AN ABSTRACT OF THE THESIS OF
Seonghoon Hong
for the degree of
Doctor of Philosophy
in
Agricultural and Resource Economics presented on November 21,
1991.
Title: An Economic Analysis of Household Recycling of Solid
Wastes: The Case of Portland, Oregon
Abstract approved:
Redacted for Privacy
Richard M. Adams
Household recycling, as a means of reducing the amount
of solid wastes disposed and conserving valuable resources,
is an important component of solid waste management.
dissertation,
In this
an economic model of household solid waste
generation is constructed, based on rate incentive schemes
and time allocation within the household.
Demand functions
for solid waste management services are derived from the
model.
Comparative static results from changes
of the
marginal price are also provided.
Under a block payment system, payment differences (gaps)
between blocks are used as a proxy or measure of marginal
price elicited under a true marginal pricing system.
In an
empirical analysis of the Portland, Oregon metropolitan area,
the gap price variable is used to test the hypothesis that
such a price incentive improves recycling efforts and reduces
the total amount of solid waste.
Other socio-economic
factors affecting recycling efforts and the demand for
garbage collection services are evaluated also.
(
Using both the theoretical and empirical analysis,
policy implications for residential solid waste management
Specifically, the effectiveness of the block
are provided.
payment system is evaluated in terms of increasing the total
amount of recyclables and reducing total amount of nonAlternative pricing options on residential
recyclables.
solid waste management are compared.
The results of empirical analysis show that an increase
in the gap increases the frequency of household participation
in curbside recycling, but such a rate structure does not
reduce
significantly
demand
the
for
garbage
collection
Education level and value of time are significant
services.
factors in explaining household's participation decision on
curbside
Although
recycling.
significant
factor
in
generation,
demand
for
household
total
determining
garbage
income
solid
collection
is
a
waste
services
is
inelastic with respect to income.
Under the current pricing structures, the only possible
means of reducing the amout of non-recyclables is to boost
household recycling, since total expenditures for garbage
collection services are a very small proportion of
Household recycling will be
typical household's income.
encouraged
schedules
services.
by
and
creating more
providing
the
blocks
quality
(gaps)
recyclable
in
payment
collection
C
Copyright by Seonghoon Hong
November 21, 1991
All rights Reserved
An Economic Analysis of Household Recycling
of Solid Wastes: The Case of Portland, Oregon
by
Seonghoon Hong
A THESIS
submitted to
Oregon State University
in partial fulfillment of
the requirements for the
degree of
Doctor of Philosophy
Completed November 21, 1991
Commencement June 1992
APPROVED:
Redacted for Privacy
Professor of Agricultural and Resource Economics
in charge of major
Redacted for Privacy
Head of department of Agricultural and Resource Economics
Redacted for Privacy
Dean of Gradue School
Date thesis is presented
Typed by researcher for
November 21. 1991
Seonghoon Hong
ACKNOWLEDGEMENTS
I would like to express my sincere gratitude to my major
professor,
Dr.
Richard
M.
Adams,
for
his
guidance,
encouragement, and unending support throughout the course of
my graduate study and preparation of this dissertation.
I also wish to thank to my advisory committee, Drs. H.
Alan Love, R. Bruce Rettig and Gregory M. Perry for their
guidance and helpful comments.
Special thanks are extended Co Terry Peterson, Douglas
Anderson and Steven Kraten of Metro, Oregon, for their useful
comments and support in collecting and analyzing data used in
this dissertation.
Finally, I would like to thank to my parents, my wife,
Taehi and my son,
absolute support.
Donghwa for their love,
endurance and
I dedicate this work to them.
TABLE OF CONTENTS
1
INTRODUCTION
1
2
REVIEW OF LITERATURE
5
2.1
7
2.2
2.3
3
A THEORY OF RESIDENTIAL SOLID WASTE GENERATION
3.1
3.2
4
4.3
4.4
6
Time Allocation Models
3.1.1
Household Production Approach
3.1.2
An Alternative to the Household
Production Approach
A Model of Household Solid Waste Generation
AN EMPIRICAL ANALYSIS: THE PORTLAND, OREGON
METROPOLITAN AREA
4.1
4.2
5
Models of Residential Solid Waste Generation
2.1.1
Application of Traditional
Consumer Theory
2.1.2
Application of Household
Production Theory
Residential Solid Waste Policy Options
The Need for a New Theoretical Model
Description of Study Area
Data
4.2.1
Amount of Household Solid Waste
4.2.2
Payment Difference Between Blocks
4.2.3
The Value of Time
4.2.4
Subsample Used in Estimation
Estimation Procedures
4.3.1
Estimation of Choice Models for
Recycling Participation
4.3.1.1
The Binary Choice Case
4.3.1.2
The Multiple Choice Case
4.3.2
Estimation of the Demand for Solid
Waste Collection Services
Results and Evaluation
8
10
12
15
17
17
17
20
21
31
31
34
35
36
39
44
46
46
47
49
51
52
POLICY IMPLICATIONS FOR RESIDENTIAL SOLID WASTE
MANAGEMENT
61
5.1
5.2
61
67
Efficiency of a Block Payment system
Comparison of Alternative Pricing Options
SUMMARY, CONCLUSIONS, AND SUGGESTIONS FOR
FUTURE RESEARCH
71
TABLE OF CONTENTS (continued)
BIBLIOGRAPHY
APPENDIX A
APPENDIX B
75
ESTIMATION OF THE TOTAL SOLID WASTE
GENERATION FUNCTION
80
DATA
82.
LIST OF TABLES
Table
G.
Page
Results of Probit Estimation for Labor- force
Participation by Females
42
Results of the Estimation of the Wage Equation
for Females with Jobs
42
Results of Probit Estimation for Labor- force
Participation by Males
43
Results of the Estimation of the Wage Equation
for Males with Jobs
43
Means of Exogenous Variables
45
Results of the Probit Choice Model of Curbside
Recycling
54
Marginal Effects of the Continuous Variables
from the Probit Model
54
Results of the Ordered Probit Choice Model of
Curbside Recycling
55
Probabilities for Each Frequency Category
56
Marginal Effects of a Change in GAP
56
Estimation of the Demand for Garbage Collection
Services
57
AN ECONOMIC ANALYSIS OF HOUSEHOLD RECYCLING
OF SOLID WASTES: THE CASE OF PORTLAND, OREGON
CHAPTER 1
INTRODUCTI ON
Solid waste is one of the residuals generated by the
economic activities of production and consumption.
Since
such activities merely transform materials without altering
their amount, increasing production and consumption implies
that more valuable resources are transformed to solid wastes
(Tolley
et
al.,
1985).
Through resource recovery and
recycling efforts, materials that would be disposed as solid
wastes can be recovered and thus used for future production.
Materials recovery and recycling activities, however, are not
free; such materials must be separated and processed.
Large scale materials recovery and recycling efforts
have been limited because of high costs.
However, [interest
in materials recovery and recycling has increased sharply in
the United States due to the rising real costs of solid waste
disposal.) As society becomes more concerned about external
damages (health and environmental risks)
from solid waste
disposal facilities, regulatory standards for such facilities
have increased, resulting in the closure of landfills and
incinerators in many urban areas.
Disposal costs at existing
landfills have increased sharply (Darcey, 1989).
The lack of
new sites near metropolitan areas resulted in transfers of
solid wastes from urban areas to more distant disposal sites.
2
This further increases the costs of disposal.
As
a
result
these rising costs
of
of
solid waste
disposal, materials recovery and recycling activities have
become more attractive.
In addition to conserving resources
for future production, such activities reduce the amount of
solid wastes disposed at landfills or incinerators.
encourage
such
activities,
schemes
incentive
to
To
boost
secondary materials markets (recovered or recycled materials)
and to foster source-separation of solid wastes have been
initiated in many communities.
Source-separation at the household level is generally
viewed as convenient and efficient method of waste reduction
(Andrews Jr., 1989).
This has led to creation of household
recycling programs in many communities.
Although mandatory
or voluntary source-separation programs are the main method
of recycling in most areas,
some provide rate incentive
schemes such as deposits on beverage containers and marginal
(or
block)
services.
pricing
systems
for
solid waste
collection
With the exception of mandatory deposits on
beverage containers, there is little economic analysis of
household recycling in the literature on residential solid
waste (Smith (1972), and Schellberg and Atri (1991))
This thesis is among the first research to incorporate
household recycling in a model of residential solid waste
generation.
The
overall
objective
theoretically and empirically
the
is
to analyze both
relationships between
3
household
recycling
and
effort
socio-economic
Specific objectives of this research are to
(1)
factors.
develop a
theoretical model combining household recycling and solid
waste generation;
(2) to estimate choice models of household
recycling and a demand function for garbage collection
services; and (3) to assess the implications of the empirical
results for recycling programs in urban areas.
The remainder of this thesis is divided in 5 chapters.
Chapter 2 reviews the economic literature on residential
solid waste and explores the need for a theoretical model of
household recycling.
Specifically, models of residential
solid waste that do not consider household recycling are
analyzed.
Next, policy options discussed in the economic
The necessity of a new theoretical
literature are reviewed.
model that incorporates household recycling is explained.
Finally, problems in constructing a comprehensive model of
household solid wastes are explored.
Chapter
3
presents
a
time
residential solid waste generation.
allocation
model
of
Factors determining
household recycling and demands for solid waste collection
services are derived.
Comparative static results from a
change in the marginal price of solid wastes are provided.
Under a block payment system, the price variable is redefined
by the payment difference or gap between blocks.
In Chapter
4, the theoretical model developed in chapter 3 is applied to
a case study of the Portland, Oregon metropolitan area (the
4
"Metro" area).
used
data
Discrete
in
The chapter describes the study area and the
estimating
choice
models
the
are
household's recycling efforts.
choice
applied
of
to
demand models.
estimate
the
The demand function for
garbage (non-recyclables) collection services is estimated.
Finally, the estimation results are presented and evaluated.
Chapter 5
study.
presents policy implications derived from the
A block payment system used in the empirical study is
further evaluated.
Other pricing options are discussed.
Summary, conclusions and suggestions for future research
are given in Chapter 6.
5
CHAPTER 2
REVIEW OF LITERATURE
Resource
and
environmental
economists
have
spent
considerable effort evaluating the more common externalities,
such as air and water pollution.
However, research on solid
wastes has been limited in the economic literature.
One
reason for lack of attention is that society identifies air
and
water
pollution
as
serious
more
a
fundamental
reason
lies
applicability
the
in
and
However, perhaps a
environmental problem than solid wastes.
more
health
of
mainstream economic theory.
For most versions of air and water pollution problems,
comprehensive economic analysis
(i.e,
applying mainstream
economic theory to perform benefit-cost analysis) is possible
since relatively small numbers of sources are involved and
the sources are mostly firms. Neoclassical marginal analysis
and
externality
determine
optimal
theory
levels
provide
of
a
useful
emissions
and
framework
to
to
evaluate
voluntary exchange schemes and other corrective mechanisms.
Conventional economic wisdom equates the marginal benefit
from abatement with marginal damage
from the
level
of
pollution to determine the optimal level of emissions. lie
transaction costs are negligible, bargaining among polluters
and victims will lead to a Pareto optimal level of emissions
(Coase)However, where transactions costs are important and
many participants are involved, economists often recommend
6
public intervention.
Specifically, using the "polluter pays
principle", they recommend Pigouvian taxes as the primary
incentive scheme to attain optimal levels
Recently,
marketable emission permits
are
of emissions.
suggested
as
alternatives to Pigouvian taxes or as more flexible and
efficient schemes than Pigouvian taxes (see Baumol and Oates
(1988), Tietenberg (1984), and Hartwick and Olewiler (1986))
While the above concepts have been useful in addressing
many externalities, applicability of economic theory to solid
waste is limited because of the large number of people
involved in the waste stream and the public provision of
waste disposal services.
According to Starrett
(1988),
markets for waste disposal emerge naturally in the absence of
institutions
for
handling
solid wastes.
When
garbage
collection services are not provided by public institutions,
the primary action of individual households will be to dump
their wastes
external
to
their property.
While
every
household may end with equal amounts of waste, individual
households will have different tolerances for garbage.
Since
households with higher tolerance for wastes have incentives
to offer collection services, waste collection is effectively
incorporated into the market system if there is sufficient
competition to generate a single exchange rate or price
(Starrett,
p
41).
In reality,
public institutions are
involved in providing and regulating solid waste collection.
Such public involvement is warranted by health concerns and
q
7
the presence of declining costs in garbage collection that
create conditions for monopolistic behavior.
Solid wastes are generated through both production and
by-products
from production and
garbage and trash from consumption.
Both production and
consumption activities:
packaging technologies and consumption habits affect the
amount of solid wastes generated.
the amount of wastes disposed.
recycled,
less
expensive
Recycling efforts reduce
By creating a supply of
raw materials,
reduces the use of virgin materials.
recycling also
Household sink disposal
and incineration transform solid wastes to liquid and gaseous
wastes.
Landfilling
of
wastes may
create
externality such as groundwater contamination,
odors and increased traffic.
a
negative
offensive
Since so many households are
involved in the waste stream, monitoring of waste generation
and disposal
is very difficult for solid wastes.
This
complex structure of the waste stream, the public role in
waste
disposal
and
the
difficulty
of
monitoring
waste
generation complicate the analysis of optimal levels of
wastes as well as in designing optimal regulation schemes.
The difficulty of constructing comprehensive models using the
neoclassical framework has induced economists to analyze each
sector of the waste stream separately.
2.1
Models of Residential Solid Waste Generation
In the neoclassical perspective,
consumption is the
8
final
stage
Analysis beyond
the economic process.
of
consumption is unnecessary.
However,
residential
solid
wastes are generated simultaneously with or even after
consumption.
major issue
Thus, in modeling residential solid wastes, a
is
how to incorporate the consumer's waste
disposal decision into the choice process of consumption. 1
Two approaches have been used in the economic literature
One is to
to specify models of residential solid waste.
apply traditional consumer theory.
waste
collection
services
is
Here the payment for
included
in
the
budget
constraint and a waste generation constraint is imposed in
the model.
The other is to apply household production theory
developed by Becker (1965) and Lancaster (1966).
2.1.1
Application of Traditional Consumer Theory
Wertz (1976) analyzed the effects of price of service,
income and supplier's terms of service on the generation of
solid wastes.
In his model, a variable representing on-site
accumulation of refuse multiplied by the distance to the
collection site enters the utility function along with goods.
Increases in this variable are assumed to reduce utility at
a constant rate. Bonus and Hastings's model (1985) implicitly
assumes that wastes generated result in negative utility to
the household even though the utility function is expressed
with market goods only.
The comparative static results of
Wertz's and Bonus and Hastings's models are thus similar.
9
One comparative static result is that suppliers' terms
of service (i.e. frequency of service and site of collection)
affect significantly
A
the generation of solid wastes.
reduction of frequency of service and a shift from backyard
pick-up to curbside pick-up induces a decrease in waste
generation.
This result provides a policy implication that
too frequent and perhaps too convenient services in solid
collection
waste
should
be
avoided
to
encourage
less
generation of solid wastes.
In both the Wertz and Bonus and Hastings'
models,
The
effects of change in service charges are analyzed also.
change
in
the
price
of
service
substitution and income effects.
effects are negative.
is
partitioned
into
Wertz suggests that both
However, he concludes that public role
in solid waste service, by adopting flat fee systems, has
induced households to generate larger amounts of waste.
In
the same context, Bonus and Hastings argue that a net social
loss can occur at zero or flat pricing on waste services; the
social costs of services might be higher than the value of
these services.
They contend that a marginal pricing system
could correct this.
Although incremental pricing systems would reduce solid
wastes, they are rarely used in the United States; Seattle's
pricing system is a notable exception.
The reason
can be found in Bonus and Hastings's following argument:
Administrative costs are involved in quantity pricing,
10
however. First, the quantity must be measured; and
second, billing becomes more complex. In addition, such
external costs as littering may result from quantity
pricing, and individual communities may lose tax and
revenue-sharing advantages. Thus the total net benefits
of pricing may not be positive. [Bonus and Hastings,
1985, p 41)
According to Wertz, the long run effects from changes in
packaging technologies would be complex because repackaging
redefine
might
equiproportional
the
Wertz
goods.
reductions
of
also
argues
refuse weights
that
goods
on
(through efficient packaging) do not affect significantly
waste generation since savings from the reduction of wastes
are used for more consumption.
schemes
for
more
efficient
This implies that incentive
packaging
have
not
will
significant effects on residential solid waste stream.
2.1.2
Application of Household Production Theory
In the household production approach as applied to solid
waste, the focus is on functional relationships between the
amount of solid wastes generated and socio-economic factors
of the household (Richardson and Havlicek (1974, 1978), Saleh
and Havlicek (1975)). Packaging technologies and consumption
habits of households are assumed to be constant.
technical
waste
transformation coefficients
that
Thus
relate
quantities of solid wastes with quantities of market goods
are not changed.
Suppliers' terms of such as frequency of
service and site of collection are not considered.
The price
11
of service is not considered because of flat fee schedules in
the areas studied.
Demand functions for solid waste management services are
derived by applying Lancaster's consumer theory (1966).
The
household is assumed to maximize utility, expressed as a
function of goods'
characteristics subject to the budget
Goods and their characteristics are linked
constraint.
through consumption activities.
residuals
of
activities,
consumption
are
Since solid wastes
linked
to
as
consumption
goods and solid wastes are connected through
technical waste transformation coefficients (WTC) .
In the
short run, since goods' prices and WTC are constant, socioeconomic factors influencing household tastes and preferences
are the exogenous variables of the waste generation function.
Richardson and Havlicek
(1974,
1978)
and Saleh and
Havlicek (1975) applied this approach to examine empirically
the relationship between the amount of household solid wastes
and selected socio-economic variables.
Except for Saleh and
Havilcek, they used aggregated data based on census tract or
blocks.
Although aggregation bias is a frequent problem in
traditional consumption theory, the treatment of aggregation
is not discussed in Richardson and Havlicek's models.
In Richardson and Havlicek's study, exogenous variables
include household income, household size, percent of adults
(18 to 61 years of age) and percent of black people.
The
seasonal variation in household solid waste generation is
12
included in the 1974 study and the composition of waste is
contained in the 1978 study.
that
One finding of their studies is
and household size
income
appear
to be
the most
significant factors affecting the quantity of solid wastes.
Another is that seasonal variation was larger in higher
income areas.
Third,
differently to income;
the composition of waste responds
newsprInt,
grass and yard wastes
generation response positively and significantly to household
income,
but
textiles
and
plastics
are
negatively
and
inelastically related with income.
Solid waste associated with food consumption activities
was
analyzed
by
Saleh and Havlicek.
Household
size,
housewife education level and availability of a sink disposal
affected significantly the generation of total food solid
wastes.
2.2
Residential Solid Waste Policy Options
There are two general options for residential solid
waste management: reduction of total solid waste generation
and recycling (or recovery of materials). [Waste generation
can be reduced through changing technology (such as less
packaging of goods and increasing durability of products) and
switching consumption patterns to less packaged goods.) The
options for recycling include source-separation, mechanical
separation, composting of organic wastes, direct recovery of
energy from household refuse and the production of refuse-
13
derived fuel (Larkin, 1986, P 55).
Among options to boost household recycling
(source-
separation), mandatory deposits on beverage containers are
one of the most common policy options adopted in the United
States. According to Naughton, Sebold and Mayer (1990), nine
states passed mandatory beverage deposit legislation by the
end of
1983 and numerous other states were considering
similar legislation.
A number of studies have evaluated the economic and
environmental
impacts of mandatory deposits on beverage
containers (Porter (1978, 1983), Moore and Scott (1983), Lee
(1990)).
hey provide
empirical evidence that mandatory deposits
have reduced
et al.
(1988), and Naughton et al.
litter by
75-85
percent
in
the
states
of
bottle bill
legislation.)
In a welfare context, benefits from mandatory deposits
include direct cost savings from the reduction of litter and
solid wastes,
and amenity benefits of litter reduction.
Costs include consumers'
bottled
products,
losses from price increases for
resource
cost
increase
of
beverage
delivery, and consumer inconvenience cost incurred returning
containers.
However, the welfare assessment in these studies
is not complete because quantifying non-market benefits and
costs (such as amenity benefits and inconvenience costs) is
difficult.
Since
increasing demand for secondary materials
is
14
essential to boost recycling, a number of empirical studies
have focused on this subject.
Some studies analyze secondary
material markets (Edwards, Deadman and Turner, Turner et. al.
Price elasticities
(1977), Fink (1977), and Lichtwer (1977))
of demand and supply for various secondary materials are
provided in these studies.
(1990)
Curlee (1986, 1989) and Considine
investigate the feasibility of
recycling plastic
wastes. According to these studies, relatively small amounts
of plastic wastes are being recycled because of a limited
market
secondary
for
difficulties
wastes.
in
separating
onsidine
products
plastic
from other
plastics
contends
that
technical
and
identifying
similar
plastic
products which can be recycled at a reasonable cost and
providing tax incentives for secondary material processors to
decrease long-run marginal costs of recycling are important
to encourage plastic recyclingj
A marginal pricing system for solid waste collection
services (analyzed in Wertz and Bonus and Hastings) is one
policy option to reduce total solid wastes.]
However, as
mentioned in section 2.1, empirical analyses of a marginal
pricing system are not provided in their studies.
Another policy option to reduce total
solid waste
generation and promote efficient recycling involves tax
refunds; Schellberg and Atri (1991) examine
goods
to
encourage
reflect
their disposal
recycling.?
Such
a
and refunds
costs
scheme
xcise taxes on
is
to
considered an
15
extension of mandatory deposits on beverage containers. They
argue that recycling is efficient if the marginal costs of
recycling is less than their marginal benefits. The marginal
costs of recycling include the value of household's sorting
time
and
the
difference between
the marginal
cost
of
reprocessing secondary materials and the marginal cost of
using virgin materials.
The authors contend that a properly
designed tax-refund scheme would promote efficient recycling
and replace flat fee systems of solid wastes.
If administrative costs and consumer welfare losses from
price increases of taxed goods are considered, the tax-refund
system might not be efficient; a benefit-cost analysis is
required to evaluate this system.
Since alternative pricing
systems (such as a marginal pricing system, including a block
payment variant) provide incentives to produce less waste and
to recycle, the cost-effectiveness of these systems needs to
be compared.
2.3
The Need for a New Theoretical Model
Although flat fee systems for waste disposal service are
the
dominant
fee
structure
in
the
Unite
States,
some
communities provide incentives for households to generate
less
solid wastes and to
improve recycling efforts by
restructuring their fee schedule.
For example,
Seattle,
Washington has established a marginal pricing system based on
weight.
Portland,
Oregon has
adopted a block payment
16
schedule using a 32 gallon can as the standard unit.
Source-
separated recyclable materials at curbside are collected at
no charge to the household in these pricing schemes.
Under the marginal pricing system or the block payment
system, the value of time is expected to have a critical role
in the household's behavior regarding recycling efforts.
Specifically,
individual
'households
must
decide
simultaneously how much total solid waste to generate and to
recycle.
Therefore, time allocation and the participation
decision regarding curbside recycling should be incorporated
in the model of residential solid wastes.
17
CHAPTER 3
A THEORY OF RESIDENTIAL SOLID WASTE GENERATION
3.1
Time Allocation Models
While the household's participation decision between
working and not working has been incorporated in traditional
consumer theory, the allocation of time in non-working uses
has been analyzed in the framework of household production
The household production approach has provided new
theory.
insights on household behavior1 by separating the effects of
tastes
and
technology
(which
traditional consumer theory).
are
not
disentangled
in
By focusing on commodities
produced at the household, the cost of time and both quality
and quantity of commodities are incorporated in the analysis.
However, the application of the household production approach
requires the assumptions of constant returns to scale and
non-jointness in production (Pollak and Wachter, 1975).
An
alternative to the household production approach is provided
by Pollak and Wachter.
3.1..1
Household Production Approach
According
to
Becker's
household production
theory
(1965), the household is assumed to combine time and market
goods to produce commodities that directly enter its utility
function.
Let Z =
(Z1,
Zm)' be a vector of commodities
18
produced in the household, and let X =
vector of market goods.
(x1,
x)' be a
Then, the production function for
the ith commodity is denoted as
Z1 = f1(X,
(1)
T1)
where T1 is a time used in producing the ith commodity.
The
utility function is denoted as
U = U(Z1 .....Zm)
The
household
U(x1 .....x;
èhooses
its
(2)
T1 .....Tm)
best
combination
of
commodities by maximizing the utility function subject)to the
income and time constraints, and the production functions in
equation (1)
.
The income constraint can be written as
k-i
A + wT
(3)
where Pk is the price of the kth good, A the nonlabor income,
w the fixed wage rate and T
working time.
The time
constraint can be written as
(4)
where T is total time available.
constraints (3) and (4) yields
The combination of the two
19
E PXk
+ w
C
T1) - A + wT - M
(5)
where M is "full income".
Pollak and Wachter show that commodity prices are not
a
useful
tool
in commodity demand analysis unless
the
household's production technology exhibits constant returns
to scale and non-jointness.
They define the cost function
as
C(P, w, Z) = {min
LPkXk
+
wT
s.t.
Z e F(X, T)}
where P is a vector of goods prices, and T =
spent at household production.
(6)
the time
And the shadow price of each
commodity is defined as
it(P,w,Z)
3C(P, w, Z)
az
(7)
Since the shadow prices of commodities depend on household
tastes, they cannot be used as explanatory variables in the
commodity demand functions.
When the household technology
exhibits constant returns to scale and no joint production,
the price of each commodity is equal to its unit COSt
function and these commodity prices are independent of the
commodity bundle consumed (Pollak and Wachter, 1975, p 268).
Some applied categories of the household production
20
approach have assumed constant returns to scale and the
absence
of
production
joint
in
production
household
technology (Biddle and Harnermesh (1990), and Kesenne (1983)).
However, the household's technology can be estimated even
with joint production or non-constant returns to scale (see
The
Barnett (1977), and Pollak and Wachter (1975, 1977)) .
household production function with jointness is estimated by
Graham and Green (1984).
3.1.2
An Alternative to the Household Production Approach
The
use
of
the
household
production
approach
in
empirical studies is limited because of the absence of data
on commodities produced.
That is, the distinction between
tastes and technology which is critical in the household
production approach is not possible without direct measures
of
the
commodities.
Pollak
and
Wachter
suggest
an
alternative model of the allocation of goods and time within
the household.
If the use of goods and time in one household
activity can be distinguished from their use
in
other
household activities, the allocation of time and goods among
activities can be analyzed without the need of the household
production framework (Pollak and Wachter, 1975).
Since each
activity produces a household commodity, the utility function
defined over commodities allows the researcher to specify it
as a function of a activity-input vector, i.e. market goods
and time.
By maximizing the utility function defined over a
21
vector of activity-inputs subject to constraint (5), demand
functions for activity inputs are derived as functions of
goods
prices,
income).
wage
rate and non-labor
income
(or
full
Thus, without the household production apparatus,
this approach analyzes the allocation of goods and time
within the framework of neoclassical consumer theory (see
Kooreman and Kapteyn (1987), and Pollak and Wachter (1975)).
In the
following,
Pollak and Wachter's approach is
applied in modeling household solid wastes.
3.2
In
A Model of Household Solid Waste Generation
this
analysis,
we
assume
activities in the household; one is
that
there
are
two
the production of a
composite commodity and the other is the collection of solid
wastes into garbage cans and recycling boxes.
The production
of a composite commodity requires a market good (composite
good) and time as inputs.
Solid wastes are generated in the
process of the production of the household commodity.
We
assume that the collection of solid wastes at the household
level requires only one input, time.
the
collection of
solid wastes
Although time spent at
includes
time used for
separating recyclable items and delivering both recyclables
and non-recyclables to the hauler pick-up site, we ignore
delivery time for solid waste.
We define "time spent at
source-separation" as the time used for separating recyclable
items in the household and thus is equal to time spent at the
22
Recyclables are defined as
collection of solid wastes.
source-separated recyclable items. Non-recyclables are total
solid wastes except for recyclables.
A utility function is defined over a vector of inputs:
U
=
U(X, T, T)
U,>0,
,
where X is a market good,
T
U1cz0,
UTC>O,
(8)
the time spent at
source-
separation, and T the time spent at production of a
household commodity.
The budget constraint is written as
X+K=wTA
where K is
the total payment of
(9)
solid waste collection
services and other terms are the same as defined at equation
(3).
We assume that the total payment for solid waste
collection services depends only on the amount
Source-separated
recyclables.
nothing.
numeraire.
The price
of
recyclables
a market good
is
are
of
non-
charged
used as
the
The time constraint is written as
T
+ Tç + T
where T is total time.
=
T
(10)
Following Becker, we assume that
"time spent at household production " equals "time spent at
consumption".
Thus, non-work time consists of time spent at
source-separation and time spent at household production.
The technological relationship between total solid wastes
generated and a market good is written as
23
S = OX,
0<0<1
(11)
where S is the amount of total solid wastes and 0 is a "waste
transformation coefficient".
We assume that. the technology
of household recycling is fixed-proportion:
Sr = bT
(12)
where S is the amount of source-separated recyclables and b
the coefficient.
recyclables.
Thus,
S,
=
Ox
bT5,
-
where
S,
is non-
Similar to equation (5), the combination of all
constraints yield:
M
= wT + A = X + K + wT + wT
(13)
where K is a function of Sn.
The Lagrangian expression to maximize the utility
function (8) subject to a constraint (13) is:
= U(X, T, T0)
+ X(M - x -
K - wT, - wTe)
(14)
The first order conditions (FOCs) for utility maximization
are:
ace
-
ic
3x
au
81's
aT5
-
+ --.0) - 0
dS
A(w-
asn
-0
(15)
(16)
24
aT
-
If
au
3T
X(w) -0
M - K - X - wT -
-0
the payment schedule for solid waste collection
services is flat (i.e. if additional amounts are collected at
no added charge), dK/dS will be zero.
There is no incentive
for the household to participate in recycling programs.
If the charge rate is based on volume or on weight (i.e.
K = t.S where t = marginal price), dK/dS will be equal to t.
The last equation of the FOCs can be written as
M -
(1 + t.0)X -
Then (1 + t.0),
(w - t.b)T, - wT = 0
(19)
(w - t.b) and w become the implicit prices of
X, T, and T0, respectively.
The household will participate in
source-separation when the marginal benefits from recycling
efforts (tb) are greater than the opportunity costs of time
(w).
In order to derive comparative static results from a
change in the charge rate t,
substitute dK/dS
by t,
and
differentiate the first-order conditions with respect to t.
By arranging in matrix form, we have:
25
at
Uxx
UXT$
UT$x
UT$Ts
UTCTS
Ujc; -(l+tO)
Xe
-(w-cb)
at
-)Lb
UTCTC
-w
aTc
0
-w
0
at
Ox-b;
-(i+tO) -(w-t.b)
at
Solving via Cramer's rule:
U
-
=
U.5
0
-(l+tO)
- (w- tb)
UTSTS
ax
at
U
UTCTC
-W
-w
0
X-bT., -(w--tb)
H
at
(AO)H1
(-Xb)H21
(ex-bT3)H41
H
H
H
tO)
Uxx
Xe
Ursx
-Xb
-(w-tb)
rJrcx
0
-w
- (1 + tO)
Uxrc
OX-b; -w
- (1 +
U
H
(XO)H12
(-Xb)H22
(Ox-bT)H42
H
H
H
where H is the determinant of the bordered Hessian and H11,
H12,
etc. are the cofactors of the bordered Hessian matrix.
Similarly, differentiating the first-order conditions with
26
respect to full income M and solving for aX/aM and aTs/aM
yields:
8M
3T5
aM
H42
H
Therefore, equations (20) and (21) can be written as:
ax
aT5
at
(AO)H11
(-).b)H21
ax
H
H
3M
(0)H12
(-).b)H22
H
H
S(
aTS)
By the second-order conditions for utility maximization,
the determinant of the bordered Hessian H is negative and a
border-preserving principal minor H11 is positive.
The signs
of all the other cof actors are indeterminate since they are
not border-preserving.
Thus, the first term of the right
hand side in equation (22) is negative.
The third term will
also be negative since the income effect on a composite
market good will be positive.
however, is unknown.
The sign of the second term,
Therefore, the sign of the 8X/at (thus
aS/at) is indeterminate; if the second term is positive and
its effect is strong, the overall effect on X from a change
in t. can be zero (or even positive).
This implies that the
27
household can maintain the same level of consumption by
spending more time on source-separation when the charge rate
t is increased.
The signs of the first and second terms of equation (23)
are unknown since both H21 and H22 are not border-preserving.
The sign of the third term will be positive because the
income effect on T will be negative.
Even though we expect
that the individual household will spend more time on source-
separation when t is increased (that is, when the implicit
price of T is decreased), this is not always the case.
The
household can maintain the same level of T1 by reducing the
consumption level and thus generating less solid wastes.
While the comparative static results for X and T1 (thus
for S and Sr)
from a change in the charge rate t is obscure,
will be unambiguous.
those for S
Let y' = (dx dT1 dT dX),
q' = (0 -b 0 0) and D is the matrix of the bordered Hessian.
Since 3S/at = 0(8X/8t)
-
b(8T/at) ,
from the equations (22)
and (23),
-
=
A(92H11 - ObH21
Xq'D'q -
-
ObH12 + b2H22) - S
as
The first term on the right-hand side in (24)
(24)
is negative
since q'A'q is a negative definite quadratic form: since D is
synimetric and y'Dy is negative definite by the second order
28
condition of the maximization of the Lagrangian function,
y'B'ABy
q'Aq < 0 where q = By and B is non-singular.
An
increase in income will induce increased consumption for a
household commodity.
The derived demands for a market good
and for time spent at household production will be increased.
Therefore, when income is increased,
the amount of tOtal
solid waste generated will increase while the amount of
Since the income effect on the
recyclables will decrease.
amount of non-recyclables
)
(
is positive, the second
term on the right-hand side is also negative.
Because both the first term and the second term on the right-
hand side are negative,
is negative.
For the case of a block payment schedule, the total
payment for the solid waste collection service is the same
within each block, but the payment jumps at the boundaries of
blocks.
Each
block
is
categorized with
standardized
collection carts or cans (for example, 32 gallon can); blocki
is 1 can, block2 is 2 cans and so on.
This pricing system is
quite different from the block rate schedules appearing in
water and electricity pricing; in this case, marginal prices
are the same within each block.
Under most empirical demand
studies involving block rate schedules, the average price or
the marginal price plus an additional variable called the
"billing difference" has been used
(see Nieswiadomy and
Molina (1989), Chicoine and Rarnamurthy
(1986), Foster and
Beattie (1981), and Billings and Agthe (1980)).
However,
29
under the block payment system for solid waste, we can
use
neither the average price nor marginal price for demand
analysis.
Instead,
the difference between blocks must be
used.
By solving the first order conditions in (15),
(16),
(17) and (18), the demands for X, T and T0 are derived as
functions of H-K, w, dK/dS and 0.
If a marginal pricing
system is adopted, dK/dS will be substituted by the marginal
Where
price (t) and K will be dropped in demand functions.
as the
a block payment schedule exists, we define dK/dS
payment differences between blocks:
payment),
(3
can payment
-
2
(2 can payment - 1 can
can payment),
and so on.
Households that generate total solid wastes near the boundary
of a block will be the most affected by payment differences.
Although the total payment
(K)
itself will influence the
household's optimal decision through income effects, it can
be ignored since it will be a very small portion of household
income.
In
the
short
run,
packaging
technology
and
households' consumption habits will not be changed.
the
Thus,
the waste transformation coefficient 0 is assumed constant in
the short run.
Then, from constraints (15),
(16),
(17) and
(18), the demands f or total solid waste management service,
non- recyclables collection service and recyclables collection
service are functions of H-K, w and dS/dK.
The following chapter presents an empirical application
30
of the above model of household solid waste generation.
Since the study area (Portland, Oregon) uses block payment
schemes,
the analysis will test performance of a "payment
difference" price variable as a measure of marginal price
elicited under a true marginal pricing system.
31
CHAPTER 4
AN EMPIRICAL ANALYSIS:
THE PORTLAND, OREGON METROPOLITAN AREA
4.1
The Portland,
Description of Study Area
Oregon metropolitan area consists
Clackamas, Multnomah, and Washington Counties.
of
A distinct
government called the "Metropolitan Service District (Metro)"
manages long range transportation planning, solid wastes, the
regional zoo and other regional facilities in this area.
population of
the
area was about
1.1 million in
The
1989.
According to a waste characterization study performed by
Metro (1990), generation of solid wastes was 5.2 poundS per
capita per day in 1989.
Residential solid wastes were about
31 percent of total solid wastes generated in this region.
Solid
commercial
wastes
generated
sectors
are
in
delivered
both
to
residential
landfills
and
through
transfer stations and recycling centers or directly to
landfills.
Metro regulates the flow of solid wastes to be
delivered to each facility.
For the facilities owned or
managed by Metro, disposal rates are set by Metro.
For
private facilities, disposal rates are set by the owners.
St. Johns landfill located within the Metro boundary and
which handled about 90 percent of general wastes closed in
February 1991.
with
the
As a replacement facility, Metro contracted
Gilliam
County
Sanitary
Landfill
(located
32
approximately 100 miles east of Portland) to deliver at least
90 percent of the Metro region's general wastes for 20 years.
Thus, the shortage of disposal sites encountered in other
regions
is
not
an
issue
in
region
this
at
present.
Transportation and tipping fee costs, however, will increase
with use of the more distant facility.
In order to reduce the amount of solid wastes deposited
in landfills, various incentive schemes have been initiated
in Oregon, particularly in the Metro area.
Oregon was the
first state in the United States to establish mandatory
deposits on beverage containers.
Oregon law
(Recycling
Opportunity Act, 1983) requires that recycling services be
provided for every Oregonian.
fees
in
In the Metro area, tipping
landfills have been discounted for haulers and
transfer station operators with separated recyclable wastes.
Curbside pick-up service on recyclable materials has been
provided also.
The Metro sets goals for recycling rates for the cities
within the Metro boundary; a recycling rate is the ratio
between the amount of source-separated recyclable materials
and the amount of total solid wastes.
It also provides
information about recycling markets, the composition of solid
wastes, projection of waste flow and related information to
municipalities. However, the responsibility for managing the
collection
of
municipalities.
solid
wastes
rests
with
individual
Except for the city of Portland, only one or
33
two haulers are involved in collecting solid wastes in each
At those cities, the charge rates for waste collection
city.
services are set either by city councils or by the haulers.
In the city of Portland, numerous haulers compete to provide
solid waste collection services
1990).
(112 licensed haulers in
Because of problems encountered in the current
collection system (such as service route inefficiencies and
lack of
incentives to encourage recycling),
a franchise
system for solid waste collection services has recently been
adopted in the city of Portland.
With the franchise system,
service routes will be assigned efficiently to every licensed
garbage collector.
In order to increase household recycling,
new standards and rate schedule for garbage collection will
be set by the city
(for more information on a franchise
system, see Bureau of Environmental Services, 1990)
Block payment schemes are adopted in all the cities
within the Metro boundary.
A 32 gallon can is used as a
pricing basis and the fees for additional cans are discounted
in most cities; for example, monthly payment for garbage
collection services in the city of Beaverton is $10.21 for 1
can, $19.31 for 2 cans, and $27.93 for 3 cans.
services
(60
gallon
cart
and/or
90
gallon
Where cart
cart)
are
available, the fees for those services are less than the fees
for equivalent can services.
In order to provide recycling
incentives, recyclable materials are collected at zero price.
34
4.2
Data
As discussed in section 2.1, aggregated data sets (based
on census tract) or micro data sets with a small number of
observations have been used in most empirical studies for
In such data sets, the
residential solid waste generation.
variables regarding household recycling are not available
either.
The current study utilizes a large micro data set
representing
4,306
households.
In
this data set,
key
variables needed for the evaluation of household recycling
efforts are available.
However, a full application of the
theoretical model developed in section 3.2 to the empirical
study is limited by the lack of some variables.
Since the
data on the household's allocation of time between sourceseparation and consumption is not available, our empirical
study is focused on the demand functions of solid waste
collection services.
Basic data
(except
fees
on solid waste
collection
services) used here are from the survey performed by Metro in
1990 (the "Population and Household survey").
A total of
4306 households were surveyed within the Metro boundary using
a multi-stage stratified sampling method.
In addition to
demographic data, data on garbage and recycling activities
were collected.
Data on the charge rates for solid waste
collection services were obtained through a telephone survey
of haulers and municipalities.
Some variables used to
35
estimate the model were derived from the survey data.
derivation procedures are discussed below.
The
In addition,
problems encountered in defining endogenous variables are
described below.
4.2.1
Amount of Household Solid Waste
The amount of non-recyclables for each household is
recorded on the survey and measured in terms of regular sized
garbage cans weekly.
A regular sized can is 32 gallons.
Measurement with partial cans is allowed: for example, 1.5
can of non-recyclables means 48 gallons of non-recyclables.
The amount of recyclables is not measured on the survey.
However, we can judge the individual household's recycling
efforts from other information on the questionnaire, such as
how often the household participates in curbside recycling
In the Metro survey, times of
and what items are separated.
participation in curbside recycling is categorized by the
year,
following
frequency:
quarterly,
S to 11 times a year, once a month, once every
zero,
less
other week and once a week.
than
4
times
a
Recyclable materials are
categorized into 11 items: newspapers, cardboard, magazines
and advertising circulars, tin cans, aluminum, glass, scrap
metal, motor oil, plastics, yard debris, and "other".
Since
all the items separated for curbside recycling are recorded,
we can calculate the number of recyclable items that each
household separates.
36
For households that do not participate in curbside
recycling,
the amount of non-recyclables equals the total
amount
solid waste generated.
of
For households
that
participate in curbside recycling, we cannot calculate the
amount of total solid wastes since the amount of recyclables
are unknown.
4.2.2
Payment Difference Between Blocks
Where a single hauler collects solid wastes or where the
payment schedule is set by the municipality, the same payment
However, for cities
schedule is applied to all households.
where multiple haulers are involved and payment schedules are
set by haulers, hauler's price schedules may differ.
In this
case, we assume that all the households within the city faces
the same payment schedule (a payment schedule averaged over
all haulers) since it is difficult to match the haulers to
their subscribers.
Where 90 (or 60) gallon cart service is
available, it is not known which households use cart services
and which households use equivalent can services (i.e. 3
can services) .
In this
households generating 3
(2)
case,
it
is
assumed that
(2)
the
cans of solid wastes face the
payment averaged across the 90 (60) gallon cart rate and the
3
(2) can rate.
Figure 1 shows payment schedules for garbage collection
services: one schedule is represented by a dotted line and
the other by a solid line.
Two payment differences are
37
gap2
gapi
0
.8
1
Figure 1.
1.8
2
2.8
3
Payment schedules for garbage
collection services
38
observed for each payment schedule.
In figure
1,
gapi
represents the payment difference between the one can rate
and the two can rate.
Gap2 represents the payment difference
between the two can rate and the three can rate.
To develop the variable GAP (or payment difference), the
following assumptions are adopted:
It is impossible to produce a zero amount of nonrecyclables,
Each household observes only one gap (either gap]. or
gap2) according to the amount of non-recyclables,
(C) .If a household generates total solid wastes near
a threshold level (i.e. O.8<S]. and l.8<S2), the
household will try to avoid exceeding the threshold
level even though it does not participate in recycling,
(ci). The household separates recyclable materials until it
achieves the next lower block.
That is, once the
household can save money by separating a specific amount
of recyclables, it has no incentive to increase the
level of recycling,
(e) .
Although it is difficult to calculate the payment
rates for more than 3 cans from the available data,
additional cans (for more than 2 cans) are assumed
the same charge.
Thus, households that fill
more than 3 cans fine a constant fee.
The variable GAP is defined as:
39
GAP=0,
if
0<S0.8can
= gapi, if 0.8 < S
1.8
= gap2, if 1.8 <
The Value of Time
4.2.3
Calculation of the wage rate (or the value of time) for
the household is difficult given that two or more family
members typically are employed.
If we extend our model to
all members of the household that are of work age (usually 18
years or older), the wage rates of all family members will be
included in the demand equations for S, S and S.
However,
since the wage rates for individuals in the same household is
typically correlated, the inclusion of each individual's wage
rate is both unnecessary and subject to multicollinearity
(Senauer et al., 1986, p 921).
As the value of time of the
household, we use the estimated wage rate of the female head
or spouse because the responsibility for source-separation
and solid waste collection in the household typically falls
on her.
For households without the female head or spouse,
the estimated wage rate of the male head of the household is
used.
Here the spouse is one of heads of the household.
The wage equations for female and male heads were
estimated using data from the Metro population and household
survey.
Excluding samples with missing data on endogenous
and exogenous variables, a subsample of 2,743 households was
40
used to estimate the wage equation of female head or spouse
and a subsample of 2,590 to estimate the wage equation of for
the male head.
Since wage rates were not given in the Metro
survey, we generated wage rates from the household income
First, for each household, the
given in the Metro survey.
number of people employed was calculated from a survey
question concerning the principal activity of each member of
the household.
Next, females' wage rates were assumed to be
80 percent of males' wage rates; two-earner households' wage
rates used in Kooreman and Kapteyn's empirical study (1987)
were applied here.
Full time workers were assumed to work 40
hours per week and part time workers were assumed to work 20
hours per week.
the household
Finally, by assigning relevant weights for
income,
the wage
rate
for each employed
household member was calculated.
While the value of time of female and male heads is
predicted from the wage equation, a sample-selection bias
problem occurs in the estimation procedure (Heckman 1974,
1979)
To address
correcting
this
problem,
sample-selection
estimation of wage equations.
bias
Heckman's method for
is
utilized
in
the
In the first stage, a probit
equation for labor force participation is estimated.
From
the parameters estimated in the probit equation, the inverse
of Mill's ratio is estimated.
In the second stage,
the
inverse of Mill's ratio, which represents the probability
that an observation is selected into the sample, is included
41
as an additional regressor in the ordinary least squares
estimation of the wage equation for the truncated sample of
(in order to correct the sample selection
employed heads
bias).
All the equations were estimated by using TSP version
4.1.
Table 1 shows the results of the probit estimation for
labor force participation of female heads or spouses.
The
explanatory variables are the female head's age (FAGE); her
age squared (FAGESQ); education (FEDUC); tenure status, where
TEN1 is home owner, which is omitted, and TEN2 is renter;
race, where RACE1 is white, which is omitted, and RACE2 is
non-white; marital status, where FMAR1 is married, which is
omitted, and FMAR2 is not married; and the number of children
in the household under age 7 (CHILD).
Education level is
specified as a continuous variable with following categories:
1 = less than the equivalent of high school graduation, 2 =
high school graduation,
3
= some college
,
4
= college
of
the wage
graduation, and 5 = post baccalaureate.
Table
2
shows
the estimation results
equation for the female heads with jobs.
The explanatory
variables are as discussed above and MILLS is the inverse of
Mill's ratio.
The natural logarithm of wage rate is
used as a dependent variable.
Table 3 and 4 shows the results of probit estimation and
the
estimation
respectively.
of
the
wage
equation
for
male
head,
The number of children in the household under
42
Table 1.
Results of Probit Estimation for Labor-force
Participation by Female
Parameter
Estimate
t-statiStiCS
CONSTANT
-2.1323
-7.489
0.1263
10.264
-0.0017
-12.776
FEDUC
0.0712
2.908
TEN2
0.0092
0.139
RAC2
-0.0061
-0.051
CHILD
-0.2621
-2.699
FMAR2
0.3743
5.550
FACE
FAGESQ
Table 2.
Results of the Estimation of the Wage
Equation for Females with Jobs
Parameter
Estimate
t-statisticS
CONSTANT
-0.0202
-0.059
0.0781
6.057
-0.0008
-4.993
0.2031
2.805
FEDUCSQ
-0.0050
-0.455
RACE2
-0.0813
-1.322
MILLS
0.2358
1.685
FAGE
FAGESQ
FEDUC
43
Participation by Males
t-statistics
Parameter
Estimate
CONSTANT
-3.0424
-8.961
0.2261
14.896
-0.0029
-17.697
0.0376
1.299
TEN2
-0.2389
-3.062
RACE2
-0.1503
-1.001
MAGE
MAGESQ
MEDtJC
Table 4.
parameter
Results of the Estimation of the Wage
Equation for Males with Jobs
Estimate
t-statistics
CONSTANT
0.9213
1.833
MAGE
0.0499
2.135
MAGESQ
-0.0004
-1.451
MEDUC
0.1281
1.930
MEDUCSQ
0.0059
0.596
RACE2
-0.0854
-1.392
MILLILS
-0.1263
-0.665
44
age seven and marital status were not included in male head's
probit equation since the male's employment decision might
not be affected by those variables.
The coefficients in table 2 and 3 were used to calculate
the estimated value of time for female heads and male heads,
respectively.
As mentioned above, the estimated value of
time for female head was used as the value of time of the
household.
For households without
a
female
head,
the
estimated value of time for male head was used as the value
of time of the household.
a retired person,
However, for households headed by
the value of time of the household was
assumed to be zero.
4.2.4
Subsample Used in Estimation
Only a subsample of single family and duplex residents
from the Metro survey was used in the estimation procedures.
Residents of apartment, condominium and mobile homes were
excluded because of the difficulty in measuring payments for
solid waste collection services, which is usually a fixed
cost included in their monthly rent.
Households which self-
haul solid wastes to landfills at least 12 times a year were
excluded also since their behavior is affected by payment
schedules at landfills.
Observations with missing data on
endogenous and/or exogenous variables were also excluded.
Table 5 shows the average values of explanatory
variables for the sample of 2298 observations used in the
45
Table 5.
Means of Exogenous Variables
Mean
Variable
Mnemonic
Monthly Household Income ($)
INC
3,883
Value of Time ($/hour)
VTIME
10.53
Payment Difference ($/month)
GAP
6.53
Number of People in Household
NO
2.98
Education Level
EDtJC
3.06
Non-White
RACE2
0.05
Home Renter
TEN2
0.20
46
estimation procedures.
Mean of RACE2 (0.05) implies that 5
percent of the sample is non-white.
Mean of TEN2
(0.20)
implies that 20 percent of the sample is home renter and 80
percent is home owner.
The variable M-K (full income minus
total payment for waste collection service) was substituted
with household income in the estimation process.
4.3
4.3.1
Estimation Procedures
Estimation of Choice Models for
Recycling Participation
Since data on the total amount of recyclables are not
available,
we
constructed
recycling efforts.
discrete
choice
models
for
The models are based on information
concerning frequencies of a household's participation in
curbside recycling.
Two models are considered: one is for
binary choice, and the other is the multiple choice case.
For the multiple choice case, the household choice set
for frequency of recycling participation is categorized as
zero, less than 12 times a year, once a month, once every
other week and once a week.
Then households reporting
participation rates of 5 to 11 times a year, quarterly and
less than 4 times a year are aggregated into the less than 12
times a year group.
Since the choices are ordered, the model is estimated by
using the ordered probit estimation method.
A problem of
I
47
this model is that the household's choice is affected by the
quality
of
curbside
recyclable
services.
collection
Specifically, the household's choice is restricted by the
haulers'
schedule
pick-up
Unless
recyclables.
for
a
recyclable collection service is provided once a week by all
haulers, the choice set might be restricted by the hauler
that the household has selected.
In the Metro area, recyclable collection services are
provided once every other week or once a month
in
some
cities, although once a week services prevail in most cities.
In those cities, the household's opportunity of choosing once
a
week
participation
Nevertheless,
might
be
limited
by
haulers.
we assume here that the model of multiple
choice is estimated by assuming the choice set is not limited
by suppliers
household
of
services.
responds
t'Ue
affirmatively
further assume that the
to
the
"once
a
week"
participation question on the Metro survey if it separated
recyclable materials once a week (even though the services
were provided once every other week or once a month)
For the binary choice case, the choice set consists of
two categories: participation in curbside recycling or nonparticipation.
Thus,
all households that participate in
curbside recycling are treated as one category.
estimated using the probit estimation method.
4.3.1.1
The Binary Choice Case
The model is
48
Assuming
a
regression
linear
relationship
between
response variable y and a vector of the exogenous factors xj,
the model is constructed as following:
= /3'x
+ u1,
(i = l,2,...,n)
The relationship between the unobservable response variable
y and the observable dummy variable Y is given by:
Y= 1
Y = 0
if y >0
otherwise.
In this model, Y = 1 represent participation in curbside
recycling and Y=0 non-participation.
Thus, the probability
of Y = 1 (i.e. the probability of participation on curbside
recycling by an individual household) can be expressed as:
Prob[Y1 = 1]
= Prob Lu1 >
f'Xj]
= 1 - F[-'x]
= FL'xJ
where F is the cumulative distribution function for u.
If u1 is normally distributed with mean zero and
variance one, we have a probit model.
For the probit model,
the likelihood function is
L
where
(.)
U ((3'x1)J'[l -
I-i
is the cumulative distribution function of the
49
The log likelihood is
standard normal.
in L
E [Y1 ln('x1)
+
The maximum-likelihood estimates of
(1 - Y1)lntl -
are obtained by solving
first-order conditions for the maximization of log likelihood
function.
We estimated the model using the PROBIT command in
TSP version 4.1.
4.3.1.2
The Multiple Choice Case
To construct the ordered probit model for the multiple
choice case for curbside recycling, define the underlying
relationship as:
13'x
where y
+ Ui,
i
=
1,2,...,n)
is the unobserved response variable, x is the vector
of explanatory variables, and u is error term.
The
categories to which the individual households belong are:
Y =
0
if
Y =
1
if 0
0,
y'
a1,
Y = 2
if a1
Y=3
ifa2y*a3,
Y =
if a3
4
a2,
where the ordinal variable Y represents the categories of the
50
curbside recycling frequency,
Y = 0
:
zero
Y = 1
:
less than 12 times a year
Y = 2
:
once a month
Y = 3
:
once every other week
Y = 4
:
once a week
and a's are unknown parameters to be estimated with
assume that
u1
.
is normally distributed with mean zero and
variance of one,
the probabilities of falling into each
category are given by:
Prob[Y1 = 0]
Prob(Y1 = 1]
where '
If we
(a1
=
Prob[Y1
= 2]
Prob(Y1
= 3]
=
Prob[Y1
= 4]
= 1
-
/3'Xj)
-
(a2 -
'x)
-
(a1
-
fl'x1)
-
(a2
-
(a3
-
-
(a3
-
is the cumulative distribution function of the
standard normal (Greene, 1990).
To insure positive
probabilities for all categories, we require
0 < a1 < a
<
a3.
As was the case for the probit model above, the log
likelihood function for this model is written as:
51
log L
-
i-i j-0
where a0 = 0,
(a4
-
3'Xj)
= 1,
(a1
-
a'x) = 0, and D is
defined by
= 1
if Y1 falls in jth category,
= 0
otherwise,
(i = 1,2 .....n and j = 0,1,..4).
The model
is estimated by using the maximum likelihood
estimation method in TSP version 4.1.
4.3.2
Estimation of the Demand for Solid Waste
Collection Services
Ordinary least squares (OLS) estimation of the reduced
form of the demand equation for garbage collection services
(Se) will yield biased estimates, since the price variable,
GAP, is correlated with the random error term.
GAP is determined by S.
That is, the
To correct for this problem, two
stage least squares (2SLS) estimation method is introduced.
In
addition,
Hausrnan's
(1978)
specification
test
was
performed to check for bias occurring from the measurement
error of the GAP variable.
Instrumental variables used in
2SLS include 1 can payment, 2 can payment, 3 can payment,
INC, VTIME, EDUC, RACE2 and TEN2.
One means of the estimating the function for total solid
52
waste generation (S) might be the application of Heckinan's
procedure used in the estimation of the wage equation. The
function S would be estimated for the subsample of nonparticipants in curbside recycling,
with the inverse of
Mills' ratio (obtained from the probit equation of curbside
recycling participation decision) included as an additional
regressor.
and
S,
However, interpreting the estimation results S
would be complex since the observations of non-
participants used in the estimation of S is also included in
the
estimation of S.
recycling
Furthermore,
participation
decision
since
itself
the curbside
affects
the
generation of total solid waste, it is questionable whether
the estimation procedure for S is adequate.
Thus, we did not
estimate the function S.
4.4
Results and Evaluation
Table 6 shows the results of the probit estimation on
the choice model curbside recycling participation.
All
coefficients except the coefficient of INC are significant at
= 0.05, and the signs are as expected.
derivative of the probability of Y =
continuous variable Xk is given by:
a(Prob[Y-l])
aXk
8(15'x)
aXk
a.
The partial
with respect to a
53
=
where c(.)
is the density function of the standard normal
distribution.
This represents the marginal effect on the
probability of participation (in curbside recycling) by a
unit change in an explanatory variable.
Table 7 shows the
marginal effects of changes in the continuous variables.
For
example, the results indicate that an increase in GAP raises
predicted probability of participation on curbside recycling.
Table
8
shows the estimation results of the ordered
probit choice model of curbside recycling participation.
As
was the case for the probit estimation for binary choice, all
coefficients except INC and RACE2 are significant at a =
0.05.
RACE2 is significant at a = 0.1.
Table 9 shows the predicted probabilities of falling
within each category, and table 10 shows the marginal effects
of the change in the GAP variable.
The marginal effects are
given by the partial derivatives of the probabilities with
respect to GAP.
A unit increase in GAP increases the
probabilities for frequent participation in recycling (Y=2,
Y=3
and Y=4),
but decreases
the probabilities
of
rare
participation (Y=0 and Y=l).
Based on the estimation results of the probit and the
ordered probit models, it appears that the larger the
differences between payment blocks, the more the household
participates in curbside recycling. Conversely, as the value
54
Table 6.
Results of the Probit Choice Model of
Curbside Recycling
t-statistiCs
Parameter
Estimate
Constant
-0.3087
-2.794'
INC
-0.4E-05
-0.610
VTIME
-0.0271
3.990*
GAP
0.0288
3.616*
NO
0.0423
2.080*
EDUC
0.2513
8.087'
RACE2
-0.2745
-2.138'
TEN2
-0.3174
-4.674'
* indicates significance at .05 level.
Table
7.
Marginal Effects of the Continuous Variables
from the Probit Model
Variable
VTIME
ä(Prob[Y- 1])
äXJç
-0.001
GAP
0.011
NO
0.016
EDUC
0.093
55
Table 8.
Results of the Ordered Probit Choice
Model of Curbside Recycling
Parameter
Estimate
Constant
-0.3174
INC
0.19E-05
t-statistics
-3.360'
0.334
-0.0196
-3.585'
GAP
0.0379
5.601'
NO
0.0526
3.041'
EDUC
0.1772
6.936'
RACE2
-0.1889
-1.675"
TEN2
-0.1888
-3.172'
VTIME
a1
0.1174
20.246'
a2
0.8461
56.686'
a3
1.1912
33.890'
* indicates significance at .05 level.
** indicates significance at .1 level.
56
Table 9.
Probabilities for Each Frequency Category
Category
Probability
Y= 0
0.348
Y= 1
0.046
Y=2
0.283
Y= 3
0. :iii
Y= 4
0.212
Table 10.
Marginal Effects of a Change in GAP
Category
Marginal Effects
aprob(Y-o)
aGAP
-0.0140
aProb(Y-1)
3GAP
-0.0006
3Prob(Y-2)
8GAP
0.0010
c3P.rob(Y-3)
aGAP
aP.rob(Y-4)
aGAP
0.0026
0. 0110
57
Table 11.
Parameter
Constant
INC
VTIME
Estimation of the Demand for
Garbage Collection Services
OLS
0.4585
2SLS
0.7258
(10.00)'
(12.95)'
0.11E-04
0.14E-04
(4.26)'
-0.0004
(-0.15)
(4.94)'
0.0030
(1.06)
-0.0046
(-0.71)
GAP
0.0456
NO
0.0913
0.1078
(10.81)'
(11.93)'
EDU
(13.81)'
0.0068
(0.55)
-0.0011
(-0.08)
0.1126
RACE2
0.1437
TEN2
0.0974
0.0936
(3.39)'
(3.10)'
(2.65)'
t-statistics are given in parentheses.
* indicates significance at .05 level.
(1.98)'
58
of time increases, the participation rate decreases.
However, the marginal effects of a change in VTIME are very
Education level is the most significant factor
small.
influencing the household's participation decision on
curbside recycling.
Also, households with large families
participate more in curbside recycling, whereas non-white and
home
renter
households
participate
in
less
curbside
recycling.
Table 11 shows the results of OLS and 2SLS estimations
of
demand
the
for
garbage
collection
services
(Se).
Hausman's specification test was performed to check the
independence of the GAP variable and the error term.
test statistic is y = (b
b
is
a vector
- V0) (b1
parameter estimates
- b0), where
from
the
2SLS
is a vector of parameter estimates of OLS,
estimation, b0
and V1, and V0
of
- b0)' (V
The
are matrices of variance-covariance estimates
under 2SLS and OLS, respectively. Under the null hypothesis,
this statistic is asymptotic. Chi-squared with degrees of
freedom equal to the number of explanatory variables.
Since
the calculated test statistic of 83 exceeds the 95 percent
critical level of 16.92, we reject the null hypothesis that
the GAP variable and the error term are independent.
the
OLS
Moreover,
estimates
presented
in
table
the estimated parameter of
10
are
Thus,
biased.
the GAP gives the
unexpected sign.
The estimation results from 2SLS indicate that the
59
parameter estimate of household income is significant and the
sign
positive,
is
However,
expected.
as
income
the
elasticity calculated at mean values of income and S is very
inelastic
demand
Non-white and home renter households
(0.049).
garbage
more
collection
Also,
services.
the
households with large families demand more garbage collection
services.
indicates
Finally,
the parameter estimate
increase
that
in
of
the GAP
the GAP reduces demand for
collection services, although it is not significant.
An interesting point is observed when we evaluate the
estimation results from both the choice models on curbside
recycling and the demand for garbage collection services.
Specifically,
in
the estimation results for the choice
models, GAP was a significant factor affecting household's
choice on curbside recycling.
The increase in the GAP raises
the probability of participation on curbside recycling. This
implies that the amount of recyclables increases and the
amount of non-recyclables decreases as the GAP increases.
However, according to table 11, the demand for garbage (non-
recyclables) collection services is not changed as the GAP
increases
household's
(GAP
is
amount
not
of
Therefore,
significant).
recyclables
is
not
each
increased
significantly when the GAP is increased; the conclusion is
supported by the low marginal effect of a change in the GAP
to the change of the probability of participation.
One possible explanation for this somewhat contradicting
60
finding above is as following.
Households that generate
total solid wastes in the neighborhood of payment blocks
(i.e.
1.1 can,
2.1 can, and etc.) will have the greatest
recycling incentive under the block payment system. However,
they can realize the available savings with a very small
reduction
of
recyclables).
non-recyclables
(or
a
small
increase
of
If a major recycling effort is required to
realize savings through curbside recycling, the household is
less likely to participate in curbside recycling.
Thus, an
increase in the amount of recyclables due to the increase in
the GAP will not be substantial even though the participation
rate on curbside recycling increases significantly.
61
CHAPTER 5
POLICY IMPLICATIONS FOR
RESIDENTIAL SOLID WASTE MANAGEMENT
5.1
Efficiency of a Block Payment System
As presented in the theoretical model of solid waste
generation, individual households decide, in a simultaneous
fashion, the optimal levels of recyclables, non-recyclables
and total solid wastes generated.
Comparative static results
from the model suggest that an increase in the marginal price
of garbage collection reduces the household's demand for
garbage collection services.
The household can maintain a
previous level of consumption by expending more time on
recycling when the marginal price of garbage collection
services is increased.
Conversely, the household can keep
the previous (lower) level of recycling effort by reducing
the total amount of solid wastes generated.
The results from the empirical analysis in chapter 4
indicate that an increase in the GAP raises the household's
recycling effort.
However, an increase in the GAP does not
reduce demand for garbage collection services.
This implies
that the effect of an increase in the GAP on the total amount
of recyclables is small.
To explore further this finding,
Appendix A presents the estimation results of a total solid
waste generation function based on a subsample of households
which do not participate in curbside recycling;
i.e,
for
62
these households, the amount of non-recyclables is equal to
the
total
amount
households.
of
solid wastes
generated
for
those
The results show that a change of the GAP does
not influence the household's consumption level and thus the
quantity of total solid waste generation,
participants.
Therefore,
even for non-
both the empirical results of
appendix A and the theoretical comparative static results
imply that a change of the GAP does not affect overall level
of total solid wastes.
The empirical analysis reported in chapter 4 reflects
the cross-sectional variations in the block payment schedules
currently in place in selected cities in the Metro area.
Thus, these results imply that the overall demand for garbage
collection services has not been affected by the changes of
the GAP arising from the imposition of a block payment
marginal pricing system.
The results generated in the empirical analysis also
indicate that household income is not a significant factor in
explaining participation in curbside recycling. While income
is
significant
in
determining
the
demand
for
garbage
collection services, the response is inelastic with respect
to income.
Therefore, small changes in household income are
not likely to affect the household's level of total solid
wastes.
The adoption of a universal block payment system in the
Metro area would affect
individual households'
behavior
63
through both an income effect and price effect; the income
effect involves the change in total payment on garbage
collection services and the price effect arises from the GAP
structure of the system.
In the Metro area,
the total
payment for garbage collection services is a small portion of
household income; average household income was $3,883 per
month and average monthly payment for garbage collection
services was $11.60 for 1 can, $19.50 for 2 can, and $26.40
for 3 can.
This implies that individual households' total
expenditures on consumption are not affected significantly
by the introduction of the block payment system.
Thus, the
income effect of the block payment system would not be
substantial in the Metro area.
payment
system on
solid waste generation
total
significant as noted above.
can declines.
However,
The price effect of the block
is
not
Further, the marginal cost per
the GAP structure of the system
provides an incentive to reduce expenditures on garbage
collection services through curbside recycling; households
participate in curbside recycling as long as the gains from
the participation exceed the time costs.
Gains here are
assumed to be the monetary savings due to reduced payments on
garbage collection services.
Losses are associated with the
value of time and disutility due to recycling efforts.
The nature of
the
empirical
results
reductions in the amount of non-recyclables
suggests
that
(due to the
introduction of the block payment system) are likely to be
64
achieved through an increase in the amount of recyclables
rather than changes in the household's consumption patterns.
This implies that under the block payment system, individual
households decide the level of recycling effort after the
generation of total solid wastes rather than simultaneously
decide their amount of recyclables, non-recyclables and total
solid wastes.
the
In the Metro area, it is unknown how effective
implementation of
the block payment system was
in
reducing the demand for overall garbage collection services
because of the lack of data.
Other research indicates that the quality ofrecyclable
collection services is important in improving the household's
recycling efforts (Bureau of Environmental Services, 1990).
The block payment system and the quality of recyclable
collection
household's
services
are
recycling
complementary
efforts.
in
boosting
Without
the
dependable,
convenient pick-up services for recyclables, a household's
participation in curbside recycling is limited, even though
the payment difference between blocks is large.
Conversely,
even
services,
with
dependable
recyclable
collection
participation in curbside recycling will be lower without the
rate incentives.
Those empirical studies done in the Metro
area show that a change from monthly pick-up services to
weekly pickup services has boosted significantly recycling
participation (Bureau of Environmental Services, 1990, p 8).
In the present study,
about 45 percent of apartment and
65
condominium
residents who
do
not
have
rate
incentives
participate in curbside recycling, primarily in the form of
recyclable newsprint.
However, more than 60 percent of the
single household and duplex residents participate in curbside
recycling.
Single and duplex residents' education level and
value of time at the mean are similar to apartment and
condominium residents'.
Although average household size of
single and duplex residents is larger than that of multifamily residents, table 7 shows that a one person increase in
household size increases the participation rate in curbside
recycling by just
1.6
percent.
Thus,
the
significant
difference in recycling participation rate between single and
duplex residents and multi-family residents are likely to due
to the GAP and the quality of services.
recyclable
weekly
collection services
collection,
the
same
are
day
In the Metro area,
standardizing toward
collection
with
non-
recyclables and the distribution of recycling containers by
haulers.
In an attempt to further motivate household recycling
efforts, small can (15 to 20 gallons) services at lower rate
than regular can (32 gallons) services have been suggested in
the Metro area.
It is expected that small can services will
soon be available in all areas within the Metro boundary.
If
quality collection services on recyclables are provided with
the small can service,
the introduction of such services
increases the likelihood that the household would reduce its
66
level of non-recyclables by the equivalent of two or more
blocks.
For example, under the current block payment scheme,
a household that generated 2.1 cans of total solid wastes is
expected to produced 0.1 can of recyclables (to achieve the
first block reduction).
Although the household could save
more money by reducing the level of non-recyclables further
(to
1
can),
the required increase in recycling
(1.1 can)
generates less benefits than the opportunity cost losses from
recycling; that is, marginal benefits of further recycling
are less than marginal costs.
However, under the small can
option, a 1.5 can level of non-recyclables will be available
at a rate lower than for 2 cans.
In this case, the household
can achieve some saving by generating just
recyclables.
0.6
can of
The household will reduce the level of non-
recyclables (from 2.1 to 1.5 cans) if marginal benefits from
further
recycling
exceed
marginal
costs.
Since
the
disutility from recycling increases as the time spent at
source-separation increases, the possibility of a net gain is
greater with 0.6 can of recyclables than in the case of 1.1
can.
That is, the possibility that the household engages in
more recycling
(from 0.1 can to 0.6 can of recyclables)
increases with the introduction of the small can service.
The introduction of
the small can option also provides
recycling incentives for households that do not participate
in curbside recycling under the current payment schedule.
Although
not
tested
formally
here,
the
descriptive
67
information in the Metro surveys and the significance of the
GAP variable on household recycling efforts suggests that the
introduction of the small can service will reduce the amount
of non-recyclables.
5.2
Comparison of Alternative Pricing Options
Four pricing systems can be considered for residential
solid waste collection:
(1)
a flat payment system,
(2)
a
marginal pricing system without a discount on recyclables,
(3) a marginal pricing system with a discount on recyclables,
and
(4)
block payment
a
recyclables.
system
with
zero
charges
on
The implication of each for recycling are
straight forward.
The flat payment system does not provide
any incentive for the household to participate in curbside
recycling or to reduce the demand for garbage collection
services.
Since the marginal cost to the household for more
waste collection services is zero, the system does not punish
those households that generate large amounts of solid waste.
Regardless of the level of discount on recyclables, a
marginal pricing system provides incentives for the household
to reduce the demand for garbage collection services.
A
marginal pricing system without a discount on recyclables is
analyzed in Wertz
(1976)
.
As shown in section 2.2.1, an
increase in price reduces the demand for waste collection
services.
In section 3.2,
it was demonstrated that the
increase in marginal price reduces the demand for garbage
68
collection services under a marginal pricing system with zero
charges on recyclables. However, the mechanism for achieving
the reduction in demand is quite different under the two
systems.
Under a marginal pricing system without a discount
on recyclables, the household reduces its amount of solid
wastes
either
through
less
consumption
or
through
the
substitution for more packaged goods by less packaged goods.
There is no incentive for curbside recycling.
Under the
marginal pricing system with zero charges on recyclables, the
demand for garbage collection services is reduced by the
decrease in the amount of total solid wastes and/or by the
increase in the amount of recyclables.
Even though it is
difficult with current data to estimate which system is
better, the second system has broader appeal in reducing the
demand for garbage collection services.
In practice, the
household's total payment for garbage collection services is
a very small portion of the household income.
Thus, the
household determines its optimal level of consumption without
considering the payment on garbage collection services.
As
discussed in section 5.1, the household will then participate
in curbside recycling if the opportunity of saving money
through
recycling
is
provided.
Therefore,
unless
the
marginal price is high enough to influence the household's
budget
constraint,
a marginal pricing system without
a
discount on recyclables will not be effective in reducing the
amount of solid wastes.
69
Under the marginal pricing system, all households are
affected by the change in the marginal price, and the total
payment for solid waste collection services is in proportion
to the reduction in the amount of non-recyclables.
However,
under the block payment system, only households within a
specific range of the solid waste levels are influenced by
the change in the price variable (GAP).
Households that are
located midway along the block typically do not confront a
GAP "step" and are thus not affected by changes in price,
unless the structure itself is changed.
For example, in the
Metro area, households that generate less than one can of
solid waste do not have any incentive to reduce level of
total solid wastes (i.e. they face a flat fee).
Furthermore,
the payment for garbage collection services is not reduced in
proportion to
the reduction in the amount of
the non-
recyclables, even for households that have a rate incentive
under the block payment system.
Thus,
a true marginal
pricing system will be more effective in reducing household
demand
for garbage
structure.
collection
services
than the block
However, under a true marginal pricing system,
administ.rative costs will be large since measurement of the
volume or weight of wastes generated is needed to calculate
the household cost or payment.
Since standardized cans or
carts are used under a block payment system, the measurement
of the volume or the weight is not required, resulting in low
administrative costs.
70
In summary, while a block payment system requires less
administrative costs than a marginal pricing system, a block
payment system will be less effective than a true marginal
pricing system in reducing household demand
collection services.
f or garbage
For a more comprehensive comparison of
the advantages of a block payment system and a marginal
pricing system,
a
formal benefit-cost analysis
systems in a real world setting is required.
of both
At this time,
such data are not available in the Portland area.
71
CHAPTER 6
StJNMARY, CONCLUSIONS, AND
SUGGESTIONS FOR FUTURE RESEARCH
The economic analysis of household behavior regarding
residential solid waste generation has been driven largely by
the pricing systems under consideration.
Since flat payment
systems prevail in most regions of the United States, simple
models that relate the generation of household solid wastes
to
socio-demographic
factors
have been constructed and
applied. With the increasing emphasis on household recycling
in many communities,
emerged.
rate incentive pricing schemes have
Based on such rate incentive systems,
a more
comprehensive model of residential solid waste generation has
been constructed here.
In addition to rate structures, the
model expresses the household production of total solid waste
and recycling effort
as
a
function
of
time and goods
allocation within the household.
This study represents the first attempt to analyze
formally
the
efforts.
The study also benefits from the use of a large
data
representing
set
Portland,
household's
behavior
4,306
regarding
households
Oregon metropolitan area.
drawn
recycling
from the
This rich data set
allows for the estimation of the household participation
decision concerning recycling efforts.
From a
theoretical perspective,
a marginal pricing
system with zero charges on recyclables provides an incentive
72
for households to dispose of less of their solid wastes
through garbage collection services (by generating less of
total wastes and/or by increasing recycling efforts).
In the
empirical analysis undertaken here, the primary focus is on
how effective the block payment system would be as an
incentive to recycle and to reduce the demand for garbage
collection services.
The analysis tests whether payment /
differences as reflected in differences between blocks under
a block payment system provide a similar role as a true
marginal price system. The results indicate that an increase
in
the
payment
difference
increases
the
frequency
of
household participation in curbside recycling, but such a
rate structure
oes not reduce significantly the demand for
;garbage collection services.J This result implies that the
quantity
of
recyclable
solid
wastes
is
not
substantially by the change of the GAP variable.
affected
However, in
order to derive an exact relationship between the amount of
recyclables and recycling effort,
recyclables
generated are
data on the amount of
required;
such
available for the current case study.
data are
not
When data on the
amount of recyclables are available, structural equations of
the
demands
for
total
non-recyclable
and
recyclable
collection services can be estimated as well as the reduced
form equations.
From the estimation results obtained in the case study
of Portland, we tested for(factors influencing total solid
73
waste generation.J
Income is positively and significantly
related to the demand for garbage collection services, but
does
not
affect
the participation decision on curbside
This implies that income
recycling.
is significant
in
influencing total solid waste generation; this is supported
also by results reported
in Appendix A.
Increases
in
household size (number of people in a household) raises both
the frequency of participation in curbside recycling and the
demand for garbage collection services, implying an increase
in total solid waste generation with increasing household
Income and household size are statistically
population.
significant
factors
in
determining
solid
total
waste
generation
a block payment system that
In order to implement
reduces total solid waste, it appears that the system must be
supported by high quality recyclable collection services.
Since total expenditures for garbage collection services are
a. very small proportion of the typical household's total
disposal income,
the only possible means of reducing the
amount of non-recyclables is to boost household recycling.
Creating
providing
more
blocks
quality
(GAPs)
services
in
will
payment
schedules
encourage
and
household's
recycling effort at the margin and thus increase the amount
of recyclables.
However, the existing empirical study is
limited in that information on suppliers' terms of services
(such as frequency of pick-up service and the distribution of
74
recycling containers) is not available.
In future research,
it will be useful to include variables representing
quality of
evaluate
recyclables
their
effects
participation decision.
collection servicesj in
on
the
household's
order
the
to
recycling
A comparison of household recycling
before and after the adoption of small can service will also
be useful to measure its effectiveness in supporting a block
payment system.
As block payment systems move closer to a true marginal
pricing system (by creating more blocks), the household's
recycling effort will increase.
However, administrative
costs of collection services will also increase.
A benefit-
cost analysis of comparing alternative pricing schemes is
therefore needed.
To perform such an analysis, information
on administrative costs
(including costs of solid waste
collection and disposal) under alternative schemes must be
available.
Estimation of a range of external costs, such as
health effects and environmental damages, is also needed.
Further, demand functions for solid waste management services
under alternative pricing systems must be estimated.
75
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APPENDICES
80
Appendix A
ESTIMATION OF ThE TOTAL
SOLID WASTE GENERATION FUNCTION
Parameter
Estimate
t-statigtics
Constant
0.6495
5.6761**
INC
0.00001
1.9469**
VTIME
0.0065
1.0487
GAP
0.0062
0.4312
NO
0.1069
6.0639**
EDU
0.0077
0.2799
P.ACE2
0.0226
0.2211
TEN2
0.1111
2.0O31*
* the function is estimated for a subsample of nonparticipants in curbside recycling by using 2SLS.
** indicates significance at .05 level.
**
indicates significance at .1 level.
81
Appendix B
DATA
1. Garbage Rates for Individual Households
Cities
1 can
Rate
2 can
3 can
8.82
16.78
24.74
Cornelius
10.15
19.16
27.15
Hilisboro
10.30
19.75
29.70
Beaverton
10.21
19.31
27.93
Tigard
11.25
21.20
33.45
Forest Grove
King City
Durham
9.65
18.60
27.55
10.75
19.80
28.85
Tualatin
11.05
19.00
21.63
Sherwood
11.70
21.60
31.50
Troutdale
11.23
21.46
23.64
Gresham
11.26
21.52
30.43
Portland
12.11
21.54
26.73
Milwaukie
11.75
22.25
31.80
Lake Oswego
10.18
19.40
27.67
West Linn
12.00
24.00
36.00
Gladstone
12.10
22.00
31.90
Happy Valley
Oregon City
12.20
24.40
27.93
12.50
23.30
34.10
Wilsonville
11.30
22.60
25.14
Washington Co.
11.15
22.30
33.45
Multonomah Co.
12.25
17.50
22.86
Clackamas Co.t
12.20
21.52
26.07
* County rates applied to those households not located
within city boundaries.
82
2. Data Used in Estimation of Choice Models of Recycling
Participation and Demand for Garbage Collection Services
Variable Definition
amount of non-recyclables
payment difference
monthly income
value of time
number of people in household
education level
race: 1 = white, 2 = black, 3= asian, 9 = other
tenure status; 1 = home owner, 2 = renter
frequency of recycling participation:
o = zero,
1 = weekly,
2 = every other week,
3 = monthly,
4 = less than once a month, but more than f times a year,
5 = 4 times a year,
6 = less than 4 times a year,
7 = participate but don't know exact frequency
Data, by Variable
1
2
5.71
1.0 10.26
3.0 8.61
1.0
9.01
5.71
1.0
1.0
3
4
56789
3167 8.82820 3 2
6250 10.54974 2 4
2500
9.90507 4 2
2167 13.83789 2 3
1167 13.82215 2 3
1 1 0
1 1 3
1 1 0
1 2 3
1 1 3
83
1.0
1.0
5.25
9.11
0.5
0.00
1.0
1.0
1.0
2.0
1.0
9.32
5.71
9.46
9.94
9.32
2.0 10.81
1.0 10.51
2.0 10.81
2917 11.00046 3 2 1 2 3
4167 16.35837 2 3 1 1 2
6250 0.00000 2 3 1 1 2
8333 18.43013 4 5 1 1 3
2917 1101923 1 3 1 1 2
2917 12.17692 1 3 1 2 3
4167 14.68427 5 4 1 1 1
4583 10.54974 2 4 1 1 1
667 8.44521 3 1 1 2 0
2000
0.00000 3 3 1 1 1
6250 11.65117 3 3 1 2 0
1.0 9.22 1833 7.98670 1 2 1 1 2
2.0 11.15 6250 13.39941 5 4 1 1 2
1.0 9.95
750 11.70761 1 2 1 2 1
1.0 9.32 5833 16.50276 5 5 1 1 2
1.0 10.80 2500 11.64328 3 2 1 1 0
1.0 7.95 4583 0.00000 2 4 1 1 1
1.0 5.71 4667 13.88197 3 3 1 1 4
1.0 5.71 2333 0.00000 1 3 1 1 3
0.1 0.00 1000 0.00000 1 2 1 1 0
0.1 0.00 4167 17.30789 4 5 1 1 3
0.1 0.00 3333 0.00000 2 2 1 1 0
1.0 5.71 1250 0.00000 1 1 1 1 0
1.0 5.71 4167 14.68427 2 4 1 1 1
1.5 5.71 4167 13.57449 2 3 1 1 1
1.0 5.71 2500 0.00000 3 3 1 1 1
0.3 0.00 5000 13.94966 3 4 1 1 0
0.3 0.00 1667 15.56308 2 5 1 1 3
1.0 9.11 6500 12.70651 3 3 1 1 3
1.5 8.95 3750 15.56308 5 5 1 1 0
1.5 10.26 4167 12.30899 4 3 1 2 1
2.0 11.15 8333 9.56842 3 3 1 1 3
2.0 8.61 3583 10.91117 4 3 1 2 0
1.0 9.1]. 3000 10.88270 2 4 1 1 0
1.0 5.71 5000 10.66240 3 3 1 1 3
2.0 3.53 1417 13.65307 2 3 1 1 3
2.0 5.49
75 5.41472 4 1 1 2 0
0.8 9.11 2500 22.01050 4 5 1 1 3
2.0 5.36 2083 10.92649 2 2 1 1 0
1.0 5.71 5417 9.84746 2 3 1 1 3
1.0 9.95 4000 13.82222 2 3 1 1 4
1.0 5.71 2500 11.56369 2 2 1 1 0
1.0 5.7]. 2833 10.22905 6 2 1 1 0
1.0 12.00 7500 12.11373 2 3 1 1 3
0.5 0.00
333 11.01605 2 3 1 2 0
0.3 0.00 2750 6.40474 2 3 1 2 1
0.8 9.95 2167 0.00000 1 2 1 1 0
1.0 7.95 5000 8.84222 1 1 1 1 3
1.0 5.71 1833 13.61928 3 3 1 2 3
1.5 5.71 1167 12.15842 1 3 1 2 0
1.0 5.71 3333 11.09567 4 2 1 1 2
1.0 5.71 3333 16.18633 4 5 1 2 3
84
1.0
0.3
1.0
1.5
1.0
1.0
2.0
5.71
0.00
9.11
5.71
5.71
5.71
5.49
1.0
1.0
0.5
1.0
2.0
0.5
1.0
2.0
1.0
1.0
2.0
1.0
1.0
0.5
1.0
1.0
5.71
5.71
0.00
5.71
5.49
0.00
5.71
5.36
5.71
5.71
8.61
9.46
9.11
0.00
8.95
9.90
2.0
2.0
1.0
2.0
2.0
0.5
1.0
8.61
8.61
9.11
5.49
8.90
0.00
9.11
1.0
0.3
5.71
0.00
0.3
0.00
1.0
1.0
1.0
0.5
9.11
9.32
5.71
0.00
0.5
1.0
1.0
1.0
1.0
1.0
1.0
2.0
0.00
5.71
9.11
9.11
9.32
5.25
9.11
8.61
1.0 10.26
1.0 12.20
1.0 12.20
1.0 11.30
1.0 11.15
1.0 10.26
1.0 11.15
4417 14.45263 5 4 1 1 3
1833 10.94368 2 2 1 1 0
2917 10.71920 2 3 1 1 3
5833 10.66240 2 3 1 1 2
5000 11.87043 4 4 9 2 2
4750 11.66389 2 2 1 1 0
1000 11.15925 2 2 2. 1 3
500 0.00000 1 2 1 2 0
2833 9.12208 1 2 1 1 0
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2.0
1.0
0.00
5.71
5.25
9.32
5.25
4.55
5.71
1.5
3.5
1.0
5.71
5.49
5.71
1.0
5.71
1.0
1.0
1.0
1.0
1.5
4.0
0.8
1.0
0.2
1.0
1.0
1.0
1.0
1.0
5.71
9.46
5.25
5.71
5.71
5.49
9.95
5.71
0.00
5.71
5.25
5.71
5.71
5.71
1.0
5.71
2.0
0.5
1.0
2.18
0.00
5.25
3.0
0.5
0.8
1.0
1.0
1.0
5.49
0.00
5.71
5.71
9.46
5.71
1.0 10.26
1.0 11.15
0.8 10.26
1.0 10.26
1.0 10.23
1.5 10.23
1.0 10.51
1.0 11.15
1.0 10.23
1.0 11.15
1.0 10.51
2.0 8.90
8333 13.04033 4 3 1 1 0
5417 14.45263 4 4 1. 1 4
2917 9.56842 3 3 1 1 3
2500 15.51120 3 4 1 1 0
3500 11.35429 3 2 1 1 0
2917 13.68000 3 4 1 1 1
4750 9.64854 3 2 1 1 0
3333 0.00000 2 4 1 1 2
2083 0.00000 2 2 1 1 0
2917 11.01354 3 2 1 1 3
6667 13.82215 4 3 1 1 3
3000 9.31923 6 1. 1 1 2
7083 18.84190 3 5 1 1 3
2167 7.83897 5 2 1 2 0
5417 18.69342 2 5 1 1 2
2500 10.21714 3 2 1 2 2
4583 16.22792 2 4 1 1 3
2667 0.00000 2 3 1 1 0
3333
8.55234 3 2 1 1 0
4167 11.72246 3 2 1 2 0
7083 11.86751 2 4 1 1 7
7167 10.92370 4 3 1 1 3
1667 0.00000 3 2 1 1 0
1500 10.11141 4 3 1 2 0
3333 11.13863 4 2 1 1 1
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5250 16.50276 2 5 1. 1 1
2667 9.79799 2 2 1 1 0
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2083 11.15925 2 2 1 1 0
7500 13.77736 2 3 1 1 1
6667 18.13341 2 5 1 1 1
2500 12.17388 4 4 1 2 1
1333 11.07729 1 2 1 2 3
2500 11.62375 3 3 1 1 3
500 7.79103 1 1 1 1 1
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3333 13.38416 3 5 1 2 3
3333 7.60140 3 2 1 2 3
3083 10.04357 3 2 1 1 3
2417 10.77107 6 3 1 1 1
5000 11.74270 2 2 1 1 0
2667 9.56842 4 3 1 1 4
3333 13.63589 4 3 1 1 3
6250 12.28762 4 4 1 1 1
5417 13.94966 3 4 1 1 0
1833 10.21610 4 4 1 1 0
6667 11.35429 7 2 1 1 0
2333 11.72447 2 2 1 1 1
2750 17.30789 4 5 1 1 2
5000 13.63589 4 3 1 1 0
116
2.0 11.15 11667 11.58899 4 2 1 1 2
1.0 10.26 3333 16.11848 3 4 1 2 0
1.5 9.95
500 7.58018 2 2 1 2 1
1.0 5.25 3750 13.82222 4 3 1 1 3
0.5 0.00 2083 7.01603 4 2 1 1 0
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0.8 10.26 4167 14.68427 5 4 1 1 0
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1.0 5.71 1250 15.73983 6 5 3 1 3
1.0 10.51 4000 9.00589 4 2 1 1 1
1.0 9.32 3750 10.92370 4 3 1 1 2
0.5 0.00 2500 18.72508 3 5 1 1 0
1.0 5.71 3750 16.78737 3 5 1 1 3
2.0 5.49 2917 13.57449 2 3 1 1 3
1.0 5.25 4583 16.25203 3 4 1 1 2
0.5 0.00 1000 0.00000 1 2 1 1 1
2.0 5.49 4833 13.18737 3 3 1 1 3
2.0 5.49 4167 12.50195 5 4 1 1 0
1.0 10.26 2500 8.54188 3 2 1 2 3
1.0 9.32
833 0.00000 2 1 1 1 0
1.0 5.71 2500 11.71624 3 2 1. 1 3
0.5 0.00 1667 0.00000 2 3 1. 1 0
1.0 5.25 2500 11.35429 3 2 1 1 1
1.0 11.15 1667 14.20749 4 4 1 1 3
1.0 5.25 1417 9.23861 1 2 1 1 3
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1.0 5.71
583 7.91667 1 1 9 1 2
1.0 9.46 1667 0.00000 2 3 1 1 4
1.0 9.32 4500 16.29451 1 3 1 1 0
1.0 5.25 3333 11.42551 5 3 1 1 3
1.0 5.71 2083 10.57298 4 2 1 1 0
1.0 10.26 3750 12.87990 4 3 1 1 4
2.0 5.49 2500 13.82215 2 3 1 1 0
1.0 5.25 2417 9.42918 7 2 1 2 1
1.0 5.71 1583 0.00000 1 2 1 1 3
1.0 5.71 2667 15.29132 4 4 1 1 3
1.0 5.71 2917 16.83252 2 4 1 1 2
1.0 9.32 10000 11.52273 4 2 1 1 0
1.0 5.25 4167 13.54510 2 3 1 1 3
1.0 11.15 8333 14.68427 4 4 1 1 3
0.8 9.46 2917 12.10012 2 3 1 2 0
1.0 5.71 2500 11.64328 3 2 1 1 1
1.5 5.25 5000 6.82799 6 2 1 1 2
1.0 10.26 4917 15.08766 4 4 1 2 0
1.0 5.25 2917 10.16460 4 2 9 1 4
2.0 5.36 5000 15.10423 5 4 1 1 3
1.0 5.71
500 0.00000 4 2 9 1 0
1.0 11.15 3333 11.58899 3 2 1 1 1
2.0 5.49 4833 13.3994]. 7 4 1 1 3
1.0 10.26 2667 16.06801 4 4 1 1 1
1.0 5.71 1917 11.49964 3 2 1 1 3
117
1.3
2.0
0.5
1.0
5.71
5.49
0.00
5.71
1.0
1.0
5.71
9.46
4167 11.64328 2 2 1 1 0
2500 10.53245 4 3 2 2 0
417 0.00000 1 1 1 1 1
1333 12.15900 2 4 1 2 2
1.0 10.26 2250 11.72447 2 2 1 1 3
0.8 5.71 1667 9.29411 1 4 2 2 6
1.0 9.32 2750 0.00000 2 2 1 1 3
0.3 0.00 1333 13.41700 1 3 1 1 4
1.5 11.15 5583 16.78737 4 5 1 1 4
1.0 10.26 3333 8.99510 3 2 1 2 0
2.0 5.36 2083 8.84222 7 1 1 1 0
2.0 11.15 8583 19.08557 1 5 3 1 2
1.0 9.22 4750 11.20079 3 4 1 2 3
1.0 10.26 4000 9.49969 2 1 1 1 2
0.5 0.00
167 10.24441 1 3 1 2 0
1.0 5.25 5833 13.77440 2 3 1 1 4
0.3 0.00 2083 0.00000 2 2 1 1 0
1.0 5.71 3333 13.43999 1 3 1 1 1
2.5 5.49 2292 0.00000 5 2 1 1 0
1.0 5.71 7500 16.57019 2 5 9 1 0
0.8 5.71 2000 0.00000 2 2 1 1 3
2.8 5.49 3667 12.11373 4 3 1 1 3
2.0 9.94 1417 10.39358 5 2 1 2 2
0.5 0.00 4167 0.00000 2 3 1 1 3
1.0 5.71 6250 15.98086 5 4 1 1 1
1.0 9.32 5000 10.21610 4 4 1 1 0
1.0 5.25 2583 7.84897 4 2 1 1 3
1.0 5.71 3333 8.07538 5 2 1 2 2
0.5 0.00 5000 0.00000 2 3 1 1 0
2.0 8.61 5833 9.44031 4 2 1 1 3
1.0 9.95 6667 14.45263 5 4 1 1 2
0.8 9.32 6833 10.96966 4 2 1 1 1
1.0 5.71 4167 15.22481 4 5 1 1 3
2.0 5.49 5000 11.17836 2 3 1 1 3
2.0 5.49 5000 14.51771 4 5 1 1 1
1.0 5.71 1917 10.25141 5 3 1 1 0
1.0 11.15 83333 13.54510 5 3 1 1 1
2.0 4.55 5000 11.01354 3 2 1 1 1
1.0 5.71 2917 13.39941 4 4 1 1 0
2.0 5.36 6250 11.21399 2 4 1 1 3
0.3 0.00 2333 12.32284 4 3 1 1 0
1.0 9.32 3167 9.44031 7 2 1 1 1
0.1 0.00 5833 13.20119 1 2 1 1 0
0.5 0.00 2500 0.00000 1 3 1 1 1
1.0 9.11 3750 11.68566 4 2 1 1 0
1.0 11.15 8333 13.32068 2 3 1 1 3
1.0 5.71 1833 11.58899 5 2 1 1 3
2.0 8.20 6667 18.29172 4 5 1 1 3
1.5 9.46 4333 11.66432 4 3 1 1 0
1.0 9.95 3333 12.50195 2 4 1 1 1
8.02552 3 3 1 2 4
2000 13.94966 4 4 1 1 1
2083
118
1.0
1.0
0.5
5.71
5.25
0.00
0.5
0.5
0.5
0.00
0.00
0.00
1.0
0.8
0.8
2.0
1.0
0.5
5.25
9.11
5.71
8.61
5.25
0.00
1.0 10.26
3417 11.85394 4 4 1 2 0
6667 11.41245 4 3 1 1 0
2917 10.47114 2 3 1 1 3
7167 15.29132 4 4 1 1 1
1083 11.40224 4 3 1 2 3
6667 18.13341 5 5 1 1 3
1917 0.00000 2 3 1 1 0
1.3 11.15 4500 11.54260 3 4 1 1 3
1.0 5.25 1667 0.00000 3 2 1 1 1
1.0 10.26 3500 9.22585 5 2 1 1 1
1.0 5.71 4167 9.84985 3 2 1 1 0
1.0 5.71 1250 11.76433 1 4 1 2 2
1.0 11.15 5417 9.64854 4 2 1 1 3
2.0 5.49 2500 9.21490 8 2 1 2 2
0.3 0.00 2083 13.88197 2 3 1 1 3
1.0 11.15 4583 18.12944 3 4 1 1 3
1.5 9.46 3750 16.50276 4 5 1 1 3
3.0 1.41 41667 11.01354 3 2 1 1 1
1.0 9.22 2250 15.29816 2 4 1 1 3
2.0 3.53 5833 13.63589 3 3 1 1 1
1.0 9.32 2500 10.12340 2 3 1 1 0
2.0 5.49 3500 13.94966 6 4 1 1 0
0.8 10.51 6250 17.75824 2 5 1 1 2
2.0 5.49 7500 19.33104 4 4 1 1 0
1.0 5.71 2083 13.10888 6 4 1 1 3
1.0 5.71 2333 11.86751 5 4 1 1 3
1.0 5.71 2000 0.00000 2 5 1 1 0
1.0 10.26 3833 13.32068 3 3 1 1 1
3167 13.43999 3 3 1 1 3
3500 17.54218 2 5 1 1 3
4583 12.18792 3 3 1 1 3
6000 16.06801 2 4 1 1 0
667 0.00000 1 3 1 1 3
2500 13.62561 2 2 1 1 3
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1.0 10.26
917 10.09699 1 2 1 1 2
1.0 9.32 2917 0.00000 2 5 1 1 3
0.1 0.00 3333 13.66515 4 4 1 2 0
3.0 11.15 12500 9.78400 5 2 1 1 0
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1.0 9.22 3750 13.10888 5 4 1 1 3
1.0 10.26 5833 13.16556 2 3 1 1 1
1.0 5.71 3500 9.00589 2 2 1 1 0
0.3 0.00 20833 13.65307 2 3 1 1 0
119
1.0
0.5
1.0
1.0
0.5
1.5
1.0
1.0
9.22
0.00
9.46
5.71
0.00
9.32
5.71
9.22
1.0
2.0
2.0
0.3
1.0
0.5
1.0
0.1
5.71
9.94
5.49
0.00
5.71
0.00
5.71
0.00
1.0
1.0
1.5
1.0
1.0
5.25
9.11
5.71
5.71
9.22
1.0
0.3
1.0
1.0
1.0
0.8
5.71
0.00
9.22
9.95
9.46
5.71
1.0
1.0
0.3
9.11
9.32
0.00
2.0
8.61
0.5
1.0
1.0
3.0
2.0
0.8
1.0
1.0
0.00
5.25
5.71
5.49
5.49
5.71
9.22
9.11
1.0
1.0
1.0
1.0
1.5
1.0
5.71
5.25
5.71
5.71
9.46
5.71
1.0 10.23
2.0 12.16
1.0 10.80
1.0 10.51
1.0 11.15
3.8 12.16
1.0 10.51
4167 11.73518 2 2 1 1 3
1417 11.56369 3 2 1 1 0
4167 16.21336 2 4 1 1 3
1250 0.00000 2 2 1 1 0
1333 13.84142 1 3 1 2 0
4167 17.54218 3 5 1 1 0
4833 0.00000 2 4 1 1 0
2083 13.54510 4 3 1 1 3
2083 8.30992 3 2 1 2 3
7500 17.95546 3 5 1 1 0
2500 8.30992 4 2 1 2 0
2417 0.00000 2 4 1 1 0
1000 0.00000 2 2 1 1 3
4167 6.88835 2 2 1 2 2
583 0.00000 1 2 1 2 0
3167 12.01216 2 3 1 1 0
2083 10.43668 2 1 3 1 0
3750 18.66777 2 5 1 1 2
2917 11.12248 2 2 1 1 0
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5417 22.62294 2 5 1 2 3
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4833 18.66777 5 5 1 1 3
5833 0.00000 2 2 1 1 3
7083 15.56308 4 5 1 1 3
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2917 11.89398 4 3 1 1 3
5417 21.47276 2 5 1 1 2
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5667 14.66834 3 4 1 2 0
3500 13.18737 2 3 1 1 3
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4083 10.04357 3 2 1 1 0
3667 10.04357 2 2 1 1 2
2083 15.75539 4 4 1 1 3
2917 10.87733 5 2 1 1 0
5833 14.15132 3 5 1 1 3
120
1.0
0.5
1.0
1.0
0.3
0.5
2.0
1.0
0.5
0.6
1.0
2.0
0.5
2.5
1.0
1.0
1.0
1.0
4583 11.52273 2 2 1 1 0
8333 18.82234 4 5 1 1 7
1333 7.53267 3 2 2 2 3
3333 15.10423 4 4 1 1 2
600 0.00000 1 2 1 2 3
583 11.25229 3 2 1 1 0
2750 12.00137 3 3 1 2 0
3333 14.71722 2 4 1 1 3
917 12.62300 1 5 1 1 3
2500 0.00000 3 2 1 1 3
1250 0.00000 2 2 1 1 3
8.20 25000 15.29132 4 4 1 1 3
0.00 1417 0.00000 2 2 2 1 0
8.6]. 2083 10.96586 1 4 1 2 1
5.25 6000 9.64854 4 2 1 1 0
5.71 7083 17.75824 3 5 1 1 0
9.95 3083 11.42551 2 3 1 1 0
5.25 8333 18.13341 5 5 1 1 0
1.0 10.23 2792 13.10888 3 4 1 1 0
0.8 9.32 5000 11.89398 5 3 1 1 1
1.3 9.95 5167 13.18737 3 3 1 1 1
0.3 0.00 4167 20.19363 1 5 1 1 4
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0.8 9.22 6250 11.74270 2 2 1 1 3
1.0 9.32 1750 0.00000 2 2 1 1 0
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1.0 12.00 2083 10.04357 5 2 1 1 2
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1.0 5.71 2917 13.85479 2 3 1 1 1
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1.0 7.95 5417 10.77107 4 3 1 1 2
1.0 9.11 3333 13.39941 2 4 1 1 1
1.0 5.25 3750 11.88061 6 3 1 2 1
1.0 10.23 3333 13.68000 4 4 1 1 0
0.5 0.00 3167 12.32284 6 3 1 1 1
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0.5 0.00 1667 0.00000 2 2 1 1 3
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500 10.21607 5 3 1 1 1
9.32
0.00
5.71
5.71
0.00
0.00
5.49
9.11
0.00
0.00
5.71
121
1.0
1.0
1.0
0.8
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9.32
9.32
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1250 13.03578 2 3 1 1 0
1750 0.00000 2 2 1 1 0
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2.0 4.55
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0.5 0.00
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0.8 5.71 6000 15.88958 2 5 1 1 1
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1.0 9.95 3583 12.32284 5 3 1 1 2
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1.5 5.25 4083 11.42551 5 3 1 1 0
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2.0 5.49
667 12.54974 1 4 1 2 1
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1.5 9.46 7917 12.87990 4 3 1 1 3
0.5 0.00 3750 0.00000 2 2 1 1 1
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0.3 0.00
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1.0 5.71 1667 20.05562 3 5 1 2 1
1.
122
1.0 10.51 1167 13.85479 2 3 1 1 0
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4.0 5.36 1250 6.47233 5 2 3 2 0
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0.5 0.00 5833 18.78351 2 5 1 1 1
1.0
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0.8
0.5
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0.00
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1.0 11.15
1.5 10.23
3833
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4333 11.54260 2 4 1 1 3
5000 8.63694 4 2 1 1 1
2917 13.39941 4 4 1 1 2
4167 13.95973 1 5 1 1 3
2667 9.23594 2 3 1 2 4
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2667
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1.0 10.26 2167 9.67198 2 2 1 1 1
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0.1 0.00 4792 13.87486 2 3 1 1 1
1.0 7.95 7500 13.10888 3 4 1 1 1
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1.0 5.71 7917 13.94966 5 4 1 1 0
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0.5 0.00
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