Two
trends are apparent in the first 19 months of operation of the extra bag
charge system — a seasonal variation similar to the one observed for
self-hauled quantities, combined with an exponential decline in the number
of extra bags presented. The exponential decline suggests the presence of
a "learning curve" in response to this particular fee modification.
Perhaps in the early months of the extra charge households continued to
leave the same number of extras, and only after they were billed did some
begin to substitute self-hauling or to order an extra can on a permanent
basis. Response time is longer than one or two months because fewer than
25% of households incur the charge in any given month.
Figure 24 illustrates the effect of the extra bag charge system on
stuffing behavior. In each of the two 12-month periods prior to
introduction of the system, 16.3 kg. per container were left for
collection. During the first twelve months of the extra bag charge system,
16.0 kg. per container were left for collection if extras are not counted,
and 14.3 kg. per container if extras are counted. In the seven subsequent
months the figures are 16.9 kg. and 15.3 kg., respectively. These
differences are significant at the 90% confidence level. It would seem
that before the extra bag charge system was instituted households left
extra bags rather than ordering extra cans, so that the recorded quantity
per container ordered includes quantities effectively "stuffed" into
containers. The extra bag charge system stopped this practice. The
meaning of the subsequent increase in waste per container is unclear,
partly because the increase is not statistically significant, and partly
because such an increase could be due either to households literally
stuffing containers more full or to households using containers more
efficiently by storing extra waste on their premises.
Finally, the quantity of waste collected from other non-commercial
sources was found to bear no significant relationship to quantities of
residential waste self-hauled or collected, or to price chang.es. To the
extent that this is a suitable proxy for litter and other forms of illicit
or socially costly disposal, we may infer that Tacoma's user charge system
has no significant effect on littering behavior.
116
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ii.ooo ^
10,000 .
9,000 -
8,000 -
7, 000 -
6,000 -
5,000 -
4,000 -
3,000
1976
Z
o
Figure 23. Number of extra bags collected, Tacoma.
117
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19 -
18 -
14 .
Price
Change
Extra
BaK9
V
1974 < 1975 ' 1976
N. B.: Upper line excludes extras; lower line includes extras.
1977
Figure 24. Kilograms per container collected.
118
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Data on Collection
Data on collection are from two sources: historical records of the
Refuse Utility, which contain price schedules and fragmentary information
about the history of the Utility dating back to 1929, and billing records,
available back to January 1973, which show the number of households billed
at each separate level of service (by number of cans, number of stair
flights, and increment of carryout service), and total number of extra bags
billed. Price schedules for residential collection effective January 1973
and January 1975 are given in Table 29. Data on numbers of households at
various service levels are summarized in Table 30 and illustrated in
Figures 25, 26, and 27.
TABLE 29. PRICES FOR RESIDENTIAL COLLECTION
Price schedule effective January 1973:
Distance from collection point*
No. of cans 0-25 ft. 25-75 ft. 75-200 ft. Over 200 ft.
1
2
3
4
5
6
$1.75
2.60
3.45
4.30
5.15
6.00
$2.65
4.45
6.25
8.05
9.85
$3.55
6.25
8.95
$ 4.50
8.15
11.80
* Add $1.00 per can for each flight of stairs carried.
Price schedule effective January 1975:**
Distance from collection point*
No. of cans 0-25 ft. 25-75 ft. 75-200 ft. Over 200 ft.
*
**
1
2
3
4
5
6
7
Add $1.60
Effective
$2.45
3.60
4.85
6.00
7.15
8.30
9.45
per can for
April 1976,
$ 3.90
6.55
9.20
11.85
each flight of
add $0.75 for
$ 5.30
9.35
13.40
stairs carried.
each extra bag left
$ 6.65
12.10
17.55
for collection.
Source: Tacoma Refuse Utility.
119
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TABLE 30. NUMBER OF HOUSEHOLDS SELECTING VARIOUS SERVICE LEVELS
Month
Jan.
Feb.
Mar.
Apr.
May
Jun.
Jul.
Aug.
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
Jun.
Jul.
Aug.
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
Jun.
73
73
73
73
73
73
73
73
73
73
73
73
74
74
74
74
74
74
74
74
74
74
74
74
75
75
75
75
75
75
No. of
households
39, 531
40, 347
40,497
39,680
40,043
39,988
39,973
40,036
40, 193
40,334
40,440
40,528
40,566
40,513
40, 504
40, 506
NA
NA
40,326
40,507
40,654
40,939
41, 114
NA
41,716
41,735
41,766
41,786
41,814
41,823
Households
with min.
service
29, 178
29,598
29,925
29,439
29, 389
29, 197
29,060
29,034
20,085
29, 162
29,217
29,351
29,387
29,381
29,398
29,366
NA
NA
28,968
29,020
29,050
29,308
29,579
NA
30,751
30,639
30, 846
30,928
31,013
31,048
Households
with more
than one can
9,943
10,336
10, 180
9,858
10,267
10,405
9,943
10, 336
10, 180
9,858
10,267
10,405
10,799
10,750
10, 723
10,761
NA
NA
10,985
11, 116
11,235
11,261
11, 155
NA
10,386
10,393
10,372
10, 338
10,311
NA
Households
with carryout
service
558
557
537
527
531
525
514
513
515
514
513
513
511
511
508
505
NA
NA
496
497
493
493
505
NA
466
462
456
458
453
NA
Extra
bags
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
(continued)
120
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TABLE 30 (continued)
Month
Jul.
Aug.
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
Jun.
Jul.
Aug.
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
Jun.
Jul.
Aug.
Sept.
Oct.
Nov.
75
75
75
75
75
75
76
76
76
76
76
76
76
76
76
76
76
76
77
77
77
77
77
77
77
77
77
77
77
No. of
households
41,652
41,666
41,801
41,951
42,083
NA
42,093
42, 102
42,157
42,355
42,201
42, 151
42, 140
42, 199
42,233
42,319
42,381
42,452
42,467
42,468
42,498
NA
NA
42,707
42,760
42,737
42, 800
42,974
43,060
Households
with min.
service
30,912
30,924
31,082
31,264
31,427
31,524
31,596
31,654
31, 760
31,947
31,735
31,668
31,601
31,596
31,648
31,764
31,895
32,019
32, 119
32, 169
32,246
NA
NA
32, 553
32,661
32,639
32,713
32,913
33,044
Households
with more
than one can
10,386
10,393
10, 372
10,338
10,311
NA
10,156
10, 106
10,055
10,067
10, 123
10, 144
10,203
10,272
10,258
10,231
10,163
10, 108
10,023
9,975
9,931
NA
NA
9,831
9,776
9,776
9,768
9,744
9,702
Households
with carryout
service
466
462
456
458
453
NA
444
447
446
445
445
439
436
435
430
427
426
426
427
426
422
NA
NA
4ZI
418
417
414
412
407
Extra
bags
0
0
0
0
0
0
0
0
0
0
8,659
10, 250
8,828
6,448
6, 240
5,424
5.058
NA
4, 922
3,543
4, 347
5, 167
5,572
7, 277
6, 021
6,455
5,647
4,686
4, 173
Source: Tacoma Refuse Utility.
121
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77%
76% .-
75% -
74% -
73% -
72% -
71%
1973 1974 19751976'1977
Figure 25. Percent of households at minimum service level.
122
-------
98%
97% 4
96%
77% J
76% J
75% -J
74% J
73% J
72%
>Z CANS
2 CANS
Price
Change
1 CAN
1973
1974
1975
1976
197
98%
L 97"
L 78%
U 77%
76%
L 75%
L 74%
L 73%
72%
N. B. : Top Line - Households with 1 or 2 cans.
Bottom Line - Households with 1 can.
Figure 26. Percent of households at various quantity levels.
123
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1.5%
1.4% .
1.3% .
1.2% _
1.1% -
1.0% _
0.9%
Price
Change
I
Extra
Bags
1973 1974 1975'19761977
Figure 27. Percent of households with carryout service.
124
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We noted earlier that only households in single-unit or duplex
structures are separately billed for solid waste collection. Figure 25
illustrates the percentage of such households demanding the minimum
required service — one can at curbside (or alleyway) location. The
short-run effect of price changes is as expected from initial inspection of
Figure 25. The price increase in January 1975 leads to an increase in the
percentage of households demanding minimum service, as the marginal price
of additional service becomes greater than willingness-to-pay for some
households. The introduction of extra bag charges leads to a decrease in
percentage demanding minimum service, as some households order extra cans
rather than incur charges for extras. The longer-run effects are more
puzzling, however. During periods between price changes, the overall
increase in personal income (using retail sales tax receipts, Table 31, as
a proxy for this variable) results in a gradual decline in real price. We
expect this to lead to a corresponding decrease in percentage of households
demanding minimum service. Furthermore, historical records show that in
1954 and 1958 about 8% of households demanded additional service,
indicating a long-term increase in demand for additional service. In 1973
and 1974 the percentage demanding additional service increased as expected,
but during the years 1975-1977 the percentage declined steadily. It is
unclear whether this represents a departure from a long-term trend or the
result of a fall in real income.
TABLE 31. TAXABLE RETAIL SALES*
CITY OF TACOMA, 1973-1977
Year Quarter 1 Quarter 2 Quarter 3 Quarter 4
1973
1974
1975
1976
1977
$152,225,411
179,621,223
186,438,554
197,460,809
219,721,862
$155,985,836
181,695,964
208,241,640
221,902,620
245,283,365
$187,040,079
190,579,107
214,056,812
228,375,907
246,901,539
$188,569,348
214,595,922
224,907,778
250,482,648
296,771,887
* Based upon local 0.5% county/city sales tax collections.
Source: Washington Department of Revenue.
Similar considerations apply when demand for service above the
minimum level is broken down into component factors of extra cans and
carryout service. Between November 1974 and January 1975, when the price
increase took place, 7.4% of the 2-can households switched to 1 can, 7.6%
of those at more than 2 cans switched to 2 cans, and 3.8% of the carryout
households switched to curbside service. The extra bag charge seems to
have led to some 2-can households switching to 1 can and to have had the
negligible effect on households at more than 2 cans or on carryout
customers. Over the five years for which data are available, demand for
extra cans follows the same pattern observed for non-minimum service, while
125
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the percentage of households with carryout service declines steadily.
Again, the long-term trend is unclear. The 1958 document referred to above
showed that 94.2% of all households had only one can in that year, and 2.1%
had carryout service.
Data on Administration
The Refuse Utility publishes an annual statistical and cost report
which contains extensive information on all aspects of the solid waste
system, broken down by type of service (including residential) and between
collection and disposal. Information from these reports for the years
1972-1977 related to collection and disposal of residential waste is
summarized in Table 32. Becuse these data are only available annually,
they were not used for calculations on quantity collected/disposed or level
of service demanded in modelling the solid waste system. Some
discrepancies between these data and those in other tables may therefore
appear. For example, the annual report takes the total number of separate
current accounts during the year for number of customers, which neglects
turnover of accounts and households billed in any given month. Also,
accounting procedures change from time to time, so that figures may not
always be strictly comparable from year to year. In the 1977 annual
report, the figure reported for total tons of residential waste collected
includes an estimate of waste collected from caster boxes at multi—family
dwellings. The figure appearing in Table 32 for this item has been
adjusted to make it comparable with figures for earlier years. Finally,
the figures in annual reports for quantity of waste collected are estimates
which take into account the fact that some refuse from packer trucks comes
from commercial customers. In Table 32 these appear as in the annual
report, which is why they are somewhat less than annual totals from Table
28.
EMPIRICAL RESULTS
The Tacoma case study is useful because it provides an opportunity
to evaluate the effects of both container-based and service-based fee
structures simultaneously. In addition, the data available in Tacoma was,
in general, more complete for econometric purposes than for the other case
study cities.
The Model
As discussed above in Section 4, the demand model for a city with a
combination fee structure (where the combination of choices is on
containers and service) can be expressed as:
q = f (C, S, z, y) (61)
C = g (pc, S, y) (62)
126
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TABLE 32. ANNUAL FIGURES RELATED TO QUANTITY OF HOUSEHOLD SOLID WASTE
AND COST OF PROCESSING HOUSEHOLD SOLID WASTE IN TACOMA
Number of customers (households)
Tonnes collected
Tonnes self -hauled
Total residential waste
Kg collected/wk/household
Kg total annual waste/household
Revenue billed to households—
Revenue/tonne collected
Pevenue/tonne total
Monthly revenue/household
1977
43,484
32, 379
20,587
52,966
14.3
1218.1
$ 1,503
46.42
28. 38
2. 88
1976
43, 324
31,051
20,682
51,733
13.8
1194. 1
$ 1,491
48.03
28.83
2. 87
1975
43, 033
30, 042
20, 178
50,220
13.4
1167.0
$ 1,421
47.29
28.29
2.75
1974
42,975
29,484
17,927
47,411
13. 2
1103. 2
$ 1, 018
34. 54
21.48
1.97
1973
42, 050
30,427
16, 761
47, 188
13.9
1122.2
$ 1,003
32.95
21. 25
1.99
1972
41, 867
30,495
19, 866
50, 361
14. 0
1202. 9
$ 984
32. 28
19. 54
1.96
System expenditures, thousands of dollars:
Direct operating costs
Total collection costs
Disposal costs
Total cost of service
Disposal cost of
self -hauled material
Total
Expenditures per tonne of
Direct operating costs
Total collection costs
Disposal costs
Total cost, collected waste
Total cost, all residential waste
$ 1, 148
1,587
268
$ 1,855
$ 127
$ 1,982
household solid
$ 35.46
49.01
6.17
55.18
30.97
$ 1,023
1,282
192
$ 1,474
$ 128
$ 1,602
waste:
$ 32.86
41. 17
6. 17
47. 34
30.95
$ 945
1, 097
184
$ 1,281
$ 124
$ 1,405
$ 31.46
36.50
6.13
42.63
27.97
$ 888
1,078
142
$ 1, 220
$ 86
$ 1, 306
$ 30. 10
36. 56
4. 82
41. 38
27.56
$ 711
901
124
$ 1,025
$ 69
$ 1, 094
$ 23. 37
29.62
4.09
33.71
23. 19
$ 740
906
91
$ 997
$ 59
$ 1,056
$ 24. 26
29. 72
2.99
32. 71
20.99
_!_/ In thousands of dollars.
Source: Tacoma Refuse Utility.
127
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S = h (p , C, y) (63)
o
Note that seasonal effects (z) are included for the waste generation
function but not for the choice of capacity or service. The reason is that
while waste generation may be expected to have seasonal components (as we
have seen in the other case studies), the transactions costs associated
with regular changing of either number of containers or service levels
discourage frequent changes resulting from seasonal effects per se.
If a double log specification is assumed for each of the equations
in (61) - (63) above, then the model would appear as:
al a2 a3 a4
q = aQ C * S ^ y z (64)
b, b, b,
C = bp 1S 2y 3 (65)
d d d
S = dQ pg L C * y 6 (66)
The reader will note that the number of extra bags presented for
collection is not included in any of the individual equations of the model.
This is because the program did not begin until April 1976 after the last
fee increase. Therefore, the only variation in price would be caused by
inflation reducing the real price. This, coupled with the fact that one
would expect a new program to expand over at least an initial period of
time would mean that any correlation between the two series may be
spurious.
Estimation
The first step in estimating the model (64) - (66) is to express
each of the equations in a linear form. This result is:
ln(q) = In aQ + aj In C + a2 In S + a3 In y -I- a4 In z
In(C) = In bQ + bj In pc + b2 In S + b3 In y
ln(S) = In dQ + dj In pg + d2 In C + d3 In y
128
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For income, sales tax receipts were used. The seasonal variable used is
precipitation (primarily because rainfall is relatively great in Western
Washington).
For the variables q , C , and S , we have used the detail
available in terms of the numbers of households selecting a particular
service. Therefore, the monthly quantity variable is in terms of tons per
household. For measures of the number of cans chosen and service level
selected, we use the "odds" of choosing more than the basic level offered.
That is, for cans, we use the odds of choosing more than one can of service
which is the ratio of the number of households choosing two or more cans of
service to the number choosing one can. Similarly for location, we use the
ratio of the number of households choosing more than "curb" service to the
number choosing the basic curbside service.
Using two-stage least squares, the resulting estimates are:
ln(q) = -5.5 + . 85 InC - . 34 InS + . 23 Iny - . 0002PRECIP (67)
(.93) (.77) (.22) (.003)
ln(C) = 3.2 - . 12 Inp - . 06 Iny + . 41 ln(q) + . 61 InS (68)
(.11) C (.13) (.42) (.12)
ln(S) = -5.9 + .09 Inp + 1. 76 InC + . 1 3 Iny + . 87 Inq (69)
(.15) S (.41) (.30) (.97)
As can be seen, the only significant explanatory variables are between the
levels of service chosen.
Since precipitation was not significant and because the dummy
variable representing the winter months appears to be important in many of
the other case studies, we next substitute the dummy variable for winter
for the precipitation variable. In addition we add to the waste generation
equation the variables for the number of extra bags (EXT) and the amount of
waste self-hauled (SELF). The results, again applying two-stage least
squares, are
ln(q) = -2. 88 -I- 1.0 InC - .42 ln(S) - .02 Iny - .08 WIN (70)
(.86) (.76) (.21) (.04)
-t- 2. 7 x 10"6 EXT + 9. 9 x 10 "9 SELF
(5.3xlO~6) (1.3xlO~8)
129
-------
ln(C) = 2.3 - .18 Inp - . 004 ln(y) + . 08 ln(q) + . 72 ln(S) (71)
(.05) C (.06) (.08) (.05)
ln(S) = -3. 1 + . 15 ln(p ) - . 0002 ln(y) - . 14 ln(q) +1.5 ln(C) (72)
(.06) S (.10) (.13) (.13)
The results of these regressions are interesting. Looking at Equation (71)
we see that there is a negative and significant price elasticity of .18.
That is, for every 10% increase in incremental can price, the percentage of
households choosing more than one can of service falls by 1.8%. However,
from equation (70) it appears that waste generation per household is not
significantly related to the percentage of households choosing more than
one can of service. If true, this would suggest that the way people
respond to increases in the incremental price of containers is by demanding
fewer containers and using those demanded more intensively. The price
elasticity for location is, however, positive and significant, suggesting
specification error.
A second approach to testing for the existence of negative and
statistically significant price elasticities is to derive the reduced form
equation for waste collected and to use OLS. The reduced form (excluding
SELF and EXT) can be shown to be:
i
' al a2 a3 a4
i = aops PC y WIN (73)
When OLS is applied to the logs of (13), the result is:
ln(q) = -2.3 - .47 Inp + . 59 Inp + .01 Iny - . 10 WIN
(.26) (.49) (.18) (.03)
R2 = .43
N = 41
Neither the coefficients on the price of an incremental can nor on location
are significant at the 95% level.
Because the prices on containers and location are set by the same
authority and change at the same time, it might be suspected that the
coefficient estimates are inefficient (but not unbiased) because of
multicollinearity. However, the two terms are not perfectly correlated.
To regress quantity disposed on one of the prices (either container or
location) would result in specification error. Further, the exact form of
specification error would be one of omitted variables and the omitted
variable would be positively related to the included variable(s).
Therefore, the coefficient estimated for the included variable would be
biased away from zero.
130
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LITTER AND ADMINISTRATIVE COSTS
Litter and Resource Recovery
As in other cities studied, we found no evidence to support the
hypothesis that imposition of user charges or an increase in price of
collection leads to increased littering or other forms of disposal with
excessive social costs. The use of a proxy variable for quantity of
litter, or to put it another way, the lack of data on quantity of litter,
means that this hypothesis has not been directly tested. On the other
hand, experience of solid waste personnel corroborates the view that
littering has not increased. Although it has been fifty years since litter
was a serious problem, the problem arose on that occasion because
collection service was non-mandatory. A similar situation occurred in 1973
in the recently annexed Hilltop district of northeast Tacoma. Refuse
collection had not been mandatory, and illicit dumping took place in
ravines in the district. Upon annexation, some residents (40%, according
to one news report) opposed mandatory city collection, preferring to
continue with private haulers. After a lengthy court battle, city
collection was imposed and both illicit dumping and residents' opposition
abated.
The presence of an easily accessible landfill where residents may
dump rubbish at no charge could also be an important factor in preventing
litter. At present the cost to the city of disposing of waste self-hauled
by residents is subsidized jointly from all other revenue sources, as is
the cost of collecting and disposing waste from other non-commercial,
non-residential sources.
Tacoma has this year instituted a large scale resource recovery
program which is expected eventually to recycle over 80% of the city's
waste stream and to finance itself from sale of recycled material. At the
same time, we found no evidence of either volunteer or commercial resource
recovery ventures for household waste. Whether this program will meet
expectations remains to be seen, but it is worth noting that in this case
the user charge system has not led to residents participating directly in
resource recovery operations on such a scale, and that the incentive for
developing the program has mainly to do with availability of large-scale
recovery technology and federal grants on the one hand, and problems with
disposal (lack of landfill sites), as opposed to collection, on the other.
Administrative Costs
During the six years for which annual reports are available,
revenues from residential user charges were on average 9% more than total
collection costs for collected household waste and 4% less than combined
collection and disposal costs. Thus costs and revenues from household
waste appear to be closely matched. Billing costs average 3.4% of revenue
over the same period, a figure close to the national average reported in
the 1976 Columbia study [13]. Historically, the Refuse Utility has been
self-supporting throughout its history and it has had to pay the same state
131
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and local taxes that a private hauler would under any other service
arrangement. This has been achieved without major cross-subsidies between
commercial customers, residential customers, and private haulers outside
Tacoma who use the city landfill, and prices are said to be comparable with
those elsewhere in the region. Thus the Refuse Utilty seems to have met
all criteria for allocative efficiency of solid waste services.
It is sometimes argued that user charges facilitate management
techniques such as careful cost accounting, and there is no evidence from
Tacoma which would contradict this.
132
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REFERENCES
1. Savas, E. S., Daniel Baumol, and William A. Wells. Financing Solid
Waste Collection. In: The Organization and Efficiency of Solid Waste
Collection, E. S. Savas, ed . Lexington Books, Lexington,
Massachusetts, 1976. pp. 79-96.
2. Wertz, Kenneth L., Economic Factors Influencing Households' Production
of Refuse. Journal of Environmental Economics and Management, 2(4):
263-272, April 1976.
3. Ibid.
4. Tolley, G. S. , V. S. Hastings, and G. Rudzitis. Economics of
Municipal Solid Waste Management: The Chicago Case.
EPA-600/8-78-013, U. S. Environmental Protection Agency, Cincinnati,
Ohio, 1978.
5. McFarland, J. M. Economics of Solid Waste Management. In:
Comprehensive Studies of Solid Waste Management, Final Report.
Sanitary Engineering Research Laboratory, College of Engineering and
School of Public Health, Report no. 72-3, University of California,
Berkeley, California, 1972. pp. 41-106.
6. Goddard, Haynes C. Incremental User Charges - Implications for an
Alternative Pricing Mechanism in Solid Waste Management.
7. Hudson, James Franklin. Demand for Municipal Services: Measuring the
Effect of Service Quality. Report R75-21, Civil Engineering Systems
Laboratory, Massachusetts Institute of Technology, Cambridge,
Massachusetts, 1975. 294 pp.
8. Stevens, Barbara. Pricing Schemes for Refuse Collection Services:
The Impact on Refuse Generation. Research paper no. 154, Graduate
School of Business, Columbia University, New York, 1977.
9. Stevens, Barbara. The Cost of Residential Refuse Collection. In:
Savas, ed., op. cit. pp. 97-120.
10. Edwards, Franklin R. , and Barbara Stevens. Local Government
Regulations of Residential Refuse Collection by Private Firms. In:
Savas, ed., op. cit. pp. 139-152.
133
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11. Savas, Baumol, and Wells, op. cit.
12. Savas, E. S., The Organization of Solid Waste Collection: A Framework
for Analysis. In: Savas, ed., op. cit. pp. 25-34.
13. Savas, Baumol, and Wells, op. cit.
134
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APPENDIX A
LIST OF USER FEE CITIES
The list of cities employing user fees was compiled from secondary
sources, particularly national or regional surveys of solid waste
collection practices. These surveys are not strictly comparable, and there
are problems with the reliability of each. Therefore, the combined list
should not be considered complete, representative, or even, perhaps,
accurate. It is, however, the most extensive list of user fee cities in
existence, and it has at least the merit of reconciling many contradictions
which have appeared elsewhere.
Most cities in the list have been derived from observations
contained in a 1975 Columbia University survey and American Public Works
Association surveys of 1964 and 1973. These are described in detail in The
Organization and Efficiency of Solid Waste Collection, E.S. Savas, ed. ,
1977, and Solid Waste Collection Practices. 4th edition, APWA, 1975.
Michael Traugott of the Inter-University Consortium for Political and
Social Research provided us with a complete listing of all cities from the
Columbia study where respondents indicated household collections were paid
for through user charges (universal telephone survey, question IIIA,
response 4, 5, 6, or 7). The Institute for Solid Waste, APWA, kindly
allowed us to inspect questionnaires returned in the 1973 survey, which
asked respondents whether residential collection was financed through user
fees, taxes, or a combination of both, as well as asking them to provide a
price schedule for collection service. Additional APWA survey information
was taken from tables appearing in Solid Waste Collection Practices and in
"Waste Collection Services: Cost and Pricing" by Stephen L. Feldman, from
Public Prices for Public Products, Mushkin, ed., 1972.
Two other published sources were used to compile the list:
"Economics of Solid Waste Management" by J. M. McFarland, from
Comprehensive Studies of Solid Wastes Management, 1972, contains a list of
all cities in California which had municipal collection of solid waste in
1969; our list includes all such cities reported to have some form of user
fee. The Economics of Refuse Collection by Kemper and Quigley, 1976,
contains a list of some cities and towns in Connecticut with private refuse
collection as of April 1974. Although Kemper and Quigley do not report
specific empirical findings on types or bases of user fees throughout
Connecticut, private collection entails a user fee by definition; hence
these cities are included in our list.
135
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Finally, in the course of selecting cities for case study we had
occasion to communicate directly with local government officials in a
number of cities during March 1978. These communications were in no sense
a survey, but rather clarification and confirmation of information
discovered elsewhere. This information is incorporated into our list.
Also in connection with our case study selection, Mr. Oscar Albrecht of the
Federal EPA provided us with a short list of cities which, according to his
personal records, employ user fees.
All cities cited as having user fees by any of these sources appear
in our list. When cities were cited by more than one source, only the
references giving more explicit information appears; e.g., where a city was
cited by two sources as having variable fees, but one source gave the fee
basis and the other did not, only the former is listed as a reference.
Consequently, where we list more than one source as a reference, each
provided the same information about type and basis of fee. Where different
sources provided conflicting information, the city name appears more than
once, with conflicting data and their sources beside separate listings.
Where our direct communications provided information that conflicted with
other sources, however, information and source references at odds with our
findings were omitted.
Conflicting information from different sources on the existence,
type, and basis of user fees may be explained in a number of ways. First,
cities may have changed from one system to another between the dates of
various surveys. Little is known about how many cities have changed to or
from user fee systems, and notions of why such changes occur are largely
theoretical or speculative. Second, a city may of course have several
haulers who charge according to different kinds of fee structures. And
third, survey instruments may have been imprecise or ambiguous.
As an example of'ambiguity, consider our experience with Provo,
Utah, one of the cities where we conducted a case study. Published results
of the 1973 APWA survey led us to believe Provo had a location—based
variable user fee. When we first contacted officials in Provo they were
uncertain as to what we meant by the term "user fee," but when we asked
whether collection was financed out of taxes or paid directly by
households, our meaning became clear. When we further asked whether there
was a flat fee or variable fee, we were told there was a flat fee. Since
this was at odds with the APWA report, we probed further, asking whether
customers were charged different rates for backyard and curbside
collection. There was, we were told, a flat fee for the former and a
different flat fee for the latter. Because terms like "variable fee" and
"user charge" are technical, it is easy to see how the Columbia survey
question could have been misunderstood — Provo does indeed have a flat fee
user charge for everyone getting the same level of service. Subsequent
questions in the Columbia survey might have clarified this conceptual
error, but evidently they did not. This may also account for why two other
cities where we conducted case studies — Tacoma and Sacramento — were
erroneously reported by the Columbia study to have flat fee user charges.
136
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Conflicts between the two national surveys (Columbia and APWA)
indicate error in one or both. We found 95 cities listed by both surveys
as having user fees. Of these, there were 44 instances where one or both
surveys either failed to indicate the type of fee used (flat or variable)
or else one or both surveys presented self-contradictory information. Of
the remaining 51 cities, there were 27 cases where both surveys indicated
the city to have a flat fee, and 14 cases where the two surveys obtained
opposing results. Assuming both surveys were equally prone to err, an
error rate of about 16.5% is indicated. If a similar rate of error
occurred in determining which cities had user fees and which did not, then
more than 150 of the cities we have listed do not in fact have user fees.
However, because the sampling universe of the surveys differs, and because
we do not have a full list of cities examined in both surveys, it is
difficult to pursue this point meaningfully.
No national or regional survey of solid waste collection systems
has ever systematically investigated the full variety of user fee systems
currently practiced. For Quigley and Kemper, information on user fee
systems was incidental to the main thrust of research. McFarland considers
only cities in California with Municipal collection, which are a minority
in that state. The APWA surveys have been distinguished by collecting data
on fee structures in all their complexity, but these data have been
presented only in illustrative form, and the surveys have been troubled by
low response rates. The Columbia survey proceeds far more systematically
and has therefore provided a basis for some useful statistical conclusions,
but its sampling universe was restricted and there is prima facie evidence
of observational error in particular cases. Two obstacles seem especially
important in all of these efforts: the difficulty of obtaining detailed
price data from private haulers, and the lack of generally accepted
nomenclature for survey use. For all of these reasons, it would seem that
a complete or representative list of cities employing user fees must await
further research.
137
-------
USER FEE CITY LIST
LEGEND:
TYPE: F = flat fee
V = variable fee
U = unknown
BASIS (for variable fee):
C = container
Q = quantity (metered bag)
L = location of pickup
F = frequency of collection
U = unknown
REFERENCES:
1 = phone conversation
2 = EPA (MERL - Cincinnati)
3 = APWA survey, 1973, transcription from raw data
4 = APWA survey, 1973, published results
5 = APWA survey, 1964, published results
6 = Columbia University survey, 1975, computer listing
7 = J. M. McFarland, 1971 California survey, pp. 45-48
8 = Kemper and Quigley, 1975 Connecticut study, p. 164
N.B. Cities listed more than once indicate conflicting references.
138
-------
TABLE A-l. CITIES EMPLOYING USER FEES FOR
HOUSEHOLD SOLID WASTE COLLECTION
CITT
A OB URN
CU1CKASAH
FOLEY
FULTONDAi.8
GLENCOE
UUNTSVILLE
LEEDS
HADISON
HONTGOMEBY
dOUNTAIN BR03K
HOUNIMN BROOK
ROOSEVELT
TUSCAL003A
7ESTAVIA HILLS
VESTIVIA HILLS
WETUMPKA
FAIRBANKS
AVONDALE
BUCKEYE
CHANDLER
HESA
STATE
ALABAMA
ALB
ALB
ALB
ALB
ALB
ALB
ALB
ALB
ALB
ALB
ALB
ALB
ALB
ALB
ALB
ALB
ALASKA
ALS
ARIZONA
ADI
ARI
API
ARI
PARADISE VALLEY API
PARKER
PEOR1A
TEBPE
WICKENBUBG
BEHTON
C AH DEN
EL DORADJ
JACKSONVILLE
LITTLE RJCK
ARI
ARI
ARI
ARI
ARKANSAS
ARK
ARK
ARK
ARK
ARK
NORTH Lli'TLE ROCK APR
PINE 3LO/F
SHERWOOD
ARK
ARK
TYPE
0? FB8
0
7
f
F
F
U
F
F
F
F
V
V
F
V
F
F
V
P
V
F
F
V
F
F
F
P
7
P
F
P
F
P
P
P
BASIS REFERENCE
FOR FEE
3
6
6
6
6
3
6
6
6
6
U 6
U 6
6
U 6
6
6
C 1
6
U 6
6
3,6
U 6
3
6
6
6
U 6
5
3
6
5.6
3,6
6
6
CALIFORNIA
ALABEUA COUNT I
ALHAflBRA
ANAHEIM
ANTIOCH
ARVIN
AUBURN
BANNING
BARSTOi
BEAUBONT
BENICIA
BERKELEY
BLITHE
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
C*L
P
P
P
V
P
V
U
7
U
V
7
P
3
5
6
C,P «
6
0 6
7
0 6
7
0 6
C,L 1
7
(continued)
139
-------
TABLE A-l (continued)
JITY
BRAHLET
BREA
BUENA PArlK
BUEBANK
BURLISGAilE
CALEXICO
CAHPBELL
CAfiLSBAD
CERES
CHOHCHILI.A
CHULA VIdTA
CHOLA VISTA
CLARKflONT
CLOVERUALK
CLOVIS
COACHELLA
COALINGA
COLTON
COLUS\
COMPTON
COHPTON
CONCORD
CORCORAN
CORONA
COSTA HESA
COVINA
CYPRESS
CYPRESS
DALY CITK
DAVIS
DEL HAR
DELANO
DINUBA
D1XON
DOS PAL03
DUNSHUIR
EL CAJON
EL CERRIXO
EL MONTE
ELSINORE
ELSINORB
ESCONDIDO
EXETER
FAIRFIELi)
FARIIERSViLLE
FILLHORE
FIREBAUGU
FOLSON
FOaNTAIM VALLEY
FRESNO
FOLL£RTOJ
STATE TYPE
OP FEE
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
&
F
P
f
0
U
V
P
F
U
F
V
0
V
F
F
P
P
U
V
F
V
U
F
U
F
P
V
F
V
F
F
V
P
U
0
V
V
F
P
V
F
U
V
U
V
V
P
P
F
U
BASIS REFERENCE
FOR FEE
U
U
0
C
c
a
c
c
U
c
tl
U
U
0
7
6
3
1,3, H, 5
3
7
6
6
6
7
3
6
7
6
6
6,7
6
6
7
3
5
U
7
6
3
1.3
3
6
3
3
6
6,7
'14
7
7
7
6
3
3
6
6
3,6
7
6
7
6
6
6
6
1,6
3
(continued)
140
-------
TABLE A-l (continued)
CITY
GALT
GILROY
GLENDALE
GONZALES
GUADALUPri
GUSTINB
HANFORD
HAYWARD
HBHET
HOLLISTE8
HOLTVlLLd
HUNTINGTON PARK
HURON
IMPERIAL BEACH
INGLEWOOJ
KERHAN
KING CITY
KINGS BURG
LA MESA
LA MESA
LAGUNA BaiACH
LEHOORE
LINCOLN
LINDSAY
LIVINGSTON
LOBPOC
LONG BEACH
LOS ALABITOS
LOS ALTOS
LOS GATOS
LUKEPOHT
LYNNWOOD
HADERA
HANHATTAN BtACH
HANTECA
HARIN COUNTY
SCFARLANJ
BENDOTA
HERCED
HILPITAS
HODESrO
HONTCLAia
HONTEREY
HONTEREY PARK
BOORHKAD
NAPA
NATIONAL CITY
NEtDLES
NED ARK
NEW BAN
HOBHALK BEACH
STATE
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CftL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
TYPE
OF FEE
F
F
P
?
V
F
F
a
u
u
u
V
u
f
u
F
V
F
F
V
F
U
F
U
0
F
/
1
?
V
U
F
U
F
F
V
V
F
U
F
V
F
V
F
F
\r
T
u
0
F
P
BASIS
FOR FEE
0
C
U
(J
C
u
C
u
C,F
U
U
u
0
u
REFERENCE
6
6
3
6
6
7
7
3
7
7
7
7
7
3,6
3.7
6
6
6
3
6
6
7
6
7
7
3
7
6
3
6
2
3,i»
7
3
6
U
6
6
7
6
6
€
6
3
3
6
6
7
3
6
3
(continued)
141
-------
TABLE A-l (continued)
CITY
NOVATO
OAKLAND
OCEANSIDci
ONTARIO
ORANGE
ORANGE COVE
OXNARD
PACIFIC OH07E
PACIFICA
PALH SPRINGS
PALH SPRINGS
PALO ALTO
PARLIER
PASAD2NA
PATTERSON
PEHRIS
PETALOMA
PICO HIV3RA
POflONA
POHTER7I1.LE
REDDING
REDLANDS
REDONDO UEACH
P.EEDLBZ
R I ALTO
RIO VIST*
RIVERSID^
RIVERSIDiJ
ROCKLIN
ROSEVILLi
SACRAMENTO
SALINAS
SAN BiRNADINO
S&N DIESO COUNTY
SAN FERNANDO
SAN FRANCISCO
SAN JACINTO
SAN JOAQUIN
SAN JOSE
SAN JUAN BAUTISTA
SAN JUAN CAPISTRANO
SAN LEANORO
SAN HAHCOS
SANGEfl
SANTA ANA
SANTA BARBARA
SANTA BARBARA COUNTY
SANTA CL*RA
SANTA CLARA
SANTA CRUZ
SANTA HA8IA
STATE
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
TIPS
OP PEE
U
7
P
P
P
F
P
7
0
P
7
P
F
P
U
P
V
U
P
a
p
F
U
P
P
If
P
V
V
p
V
V
p
U
p
U
p
U
V
U
?
U
?
p
p
?
U
7
p
0
7
BASIS
POR PEE
C,F
U
U
U
U
tl
U
c
c
U
U
(I
0
c
C,L
EEFERENCE
3
3
6
3,6,7
6
6
«,6
6
3
7
6
6
7
3,7
7
6
6
3
5
7
7
7
7
6
6
6
6
6
6
6
1
3
6,7
3
7
3, a
6,7
7
6
7
6
3
6
6,7
«,6
6
3
3,5
6
3,7
1.1
(continued)
142
-------
TABLE A-l (continued)
CITY
SANTA HOHICA
SANTA PAULA
SANTA ROSA
SARATOGA
SEAL BEAJH
SEASIDE
SELHA
SHAFTSR
SIGNAL HILL
SONOHA
STANTOW
STOCKTON
SUKNYVALB
1AFT
TEHACHAPi
THOUSAND OAKS
TOfiRANCE
TULARE
TULELAKE
TURLOCK
UPLAND
VALLEJO
VENTURA
VILLA PARK
VISALIA
VISTA
HA SCO
HATSONVILLB
HEED
WESTMORELAND
H HITHER
HILLIAflS
WINTERS
UOCDLAND
YORBA LINDA
STATE
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
CAL
TYPE
OP FEE
F
P
V
V
7
1
V
u
u
V
V
V
u
F
F
V
F
V
U
F
F
V
V
V
F
V
F
U
F
U
U
U
F
F
V
BASIS
FOR FEE
U
U
U
0
U
0
U
U
U
C,L
U
U
U
D
REFERENCE
1
6
6
6
6
6
6
7
7
6
6
6
3
6
6
6
3,7
7
7
6
€
1
6
6
3, a
e
7
7
7
7
3
7
7
6
6
COLORADO
ARVADA
BOULDER
CHERRY HILLS VILLAGE
COLORADO SPRINGS
DUltANGO
FOET COLLINS
GRAND JUNCTION
GHEENHOOJ VILLAGE
LAFATETTd
LOVELAND
HANITOU SPRINGS
PUEBLO
SHfcBIDAH
STBRUBd
IHEAT R1JGB
COL
COL
CCL
COL
COL
COL
COL
CCL
COL
COL
COL
COL
cot,
CCL
COL
0
V
V
V
V
F
F
V
V
u
V
V
»
*
V
0
U
U
C
0
u
a
n
u
c
0
3
6
6
6
3
5
3
6
6
3
6
6
6
5
6
(continued)
143
-------
TABLE A-l (continued)
CITY
STATE
TIPS
OF FEE
BASIS
FOR FEE
REFERENCE
CONNECTICUT
ANDOVER
AVON
BETHANY
BETHEL
BOLTON
BOZRAH
BRIDGEHACER
BHOOKFIELD
BURLINGTON
CANTON
CHAPLIN
CHESHIRE
CLINTON
CORNWALL
CBOHWELL
DANBURT
DARIEN
BAST WINDSOR
EASTON
PAIRFIELJ
FAEHIHGTJN
FRANKLIN
GLASTONBJR?
GRAHBI
GREENWICH
GRISHOLD
GROTON
GUILFORD
HARHINTOH
HEBRON
KILLINGL*
LEBANON
L£DTARD
LISBON
LITCHFIELD
BAHSFIELU
BERIDEN
HIDDLEBUBY
fllDDLETOWN
HONROE
BONTVILLJ
ft ORRIS
NEW CANAAN
NEW tllLPURD
NEHTOUN
NORTH BRADFORD
HOBBJUK
800HZCH
NORWICH
0 RANGE TJHN
CON
CON
con
CON
CON
CON
CCN
CON
CON
CON
CON
CON
CON
CON
CON
CON
CON
CCN
CON
CON
CON
CON
CON
CON
CCN
CON
CON
CON
CON
CON
CCN
CON
CON
CON
CON
CON
CON
CON
COR
COB
CON
CCN
CON
COM
CON
CON
CON
CON
CON
CON
U
0
7
V
u
0
a
u
u
a
a
i
0
D
a
V
0
0
V
u
F
0
U
u
»
?
V
?
u
0
u
u
u
V
u
u
»
V
u
V
a
u
u
u
9
V
t
F
V
V
0
n
u
u
u
u
0
u
a
u
0
o
n
n
u
u
u
8
8
6
6
8
8
8
8
8
8
8
6
8
8
8
6
8
8
6
8
6
8
8
8
6
6
6
6
8
8
8
8
8
6
8
8
6
6
8
6
8
8
8
a
e
6
6
3,6
6
6
(continued)
144
-------
TABLE A-l (continued)
CITY
PLYMOUTH
PROSPECT
R ELDING
SHARON
SIHSBURI
SOMEBS
SOUTH WINDSOR
SOUTHINGT3N
SPRAGOE
STABFORD
STRATFORD
THOflASTON
TRUHBULL
BARREN
MATER TOWN
WESTBROOK
WESTPORT
WILTON
WINDHAfl
HOODBRIDUE
HOODBURY
STATE
COH
CCN
CON
COH
COH
COH
CON
COH
CON
CON
CON
CON
CON
CON
CCN
CON
CON
CCN
CON
CCN
CON
TYPE
OF FEE
U
?
U
U
U
0
a
u
V
7
V
?
V
U
V
U
V
0
0
V
V
BASIS
FOR FEE
n
u
u
u
u
L
0
0
0
u
REFERENCE
8
6
3
8
8
8
8
8
6
6
6
6
3
8
6
8
6
8
8
6
6
DELAWARE
HILH1NGTGN
FLORIDA
APOPKA
BELLE GLADE
BELLE ISLE
BISCAYNE PARK
BOCA RATON
BOYNTON BBACH
CORAL GABLES
DANIA
DAViE
DAYTONA BEACH
DELRAY BilACH
DELRAY BjJACU
DUNEDIN
FLORIDA CITY
FORT LAUJERDALE
FORT PIERCE
GAINESVILLE
GULF BREEZE
GULFPORT
HOLLYWOOD
HOBESTEAi)
J.SDIAN RJCKS BEACH
JACKSONVILLE
JUPIT KB
KET WEST
KISSlnBEi
LANTANA
OIL
I
FLO
FLO
FLO
FLO
FLO
FLO
FLO
FLO
FLO
FLO
FLO
FLO
FLO
FLO
FIO
FLO
FLO
FLO
FLO
FLO
FLO
FLO
P10
FLO
FLO
FtO
FLO
F
F
V
P
F
F
F
V
V
F
F
V
F
F
F
P
F
F
F
F
F
V
P
F
P
U
F
6
6
€
6
6
6
6
6
6
-------
TABLE A-l (continued)
CITY STATE TYPE BASIS REFERENCE
OP FEE FOR FEE
LIGHTHOUSE POINT
LONGVOOD
MADEIRA
HADEIRA BEACH
HABGATE
MELBOURNE
HIAHI BEACH
BIAfll SHORES
HIAH1 SPaiHGS
HILTON
NORTH BAX
NORTH HIABI
NORTH HIAHI BEACH
NORTH PALfl BEACH
OAKLAND PAFK
OPA-LOCKA
OELANDO
PAHOKEE
PANAMA CITY
PfiHBROKE PARK
PEMBROKE PINES
PENSACOLA
PLANT CITY
PLANTATION
RIVIERA BEACH
SAFETY HAHBOR
SARASOTA
SOUTH BAY
SOUTH 8IAHI
ST AUGUSTINE
ST PETERSBURG
ST PETERSBURG
ST PETERSBURG
SURFSIDE
SHEETWAT£.H
TALLAHASSEE
TAHPA
TARPON SPRINGS
TEBPLC TZ BRACE
TREASURE ISLAND
WEST PALrt BEACH
HILTON HANORS
HINTER PARK
GEORGIA
ALBANY
ATLANTA
AUSTELL
AUSTELL
BDFORD
DECATUB
DOEAVILLii
FLO
FLO
FLO
FLO
FLO
FLO
FLO
FLO
FLO
FLO
FLO
FLO
FLO
FLO
FtO
FLO
FLO
FLO
FLO
FLO
FIO
FLO
FLO
FLO
FLO
FLO
FLO
FLO
FLO
FLO
FLO
FLO
FLO
FLO
FLO
FLO
FLO
FLO
FLO
FLO
FLO
FLO
FIO
OEO
GEO
RED
GEO
GEO
GEO
GEO
7 U
/ U
t
r
T U
0
F
f
F
F
F
F
F
F
F
F
F
F
F
V D
F
F
F
V Q
F
F
U
P
F
P
7 C
P
7 U
F
P
F
F
V
P
F
F
7 0
F
F
F
F
? U
F
F
F
6
6
6
6
e
3
6
6
6
6
6
6
6
6
6
6
3,
6
3
6
6
1,
6
1
6
6
3
6
6
3
1,
6
6
6
6
6
t,
6
6
6
3,
e
3,
3
6
e
6
6
3
6
6
3
5
6
6
0
(continued)
146
-------
TABLE A-l (continued)
CITY
EAST POINT
FAIRBURN
FOREST PARK
GTAINES¥Ii.LE
GARDEN Ci.II
JONESBORO
K-EHNESAB
LAHREHCE7ILLE
HACOH
MARIETTA
BORROW
PEHRT
POWDER SPRINGS
RIVERDALJS
HOSWELL
SMYRNA
THUNDERBOLT
BOISE CITY
IDAHO PALLS
MERIDIAN
TWIN FALi,S
STATE
GEO
GEO
GEO
GEO
GEO
GEO
GEO
GEO
GEO
GEO
GEO
GEO
GEO
GEO
GEO
GEO
GEO
IDAHO
IDA
IDA
IDA
IDA
TYPE
OF FEB
F
F
y
p
F
P
F
P
V
7
P
F
P
BASIS
FOR FEE
0
U
c
REFERENCE
6
6
6
3
6
6
6
6
3.
1.
6
6
6
6
6
6
6
3
3
6
3
6
6
ILLINOIS
ARLINGTON HEIGHTS
BARTONVI1.LE
BELLWOOD
CAR BONDAGE
CHAMPAIGN
DECATUR
DKS PLAICES
DOHNERS «R07S
ELHHOBST
GLEN ELLIN
GLENVIEW
HARQUETT3 HEIGHTS
HASON Cli'Y
NORMAL
NORTH8ROOK
OAK LAWN
PEKIN
PEKIH
RANTOUL
ROLLING 3EADOHS
SPRINGFIELD
08BANA
iASHINGTOM
BJBHETKA
ROOD HtTSR
ZIO»
ILL
ILL
IlL
ILL
ILL
ILL
ILL
ILL
ILL
ILL
ILL
ILL
ILL
ILL
ILL
ILL
ILL
ILL
ILL
ILL
ILL
ILL
ILL
ILL
ILL
IlL
F
V
0
F
7
\f
P
P
P
II
P
V
?
P
T
7
V
P
u
u
p
f
7
U
a
p
u
u
u
u
p
r
L
0
0
3
6
2
3
6
6
3,
3
3
2
3
6
3
3,
3
-------
TABLE A-l (continued)
^ITY STATE TYPE
OP PEE
CHESTERFIELD
DANVILLE
ELWOOD
INDIANAPOLIS
JACKSON
KOOEESVILLE
HUNCIE
NEW HAVEid
MEW HHITiLASD
PLYHOUTH
SOOTH BEND
SOUTH BEdD
IOWA
AHES
CEDAR FA^LS
DES HOINiS
DUBUQUE
ESTHERVILLE
MARION
OTTUMWA
PERRY
SIOUX CIIY
HAVER LY
WINDSOR dEIGUTS
KANSAS
AUGUSTA
DERBY
EL DOEADU
EflPOKIA
HAYSVILLli
HOTCHINSON
LAWRENCE
LEAVENWOSTH
OVERLAND PARK
PEARIE VILLAGE
S ALINA
TOPEKA
TOPEKA
VALLEY CONIES
WICHITA
WICHITA
WICHITA
KENTUCKY
HAZARD
0*£J*SBORQ
PW>pc*g
LOUISIANA
BATOf BOJGE
HARASS
LAPAYKTTii
BONROE
IND
IND
IND
IND
IND
IND
IND
IND
IND
IND
IND
IND
IOW
IOW
IOW
IOW
IOW
low
IOW
IOW
ICW
IOW
I OH
KAN
KAN
KAN
KAN
KAN
KAN
KAN
KAN
KAN
KAN
KAN
KAN
KAN
KAN
KAN
KAN
KAN
KEN
KEH
KEN
LOU
LOU
LOU
LOU
?
7
7
7
r
7
V
7
V
7
P
7
U
P
P
P
7
P
7
P
P
F
7
F
7
P
U
7
7
F
U
0
U
P
P
7
7
7
P
7
0
P
7
7
P
F
P
BASIS REFERENCE
FOR FES
D
0
U
U
U
U
U
U
U
U
Q
C
U
U
U
C
U
U
c
U
F
U
6
6
6
6
3
6
6
6
6
6
t)
6
3
3,1*
3
1.6
4
6
4
5
3
3
€
6
6
3,1,6
3
6
U
3
3
3
3
5
3,6
6
6
1,<»
6
6
3
6
1,5
6
€
6
5
(continued)
148
-------
TABLE A-l (continued)
CITY STATE TYPE
OF FEE
VENTON
HES1LAKE
BAISE
FALflOUTH
UORHAfl
LISBON
SCARBOROUGH
MARYLAND
COLLEGE PARK
COLUMBIA
ROCKVILLt;
SALISBURY
TOW SON
MASSACHUSETTS
AHHERST
BILLERICA
BOYLSTON
DALTON
HANOVER
LEE
H1LLBURY
NKEDHAM
NOHTHBRIOGE
OXFORD
ROCKPORT
TEWKSBURY
UPTON
WELLES LEY
WELLESLEY HILLS
WEST BOYLSTOH
V ESTFORD
1ICHIGAN
ADA
ALPINJi
ANN ARBOH TOWNSHIP
ARGENIIHc. T03NSHIP
ATTICA TOWNSHIP
BANGOR TOWNSHIP
BATTLE CrtEEK
C ALEONIA
CARROLLTON
CASCADE
CHARLOTTE
CHELSBA
CLATTQN IOVNSIIIP
const OCR
DAL TUN
Dfi HIFT
DEERFIELJ TOWNSHIP
EATON RAPIDS
ELBA TOWNSHIP
LOO
LOO
HAI
HAI
HAI
nA|
BAR
BAR
BAR
MAR
BAR
HAS
BAS
BAS
MAS
BAS
BAS
WAS
BAS
BAS
HAS
MAS
MAS
MAS
BAS
MAS
MAS
MAS
MIC
BIC
MIC
MIC
BIC
MIC
MIC
BIC
BIC
BIC
BIC
BIC
nic
BIC
BIC
HIC
BIC
MIC
MIC
P
t
V
7
V
0
F
V
F
U
U
F
V
V
»
U
V
7
U
7
7
U
P
7
U
U
7
7
7
7
V
V
7
7
U
7
F
7
7
7
7
7
V
7
7
7
7
BASIS FEFERENCE
FOR FEB
6
U
U
U
r*
U
U
U
U
0
0
0
0
U
U
U
U
U
U
U
U
n
U
U
U
tj
U
U
II
U
1)
U
6
6
6
6
3
I*
it
3
3
3
3
6
6
6
2
6
6
3
6
6
3
6
6
3
3
6
6
6
6
6
e
6
6
3
6
6
6
6
6
€
6
e
6
6
6
6
(continued)
149
-------
TABLE A-l (continued)
CITY STATE TIPE
OF FEE
FLINT TOWNSHIP
FOREST
FRANK ESMUTH
FRUITLAN1) TOWNSHIP
6AINES
GEORGETOWN
GRAND HAVEN
GRAND RAPIDS
HAHPTON lOWSSHIP
HOLLAND
J AMESTOWM
KALAMAZOO
KENTWOOD
LAKETON TOWNSHIP
LANSING
LANSING
LAPEE8
LAPEER TOWNSHIP
MAYFIELD TOMNSHIP
MERIDIAN
MONITOR TOWNSHIP
BONTROSE
MOUNT CLtHENS
NORTH HUSKEGON
NOETHFIELO
OREGON TOWNSHIP
OSBTEWO IOWNSHIP
PLAINFIELD
PORTAGE
PORTSMOUTH TOWNSHIP
HIGHLAND TOWNSHIP
ROSS
SAGINAW
SAGINAH TOWNSHIP
SALEM TOWNSHIP
SCIO
SOUTHFIELD
SPARTA
SPRING L«KE
T ALLMADGJi
TEXAS TOWNSHIP
THETFORD TOWNSHIP
WILLIAM Si'ON
WYOIUNG
YORK TOWNSHIP
ZEELAUO
MINNESOTA
ANOKA
BLOOBINGION
BBOOKLIN CENTER
BROOKLYN PARK
MIC
MIC
MIC
MIC
MIC
MIC
MIC
MIC
MIC
MIC
MIC
MIC
MIC
nic
MIC
MIC
NIC
MIC
MIC
MIC
MIC
MIC
MIC
MIC
MIC
MIC
MIC
MIC
MIC
MIC
nic
MIC
MIC
MIC
MIC
MIC
MIC
MIC
MIC
nic
1IC
MIC
MIC
MIC
MIC
MIC
MIR
MIN
MIN
MIN
7
7
F
7
?
7
V
7
7
7
7
7
7
7
F
7
7
7
7
7
7
V
F
7
7
7
7
7
7
7
7
7
7
7
7
7
p
V
7
V
7
7
7
7
7
7
a
0
u
p
BASIS REFERENCE
FOR FEE
0
0
0
0
u
a
Q
U
U
0
U
U
U
u
a
0
u
0
u
u
II
u
u
D
U
U
U
n-
u
u
u
u
u
0
u
u
u
u
u
u
u
D
6
6
€
6
6
6
6
1
6
e
6
6
6
6
€
6
6
6
6
6
6
6
3
6
6
6
6
6
6
6
6
6
6
6
6
6
3
6
6
6
6
6
6
€
6
6
3
3
3
3
(continued)
150
-------
TABLE A-l (continued)
CITY
COON HAPIDS
CRYSTAL
DULUTH
ED1NA
FAIRBAULT
FRIULEY
GOLDISH VALLEY
INVfcR HEIGHTS
HAPLEHOOU
HINNETONKA
ROCHESTER
SEDALIA
SOUTH ST PAUL
ST CLOOD
ST PAUL
STEWAHTVILLE
STATE
HIN
(UN
BIN
HIN
flIN
HIN
HIM
niN
HIN
HIN
HIN
HIN
HIN
BIN
HIN
HIN
WHITE BEAR LAKB (UN
CEN1RALIA
COLUMBIA
FERGUSON
GLADSTONE;
INDEPENDENCE
KANSAS C1IY
KIRKWOOD
NEVADA
POPLAR BLUFF
SPRINGFIsLD
SISSOURI
HRI
MRI
MRI
MRI
HRI
HRI
HRI
HRI
HRI
HRI
WEBSTER PROVES HRI
BILLINGS
GREAT FALLS
GREAT FALLS
HELENA
GRAND ISLAND
LINCOLN
SIDNEY
BOULDKR JITY
HENDERSON
LAS VEGAS
LAS VEGAS
RENO
SPARKS
NEW
HUDSON T4«N
LACONIA
HONTANA
HTA
HTA
HTA
HTA
NEBRAKA
NEB
NEB
NEB
NEVADA
NED
NED
NED
NED
NED
NED
HAMPSHIRE
NEB
NIH
TYPE BASIS
OF FEE FOR FEE
U
F
0
U
0
a
u
a
u
F
V U
F
U
P
F
V U
P
P
F
a
u
u
u
?
u
u
V U
u
V L
F
V U
F
0
¥ U
F
F
F
V 0
F
V U
U
V U
a
FEFfiRENCE
3
3
3
3
3
3
3
2
3.H
3
6
3
3
1
3
6
3
6
3.6
3
3
3
2, U
3
3
3
6
3
1,3
6
6
3
3
6
5
6
6
6
a
6
3
6
3
NEW JERSEY
EGG UARBOR TOWNSHIP HEJ
HOHOKUS
NEJ
V 0
v u
6
6
(continued)
151
-------
TABLE A-l (continued)
CITY STATE
HOPEHELL TOHMSHIP
HAPLEWOOU
HONMOUTH JCT
HULLICA fOHRSHIP
OAKLAND
PATER50H
SOUTH ORANGE
VINELAND
HASHINGTON-BER3BN
HYCKOFF
NEB HEXICO
ALBUO.UEROUE
CARLSBAD
HOBBS
L07INGTON
ROSWELL
MEW TOBK
AKRON
ALDEN
AMHERST
ARCADE
ARCADIA
AURORA
AVON
BALDWINS7ILLE
BALLSXON SPA
BRIGHTON
BROCKPOHf
CAMDEN
CAH1LLUS
CARLTON iOHN
CAZENOVIA
CUARLTON TOWN
CHILI
CICERO
CLAHKSTON
GOLDEN
CONSTANT1NA
CORINTH
DANS7II,Lii
DUANESBUiJG
EAST liREiNBOSH TOWN
tDEN
ELBRIDGE
GATES
CbENVIItLa
GBAND ISLAND
GHANDBt
GREECE TOWN
GREENFIELD TOWS
GROVELANtl TOWN
NEJ
NEJ
NEJ
HEJ
HEJ
NEJ
NEJ
NEJ
NEJ
NEJ
REN
NEB
NEM
NEH
NEH
NEY
NET
NEt
NET
NEY
NEY
NEY
NET
NEY
NEY
NEY
NEY
NEY
NEY
NEY
NEY
NEY
NEY
HEY
NEY
NEY
NEY
NEY
NEY
NEY
NEY
NEY
NEY
NEY
NET
HEY
NEY
NBY
NEY
TYPE
OF FEE
7
U
U
?
?
U
a
V
V
7
F
F
F
P
F
T
?
?
U
V
V
»
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
BASIS
FOR PEE
U
tl
U
0
0
U
0
U
0
u
U
U
U
1)
U
U
U
U
U
U
U
U
0
U
U
U
U
0
fj
U
U
U
U
U
U
U
U
0
a
REFERENCE
6
3
3
6
6
3
3
6
6
6
1,5,6
3
4
3
3
6
6
6
3
6
6
6
e
6
6
6
6
6
6
6
e
6
6
6
6
6
6
6
6
6
6
e
6
6
6
6
6
6
6
(continued)
152
-------
TABLE A-l (continued)
CITY
HAHLIN TOWN
HENRIETTA TOHN
HOLLAND
HOOSICK
IRONDEQIJUIT
LANCASTER
LEE TOWN
LENOX
LIVONIA
LYONS
LYSANDKR
MACEDON
MALTA
HARCELLUS
MARION TOWN
MEXICO
MILTON
MOHEAO
MOUNT HOHRIS
NEWARK
NORTH GRliENBUSH TOWN
OLD WESTOURY
CLEAN
ONTARIO TOWS
ORCHARD PARK
OSWEGO
PALttYHA
PAH IS
PARMA
PATCHOGUii
PENPIELD TOHN
PEHINTON
PITTSFORD
POESTENKILL TOWN
POMPEY
RA7ENA
RIDGEWAY
BIGA
HOTTEHDArt
S ALINA
SAND LAKd
SCUODACK
SHELBY
3KANEATELES
SOD03
STIfcLWATER
SVKDBN
VEBOHA TOHN
70ORHEBS7ILLS
WALWORTH TOWN
WEBSTER
STATE
HEY
NEY
NEY
NEY
NEY
HEI
NEY
NEY
NEY
NEY
NEY
NEY
NEY
NEY
NEY
NEY
NEY
NEY
NEY
NEY
NEY
NEY
NEY
NEY
NEY
NEY
NEY
NEY
NEY
NEY
HEY
NEY
NEY
NEY
NEY
NEY
NEY
NEY
NEY
NEY
NEY
NEY
NEY
NEY
NEY
NEY
NEI
NJY
NEY
NEY
NEY
TYPE
OP PEE
7
V
¥
y
7
?
7
?
V
V
V
V
\r
V
V
7
7
V
7
7
7
[)
F
7
7
7
7
7
P
F
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
BASIS
FOR FEE
U
0
0
U
0
U
0
U
0
U
U
0
U
0
U
0
U
U
0
U
U
0
0
U
U
U
U
U
U
U
U
U
U
U
U
0
U
U
U
0
II
U
U
U
U
a
a
HEIEHENCE
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
e
6
6
6
6
€
3
3
6
6
6
6
6
6
3
6
6
6
6
6
e
6
6
e
6
6
6
6
6
6
6
6
6
6
6
6
(continued)
153
-------
TABLE A-l (continued)
CITY STATE TYPE
OP PEE
WEST nONBOE
WESTMORELAND TORN
BILLIAflSGH
YORK TOBS
NEY
HEY
NET
HEY
V
?
?
V
BASIS REFERENCE
FOR FEE
a
U
U
0
6
6
6
6
HOBTH C4EOLIN1
HONROE
H ILHINGTOH
HINSTON-SAtBH
HORTH DAKOTA
FAHGO
GRAHD fOStKS
OHIO
AKRON
AHHERST
AURORA
AUSTINTOWN
BAZETTA
BEAVER
BEAVER CHEEK
BETHEL
BETHLEHEM
BLUFFTON
BOARDrtAN
BRACEVILLE TOWNSHIP
BROOKFIBLD
BUTLEEI
CANPIELD
CAHTOH
CENTERVILLE
CHAHPION TOWNSHIP
CLAY
CLINTON
COLUMBIA TOWNSHIP
COLUMBUS
CONGO 80
COPLEY
CORTLAMD
CUYAHOGA FALLS
CUYAHOGA FALLS
DELAWARE
ELYRIA
ENGLEWOOD
FAIBBORN
6ERHAN
GERHANTOUN
GOSHEN TOHNSHIP
GR££H
GROVE CITY
HARRISON
UOULAND
BUBBARD
HOC
»OC
HOC
NOD
NCD
OHI
OKI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
Oill
OHI
OHI
OHI
OHI
OHI
OHI
OHI
CHI
P
P
U
u
P
P
V
V
V
V
V
V
V
V
F
V
7
V
V
V
V
F
V
V
7
V
P
V
V
V
P
V
a
V
V
p
V
p
V
V
V
p
V
V
u
0
u
u
u
u
a
u
u
«
u
u
u
c
u
(1
u
0
a
u
u
u
u
u
u
u
u
u
u
u
6
6
3
Q
5
€
6
6
6
6
6
6
6
6
6
6
e
6
6
6
1.3
6
6
6
6
6
3
6
6
e
6
6
3
6
6
6
6
6
e
6
6
€
6
6
(continued)
154
-------
TABLE A-l (continued)
JIT I
HUDSON
KENT
KETTEHIN*;
LANIEB
LEBOH
LEXINGTON
LIBERTY
LIMA
LOUISVILLE
HACEDONIA
HAD HI7EK
H ADISON
MANSFIELD
HANTUA
HARION
MARLBORO TOWNSHIP
H AYHOOD
MCCOHB
(JUNTOS
HILTON
HONROE
HOOR£FIEi.D
HUNROE FALLS
NtB CARLISLE
NEW LEBANON
NEHBERRY
NEWTON
NEHTON FALLS
NILES
NORTH CANTON
NORTON
HORHALK
NORWICH
OSNABUBG
PERRY
PEEHY TOWNSHIP
PLEASANT
POLAND
RANDOLPH
RAVENNA
POOTSTOHH
SCIOTO
SEBBING
SHARON
SHAWNEE
SUEFFIELi)
SHEFFIELD LAKE
SHELBY
SILVER LAKE
SMITH
SOUTHINGION TOWNSHIP
STATE
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
oni
om
OHI
OHI
OHI
OHI
OHI
OIII
TYPE
OP FBB
V
V
0
7
V
1
1
F
7
V
7
V
F
7
7
V
0
7
U
?
V
V
7
V
7
7
7
7
7
P
7
a
7
7
7
7
7
7
7
7
7
7
7
7
Y
7
F
7
7
7
7
BASIS
FOR FEE
U
U
U
0
0
0
U
U
U
0
U
U
0
Q
U
U
0
U
U
U
0
[/
U
0
U
U
[J
a
u
U
'J
u
D
u
0
u
u
(J
u
0
0
0
(1
REFERENCE
6
6
3
6
6
6
6
6
6
6
6
6
1
€
6
f
3
14
3
6
€
6
6
6
6
6
6
6
6
6
6
3
6
6
6
6
6
6
e
6
6
6
6
6
6
6
6
6
6
6
6
(continued)
155
-------
TABLE A-l (continued)
JITY STATE TIPE
OF PEE
SPRINGFIuLD
SPBINGFIiSLD TOWNSHIP
STOH
STREEISBORO
STRUTHERii
SUGAR CREBK
SONBUHI
TRENTOH
TROTWOOD
TRUBO
UNION
VAN WERT
VIENNA TOWNSHIP
UARREN
BARREN
WASHINGTON TOWNSHIP
WAYNE
WEATHERSFIELD
WESTERVILLE
WINDHAH
XEN1A TOWNSHIP
OKLAHOMA
BETHANY
BRISTOW
BROKEN ARROW
BROKEN AHHOH
EDHOND
LAWTON
MIDWEST JITY
MIDWEST CITY
NICHOLS HILLS
NORHAN
OKLAHOMA 'JITY
OWASSO
S APULPA
SHAWNCE
SKIATOOK
SPENCriR
TULSA
TULSA
WARR ACR&S
YUKON
OREGON
ALBANY
ASIORI1
COHVALLI3
COTTAiiB uROVE
DALLAS
EUGENE
BEDFORD
HONHOUTB
OHI
OHI
OHI
OHI
OBI
OHI
OBI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OHI
OKL
OKL
OKL
OKL
OKL
OKL
OKL
OKL
OKL
OKL
OKL
OKL
OKL
OKL
OKL
OKL
OKL
OKL
OKL
OKL
ORE
ORE
QBE
ORE
ORE
ORE
ORE
ORE
V
V
1
V
V
V
V
F
F
V
T
V
V
F
V
V
V
V
F
V
V
F
F
F
V
F
F
F
V
F
F
F
F
F
F
F
F
F
V
F
F
U
U
(I
V
V
V
V
V
BASIS REFERENCE
FOB FEE
U
U
U
U
D
U
0
U
U
0
a
u
0
0
a
u
a
u
u
u
0
0
C,F
C
0
6
6
6
6
e
6
6
6
e
e
6
6
6
3,5,6
6
6
6
6
6
6
6
6
6
6
6
3.6
it
6
6
6
3,6
5,6
6
6
3,5
6
6
6
6
6
6
3
3
3
6
6
3,5
H
6
(continued)
156
-------
TABLE A-l (continued)
CITY STATE TTPE
OF PEE
OAKRIDGE
OAKRIDGK
OREGON CHI
PORTLAND
S ALEH
STAYTON
PENNSYLVANIA
ADAHS
ALLEGHENY
ALSACE TOWNSHIP
ALTOONA
AMITY TOWNSHIP
ANTIS
BAPNESBOttO
BEAVER PALLS
BERN TOWNSHIP
BERN TOWNSHIP
BETHfcL TJWNSHIP
BETHLEHEd
BIRDSBOHu
BOYERTOWN
BRECKNOCK TOWNSHIP
BUTLER TJ^NSHIP
CAHBRIA
CARLISLE
CODORUS tOWNSIIIP
COLE BROOKDALE
CONEHAUGtl
CONEKAGO
cun&u
DALLAS
DALLAS TJHNSIIIP
DOUGLASS TOHNSHIP
DUPONT
EAST COCALICO
EAST CONritlAUGH
EAST EARL TOWNSHIP
EAST HANOVER TOWNSHIP
tAST LAMP£TER TOWHSHP
EAST TAYLOR
EASTON
EBENSBURj
ELIZABKTUTOWN
EPHRATA XOWNSHIP
liXETER
FA1RVIEH TOWNSHIP
FAIRVIEW TOWNSHIP
FLEET WOOD
FORTY FOHT
FOSTE3
FRANKLIN
ORE
ORE
ORE
ORE
ORE
ORE
PEN
PEN
PEN
PIN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
F
V
V
J
V
V
V
V
V
?
V
V
F
F
F
V
V
a
V
V
V
V
V
V
V
V
V
V
F
V
7
V
?
V
F
7
V
V
V
F
V
7
V
V
F
V
V
V
V
F
BASIS REFERENCE
FOR FEE
0
c
c
u
u
u
D
U
U
0
U
0
u
II
0
u
u
u
IJ
u
u
!J
[J
u
u
u
II
u
II
u
IJ
u
17
u
[J
11
u
u
6
6
3
3
3
6
6
6
6
6
6
6
6
3
6
6
6
3
6
6
6
6
6
6
€
6
6
6
6
6
6
6
6
6
6
6
6
6
6
3
6
6
6
t
6
e
6
6
6
3
(continued)
157
-------
TABLE A-l (continued)
JITY STATE TYPE
OF FEE
FBANKLIN TOWNSHIP
FREEDOM
FREELAND
F PEEL AND
GEISTOWN
GIRAHD
GHEEBE TOWNSHIP
HAHPDEN
HARRIS BOBG
HELL AH TUWHSHIP
UIGHSPIHB
HOLLIDAYSBURG
JACK SOU
JENKINS TOWNSHIP
JEN HER
JOHNSTOWN
KENHORST
KINGSTON
KUTZTOWN
LANCASTER
LITTLESTOWN
LONDONDEBRY TOBNSUIE
LONGSHAMt? TOWNSHIP
LOWER ALLiiN
LOWEH ALSACE TOWNSHIP
LOWER SArfAHTA TOWNSHP
LOWER WINDSOR TOWNSHP
LOWER YOJER
HANHEIM
HAXATAWNf TOWNSHIP
MEY£BSDAi,E
HIDDLE PAXTON TOWNSHP
MIDDLES EX
NILLCREEK
HILLERSBURG
MOUNT JOt
MUHLENBEtiG TOWNSHIP
NANTICOKi
NANTY-GLO
NEW BBIGdTON
NEW CUMBERLAND
NiSW HOLLAND
NEWBERRY TOWNSHIP
NEWTON TOWNSHIP
NORTH EAST TOWNSHIP
OIL CITY
OLEY TOHMSHIP
PAINT
PATTON
PENN
PENN TOWUSHIP
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PIN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
7
»
f
T
?
F
?
»
F
V
F
1
V
?
/
?
P
V
F
V
V
V
V
F
1
V
V
F
V
V
V
V
V
V
V
p
F
F
V
u
F
\f
7
V
7
F
V
F
7
7
7
BASIS REFERENCE
FOR FES
a
0
U
0
U
u
u
u
u
u
u
0
u
u
u
0
0
u
u
u
u
a
a
n
u
u
u
u
u
0
u
u
0
6
6
6
6
6
6
6
e
1,6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
e
6
6
€
6
6
6
6
€
6
6
6
e
2
e
6
6
6
6
3
6
6
6
6
6
(continued)
158
-------
TABLE A-l (continued)
UITY STATE TYPE
OP VV.V.
PITTSBURGH
PITTSTON
PLYMOUTH
PORTAGE
PROVIDENCE TOWNSHIP
READING
HIGHLAND
RICHHUND TOWNSHIP
ROARING SPRING
ROBESON TOWNSHIP
SALISBURY
SHADE
SHAlliJ TOWNSHIP
SHARON
SH1PPENS3UHG
SHREWSBURY TOHNSHIP
SILVER SPRING TOWNSHP
SOMERSET
SOUTH UEIDEL3ESG TBSP
SOUTHMONT
SPANGLER
SPRING
STATE COLLEGE
STONYCREiJK
STRABAN TOWNSHIP
SHATARA i'OHHSHIP
T YRONK
UNION Cli'Y
UPPER AL^EN
UPPER LEACOCK TOHNSHF
UPPEH YOuDER
M ARBIHSTiiR
JEST COCALICO TOWNSHP
WEST EARL TUWNSHIP
WEST HANOVER
WEST LAMPtTER TOWNSHP
WESTHONT
WINUBER
WORtlLEYSiiURG
WRIGHTS VlLLii
YORK TOWNSHIP
RHODE ISLAND
NORTH KldGSTOWN
WESTERLY
SOUTH CAROLINA
AIKEN
ROCK HILL
SOUTH DAKOTA
ABERDEEN
RAPID CITY
SIOUX FAi.LS
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PEN
PFII
RHI
soc
sec
SOD
SOD
SOD
F
?
f
V
V
V
V
V
?
V
V
V
F
F
P
V
V
V
V
F
V
V
F
P
V
V
V
V
V
V
F
a
V
V
V
V
P
F
F
V
V
u
u
F
F
?
P
U
BASIS REFERENCE
FOR FEE
U
0
U
U
U
0
U
U
u
u
u
u
u
IJ
u
u
IJ
fl
u
u
u
u
u
u
u
n
i)
L
3
6
6
6
6
6
6
6
6
6
6
6
3
3
6
6
6
6
e
6
6
6
3
e
6
6
6
6
6
6
6
3
6
6
6
6
6
6
6
6
6
3
3
3
3
3
3
6
(continued)
159
-------
TABLE A-l (conbinued)
CITY STATE
TENNESSEE
NASHVILLE- DAVIDSON
TEXAS
ABILENE
ALICE
AMARILLO
ANSON
ARANSAS PASS
ARLINGTOU
AUSTIN
BEAUMONT
BEAUMONT
BELLMEAD
BIG SPRING
BROWNSVILLE
BRYAN
BtJRKBURNiTT
BURLESON
CANYON
CASTLE HILLS
COLLBGt .STATION
COLLEYVILLE
CORPUS CHRISTI
CORPUS CliRISTI
DALLAS
DENISON
DENTON
tDCOUCH
EL PASO
FAR HERS BRANCH
FOREST HILL
FORT HORTH
FRItNDSHOOD
GARLAND
GRAND PRAHIE
GRAPEVINE
GROVES
HAHLIN
HARLINGEN
HURST
LA FEHIA
LA 1-IARQUji
LACY-LAKiiVIEW
LAKEVIEH
LIVE OAK
LUBDOCK
HABSFIELU
MCALLEN
BCGREGOR
MERCEDES
MESQ01TE
TEN
TEX
TEX
TEX
TEX
THX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TYPE
OP FEE
V
?
U
t
F
F
F
F
?
V
F
P
F
F
F
F
P
P
F
T
P
V
V
P
U
F
F
U
F
V
F
F
F
P
F
F
F
P
F
F
F
F
V
F
P
F
F
F
F
BASIS
FOR PEE
U
L
U
U
U
L
L
U
BEBERENCE
6
1
3
3,U,6
6
6
3.6
3
3,6
6
6
3
e
6
6
€
6
6
6
6
5,6
6
3,«
6
3
6
6
3
6
1,3,U
6
1
3
6
6
6
6
6
6
6
6
6
6
6
6
6
6
e
3
(continued)
160
-------
TABLE A-l (continued)
JITY
MIDLAND
MINERAL dELLS
N SDiiRLANU
ODESSA
ORANGE
PEAR RiDGE
P1IABR
PLANO
PORT ARTHUR
PORT HECHES
PORTLAND
HIGHLAND HILLS
ROBINSON
SAN ANGELO
SAN ANTOrtIO
SAN BSNITO
SAN JUAN
SANSOM PARK VILLAGE
SCHEETZ
SHERMAN
SHERMAN
SINTON
SLATON
STAMFORD
TiMPLli
TEXAS CITY
TYLER
VIDOfi
WACO
WACO
WFST ORANGE
WHITE SEflLEMENT
WHITES3080
WICHITA FALLS
WINDC3ES1'
HOODWAY
STATE TYPE BASIS
OF FE3 FOR FEE
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TSX
TEX
TEX
TEX
TEX
TEX
TRX
TEX
TEX
TEZ
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
TEX
P
P
F
P
F
F
F
P
F
r
p
p
F
P
V Q
F
F
F
F
V C,L
F
F
F
F
F
F
F
F
F
V U
F
F
F
V L
F
F
REFERENCE
5,6
3
6
1.3,6
6
6
6
3
6
6
6
6
6
6
1
6
6
6
6
3
e
e
6
6
3
6
3,6
6
3,6
6
6
6
6
1
6
6
UTAH
BOUNTIFUL
CLEARFIELD
FARHINGTON
LAYTOH
HURRAY
NORTH OGDKN
OGDEN
PLEASANT GROVE
PROVO
ROY
SANDI Cl'l't
SPRINUVI^LE
WASHIHGTON T3ERACE
WEST JORJAN
UTA
UTA
UTA
UTA
UTA
UTA
UTA
UTA
UTA
UTA
UTR
UTA
OTA
UTA
F
F
P
P
F
P
F
P
V L
F
F
?
?
F
6
6
6
6
6
6
6
6
1.3
6
6
6
6
e
(continued)
161
-------
TABLE A-l (continued)
CITY STATE TYPE
OF FEE
BASIS
FOB FEE
REFERENCE
VE.1HONT
RUTLAND
VIGINIA
BRISTOL
HABBISONdUHG
PEARISBUHG
PORTS MOOT tl
HAYNESBORO
VER
VIR
VI B
VIR
VIR
VIR
U
a
U
V
?
V
C,F
C
3
(4
3
li
6
3
WASHINGTON
ANACOHTE3
AUBURN
BELLINGfUM
BOTHELL
CLYDE HILL
DES MOINtS
EVERETT
ISSAQUAH
KISKLAND
LONGVIEH
MARYSVILLE
HERCER IJLAND
MILTON
MONROH
OLYMP1A
PUYALLUP
REDHOND
6ENTON
SEATTLE
SEATTLE
SEATTLE
SNGHOPIISd
SPOKANE
SPOKANE
TACOHA
VANCOUVER
Y AKIMA
WEST VIRGINIA
DUN BAB
WHEELING
WISCONSIN
BROOKFIELD
CALEDONIA TOWN
DELAFIEL1)
DUNN '10HN
EAU CLAIHE
ELB GPOVS
GiNESEE
GRAND CHUTE TOHS
HOWARD
LISBON
HEQUON
HAS
HAS
HAS
HAS
wa.S
WAS
WAS
HAS
HAS
HAS
HAS
HAS
HAS
HAS
HAS
HAS
HAS
HAS
HAS
HAS
WAS
HAS
HAS
HAS
HAS
HAS
WAS
HEV
HEV
HIS
HIS
HIS
HIS
HIS
HIS
HIS
HIS
HIS
HIS
HIS
T
r
a
p
V
V
V
V
V
F
P
V
F
F
U
F
V
V
P
V
V
P
V
F
V
V
V
F
P
V
V
V
V
F
V
V
V
V
V
V
C,F,L
U
0
U
0
C
U
U
C,L
C
U
C,L
C,L
r*
C,L
U
U
0
U
U
U
U
U
U
U
4
6
2
6
6
6
6
6
t»
3
6
6
6
6
3
6
6
4
3
1)
(.
6
5
6
1,5
14
1,3
6
3
6
6
6
€
3
6
6
6
6
6
6
(continued)
162
-------
TABLE A-l (continued)
:ITY
BEETON
HUSK EGO
NEW BERLIN
OCONOBOHUC
PEHAUKEE
POLK
RICHFIELi)
SALEM
SUMHIT
VERNON
HATERFOHJ TOWN
b AOKESHA
NEST BENJ
STATE TYPE BASIS REFERENCE
OF FEE FOR FIE
HIS V U
HIS V U
WIS
HIS
HIS
HIS
HIS
HIS
HIS
D
U
U
U
U
0
HIS 7 0
HIS ? U
HIS T 0
HIS V U
6
6
6
6
e
6
6
6
6
6
6
6
6
HYOHING
CASPER
LARAHIE
HOCK SPRINGS
SHERIDAN
HYO
HYO
HYO
HYO
163
-------
APPENDIX B
MULTINOMIAL LOGIT DEMAND MODEL OF TACOMA
INTRODUCTION
This appendix is taken from a paper delivered by Robert J.
Anderson, Jr., of MATHTECH at the Fifth Annual Research Symposium of the
Solid and Hazardous Waste Research Division of the Municipal Environmental
Research Laboratory, U.S. Environmental Protection Agency, at Orlando,
Florida, on March 26-28, 1979.
MODEL AND RESULTS
To model the demand for solid waste collection services in Tacoma,
I have employed both a multinomial logit model and a regression model. The
multinomial logit model is used to characterize choices among the
alternative levels of service offered in Tacoma while the regression model
is used to explain data concerning total quantities of household waste
generated.
Choice Among Alternative Services
Tacoma offers its residents a choice among a number of different
collection services distinguished by the number of cans presented for
collection and the location with respect to curbside at which these cans
are presented. In all, approximately 16 different service levels are
offered, ranging from 1 to 6 cans per collection at various distances from
curbside. (Virtually no subscribers elected options to have "flight of
stairs" service. These options are therefore ignored in my analysis.)
The basic hypothesis on which the statistical model of service
selection used here rests is that the probability that any given subscriber
will choose a given service level is related to certain attributes of that
service level and certain individual characteristics of the subscriber. In
the particular case at hand, the service attributes would be such things as
its monthly cost, number of cans, and distance from the curb; subscriber
characteristics would be such things as family size, income, and other
household characteristics.
The available data report the frequency with which subscribers
chose the different available services in each of 59 months spanning the
period January, 1973 to November, 1977. The schedule of fees for the
various available services changed once during this period (in January of
164
-------
1975), and the charge for collection of extra bags was increased from zero
to $0.75 per bag in May of 1976.
The model used here to characterize service choice data is the
multinomial logit model of qualitative choice. This model, as noted above,
expresses the probability that an individual will choose a given
alternative as a function of the characteristics of the alternative
available, the characteristics of the chooser, and interaction terms
between alternative and choice-maker attributes. The basic form of the
model is as follows:
"
a'X.
e
E(e
i=l \
+ js.'z. + r.'W..
2'Xi + /3[Z. + y^W..^
/
where P.. = the probability that the jth individual will be observed
to choose the ith alternative.
M.. = number of alternatives.
a = K, x 1 vector of parameters.
X. = K, x 1 vector of attributes of the ith attribute.
/?. = K7 x 1 vector of parameters.
1 ^
Z. = K9 x 1 vector of attributes of the jth individual.
J ^
V. = Ko x 1 vector of parameters.
W-- = K-^ x 1 vector of interaction variables formed from
products (or other combinations) of alternative attributes
and individual attributes.
This is the general form of the model I have estimated using data on
service choices in Tacoma.
Before discussing the precise form of the model estimated and the
statistical procedures employed, some explanation of why the multinomial
logit model was chosen to characterize solid waste collection service
choice may be in order. There are, in fact, two reasons. First, it can be
shown that the logit model can be derived from a theory which explains the
distribution of choices observed in a population of consumers with
differential preferences. In particular, it can be shown that starting
with some assumptions about the nature of consumers' preferences and the
nature of the random differences that distinguish individual preferences,
equation (B-l) can be derived by assuming that consumers choose the most
165
-------
preferred alternative available to them. The probability distribution
implied by equation (B-l) may thus be interpreted as predicting the
probability distribution of choices that would be made by a population of
utility maximizing consumers. Second, among the alternatives available for
modeling choice among distinct alternatives, the multinomial logit model is
computationally the most tractable. These two factors — consistency with
economic theory and computational considerations — have led to the choice
of the multinomial logit.
Unfortunately, the data available are not adequate to estimate the
logit model in its most general form (as shown in (B-l) above). In
particular, the data contain no information on the characteristics of
individual subscribers in Tacoma that could be matched with subscriber
choices among services. My working assumption is thus that the
distribution of these characteristics in the population has remained
roughly constant over time, and that these characteristics can be treated
as a part of the random variation in individual preferences. The remaining
explanatory variables in the model are attributes of alternative services,
including price, number of cans, and distance from the curb at which cans
are placed.
Employing this specification, the unknown parameters of the model
have been estimated using the method of maximum likelihood. Estimated
parameters and asymptotic t-ratios are shown below in Table B-l. As the
results reported in Table B-l show, the fraction of the total subscriber
population choosing any particular service declines with the cost of the
service, the number of cans per collection the service provides, and the
distance from the curb at which collection is offered. Taken at face
value, these results suggest that households prefer lower levels of service
(e.g., fewer cans relatively close to curbside), other things being equal.
While this result may seem curious at first sight, many investigators have
reported that households do not like backyard service (due to noise,
spillage, unfamiliar persons in close proximity to the house) and do not
care for the space and other management problems posed by additional cans.
The negative signs of the coefficients of number of cans and distance from
the curb could thus reflect these considerations.
Estimated price elasticities of demand for each of the services
corresponding to the parameter estimates shown in Table B-l are reported
below in Table B-2. These estimates exhibit the pattern that elasticities
increase with service levels. This is generally what one would expect. At
higher levels of service, one has more alternatives to switch to in the
face of a price increase. For example, households subscribing to two can
service at a distance of between 25 and 75 feet could consider switching to
one can service at this distance, to two can service in the curbside zone,
or to one can service in the curbside zone. Households subscribing to two
can service in the curbside zone can only reduce their service demanded by
switching to one can service in the curbside zone.
Taken together, the results reported in Tables B-l and B-2 suggest
that the demand for solid waste collection service levels is sensitive to
166
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TABLE B-l. ESTIMATED PARAMETERS OF MULTINOMIAL LOGIT MODEL
Service
characteristic
Price
Number of cans
Distance from curb
Coefficient
estimate
-0.486187
-1.00216
-0.091494
Standard
error
5.394209E-03
5.685739E-03
2.394175E-04
Asymptotic
r-ratio
-90. 1312
-176.259
-382. 152
Gradient
-599.292
-227.691
-3209. 7
TABLE B-2. ESTIMATED PRICE ELASTICITIES USING MULTINOMIAL LOCIT MODEL
Service
(number of
cans, distance)
(l.D
(2.1)
(3,1)
(4,1)
(5,1)
(6.1)
(1,2)
(2,2)
(3,2)
(4,2)
(1.3)
(2,3)
(3,3)
(1.4)
(2,4)
(3,4)
Price of
service
2.45
3.60
4.85
6.00
7.15
8.30
3.90
6.55
9.20
11.85
5.30
9.35
13.40
6.65
12.10
17.55
Snare of
market
.7809
. 1639
.0328
.0069
. 0014
. 0003
.0125
.0013
.0001
.0000
.0000
.0000
.0000
.0000
.0000
.0000
Estimated
elasticity of
demand
0.2610
1.4634
2.2807
2. 8970
3.4714
4. 0341
1.8724
3.1804
4.4725
5. 7613
2.5768
4.5458
6.5149
3.2331
5.8829
8.5326
Distance key:
1 = 0-25 feet
2 = 25-75 feet
3 = 75-200 feet
4 = > 200 feet
167
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service price, and that pricing policy can have a very substantial effect
on the number of households subscribing to higher levels of service.
Waste Generation
The results reported in Tables B-l and B-2 above pertain to choices
among the alternative collection services offered by the City of Tacoma.
The other equations in my model of the demand for collection services in
Tacoma are equations explaining the total quantity of waste generated by
households in Tacoma. For these relationships, I have adopted regression
equations relating estimated waste generated in Tacoma by households to the
price of collection services, income (retail sales is used as a proxy for
income), number of subscribers, and season. Three different equations have
been estimated, one for each of the components of total waste generation.
These three components are total residential collection, litter (which is
assumed to be generated by households), and the estimated quantity of waste
self-hauled by households for disposal at the city landfill. The estimated
equations for each of these components of household solid waste are
summarized below in Table B-3.
TABLE B-3. REGRESSION MODEL RESULTS (t-STATISTICS SHOWN IN PARENTHESES)
log
(tons of solid
•waste collected)
log (tons)
of solid waste
self-hauled)
log (tons of
litter)
Constant
2.2861
20.4863
-6.4047
log(deflated average service price)
log(deflated retail sales)
log(number of subscribers)
Winter months dummy variable
Extra bag service dummy variable
R
-0. 1516
(-1.3626)*
0.0615
(0.3707)
0.5098
(1.7714)*
-0.0745
(-2.9772)*
-0.0319
(-1.4324)*
0.1576
0.5350
(1.2344)
-1.6283
(-2.5182)*
0.5658
(0.5046)
-0. 7935
(-8.1399)*
0.1690
(1.9458)*
0.6224
-0.7120
(-0.6414)
-2.2052
(-1. 3314)*
3.4908
(1.2154)
-0.0114
(-0.0456)
-1.3446
(-6.0437)*
0. 5402
F(5,41)
D. W.
2.721
2.06
16.167
1.70
11.808
1.87
* Significant at the 0. 10 level.
168
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In broadest terms, the results shown in Table B-3 are consistent
with what one would expect. Increases in the price of collection services
tend to reduce the quantity of household waste presented for collection and
to increase the quantity self-hauled. Collection service price seems to
have a small and statistically insignificant negative effect on litter.
Note, however, that only in the equation for collection is the price term
significant at the 10 percent level.
Because of the fact that the regression equations reported in Table
B-3 were estimated in the natural logarithms of the variables shown, each
coefficient may be interpreted as an elasticity. Interpreted in this
fashion, the regression results imply a relatively low price elasticity of
demand for quantity collected with respect to average collection charge of
approximately 0.15. If collection charges were increased across the board
by 10 percent, approximately a 1.5 percent reduction in quantity of waste
presented for collection could be expected, and quantity self-hauled could
be expected to increase by about 5.4 percent. Since self-hauling accounts
for about 20 percent of household solid wastes and municipal collection
accounts for about 80 percent (litter is a negligible fraction), the
estimated percentage change in quantity generated associated with a 10
percent increase in price would be approximately zero (i.e., 0.2 x 5.4 -
0.8 x 1.5). This implies that the main effect of increases in the price of
solid waste collection services is to increase self-hauling. There is no
reduction in quantity of waste generated according to these results.
The coefficients of the proxy used to represent income — retail
sales — indicate that self-hauling and littering are negatively associated
with income levels, and that quantity presented for collection is
associated positively (but weakly) with income. The retail sales variable
is statistically significant, however, only for self-hauling and littering,
with self-hauling and littering decreasing with retail sales.
Increases in the number of subscribers (which is equal to the
number of households in Tacoma) results in increases in all three
components of the household waste stream. Our estimates for collection and
self-hauling (only the coefficient in the collection equation is
significant at the 10 percent level) imply that waste has increased less
than in proportion to the number of subscribers. Our estimate for
littering suggests that this component has increased at over three times
the rate of increase of households, other things being equal. As has been
suggested above, however, littering is an insignificant portion of the
total, and may not have been household litter. In addition, the
coefficient of number of households is not statistically significant.
The coefficient of the dummy variable for the winter months
indicates that less waste is presented for collection and less is
self-hauled during the winter months than during other months of the year.
This probably reflects seasonal patterns in household waste generation,
particularly with respect to yard wastes.
169
-------
The coefficient of the dummy variable representing the formal
introduction of extra bag service and associated increase in the price of
this service in 1976 suggests that the price increase reduced household
collection and increased self-hauling, and reduced littering (perhaps by
providing a ready means to dispose of temporary excess wastes).
To sum up, the statistical results presented in this section show
that the choice of service level is sensitive to price, and that choices
are somewhat more responsive to price changes at relatively high service
levels. This is, I hypothesize, because there are relatively more
substitution alternatives available to households at high levels of service
(i.e., they either can reduce the number of cans per collection, or the
distance from the curbside at which cans are presented for collection, or
they can self-haul) . My results also suggest that the quantity of waste
generated seems to be relatively insensitive to price. However, the choice
between collection and self-hauling does seem to be sensitive to price.
Let me hasten to caution that the results presented are far from
conclusive. The data available are relatively rough, and the statistical
procedures and assumptions I have made in modeling them by no means are the
only ones that could be adopted. If these assumptions or modeling methods
were changed, it is quite possible that the results would change also.
CONCLUDING REMARKS
Keeping firmly in mind that all of the statistical results reported
in this paper are tentative, it is still useful to discuss the implications
for solid waste management which follow from my results. Certainly the
strongest implication is that user charges can be used to affect the demand
for levels of service. Increasing prices on high service levels seem to
result in a reduction in the number of households choosing high service
levels. To the extent that provision of high service levels is uneconomic,
such a charging policy can increase the economic efficiency of solid waste
management.
Some idea of the overall difference in demand for services under a
graduated user charges policy, like that employed in Tacoma, and under a
flat rate policy distance be gotten by comparing predicted service demands
under these two charge systems. This is done in Figure B-l below, where
predicted percentages of subscribers are shown on the vertical axis, and
the various can/distance combinations that constitute the available
services in Tacoma are shown on the horizontal axis. As can be seen in
Figure B-l, the percentage of households subscribing to basic service is
estimated to be about 17 percentage points higher under Tacoma's current
graduated charge policy (see the diagonally shaded area in Figure B-l).
These 17 percentage points reflect a predicted shift away from higher
levels of service. The predicted net percentage shifts from each of the
higher levels of service are shown by the stippled areas in Figure B-l.
These calculations suggest that the aggregate effect of pricing
policy on service level choice may be quite large. If the costs of
170
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Per
80 —
70 —
60 —
50 —
40 —
30 —
20 —
10 —
o —
rent
iH
^^,
(i,M
Amount by which percentage ol
Y/y/ftA under present rates exceeds pe
flat rate
Amount by which percentage of
(•$'£:"£] under flat rate exceeds percenl
current rate
n
JZHj-^n p^SL,^.^
(2, 1) (3, H (4. 1)'(5. 1)'<6, n'fl.Z)'(Z,Z)' (3,2)' H.Z)1 (1, 1)'(Z, 3)'(3, 3)'(1.4)' (Z,4)'(3, 4)'
(Cans, Distance)
Figure B-l. Percentage of households at each service level under present
charge rates and under a flat rate system.
-------
providing higher service levels are large, this suggests that the
efficiency gains from adoption of a cost-based pricing policy could be
substantial.
A second implication of my results is that service prices have
little effect on the quantity of waste generated by households. While my
results do show some tendency to reduce tonnage placed for collection, they
also show an approximately equal increase in tonnage self-hauled to the
landfill.
Overall, these results support the proposition that households'
demands for solid waste collection and disposal service are sensitive to
price. In addition, these results suggest that the degree of sensitivity
may be quite high, and therefore that the solid waste management efficiency
gains typically ascribed to a user charge policy that sets charges equal to
service costs may be correspondingly great.
172
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TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
1. REPORT NO.
EPA-600/5-79-008
2.
3. RECIPIENT'S ACCESSI Of* NO.
4. TITLE AND SUBTITLE
Impact of User Charges on Management of Household
Solid Waste
5. REPORT DATE
August 1979 (Issuing Date)
6. PERFORMING ORGANIZATION CODE
7. AUTHORIS)
Fritz Ef
aw and William N. Lanen
8. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Mathtech, Inc.
Box 2392
Princeton, New Jersey 08540
10. PROGRAM ELEMENT NO.
1DC818 - SOS #5. Task 06
11. CONTRACT/GRANT NO.
Contract No. 68-03-2634
12. SPONSORING AGENCY NAME AND ADDRESS
13. TYPE OF REPORT AND PERIOD COVERED
Municipal Environmental Research Laboratory
Office of Research and Development
U.S. Environmental Protection Agency
Cincinnati, Ohio 45268
Fina'
14. SPONSORING AGENCY CODE
EPA/600/14
15. SUPPLEMENTARY NOTES
Project Officer - Oscar W. Albrecht 513/684-7881
16. ABSTRACT
A basic proposition of economic theory is that when the price of a good in-
creases, other things being equal, the quantity of that good purchased will decrease.
For some time now, economists have suggested that this relationship between price and
quantity should be considered by policymakers and solid waste managers for efficient
management of solid waste. The effects of pricing (e.g. changes in quantity, compo-
sition of the waste collected, impacts on resource recovery, litter, and economic
efficiency) have never been fully investigated.
Empirical results from five (5) communities having several forms of user
charges are presented. The results suggest that household demand for various levels
of collection services are sensitive to price but that the quantity of waste that
households set out for collection and disposal may not be sensitive to price. These
results must be considered tentative because of data problems and critical assumptions
that had to be made. Additional studies are needed before firm conclusions can be
made about user charges.
17.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.IDENTIFIERS/OPEN ENDED TERMS
COSATI Field/Group
Demand (economics)
Mathematical models
Econometrics
User charges
User fees
Solid waste
5C
68C
91A
18. DISTRIBUTION STATEMENT
Unlimited
19. SECURITY CLASS (This Report)
unclassified
21. NO. OF PAGES
185
20. SECURITY CLASS (This page)
unclassified
22. PRICE
EPA Form 2220-1 (9-73)
173
r.: PRlHTiNc. OFFICE li;s -657-ObO/54-10
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