Estimating the Demand for Drop-off Recycling
Sites: A Random Utility Travel Cost Approach
Shaufique Sidique1, Frank Lupi1,2, and Satish Joshi1
1 Department of Agricultural Economics, Michigan State University
University
2 Department of Fisheries and Wildlife, Michigan State University
University
Introduction
Methods
Results
Results (cont)
• DropDrop-off recycling sites are locations where people
can come to drop off a range of recyclables.
Data Collection
• InIn-person interviews were conducted at eight dropdrop-off
recycling sites in the metropolitan area of Lansing,
Michigan.
• The onon-site interviews were conducted randomly across
sites and days of week in Fall 2006.
• The response rate was 68%.
• A separate survey was conducted to obtain an estimate of
the population shares for the eight dropdrop-off sites.
• Table 1 presents the estimation results.
• The MIP results demonstrated that yardwaste
acceptance is a highly influential site attribute to
recyclers. Conversely, sites with potholes have large
negative impacts on site visitation.
• DropDrop-off recycling is one of the most widely adopted
recycling programs by the local governments in the
United States. There were 12,000 recyclable dropdrop-off
sites and 9,000 curbside programs established in this
country (USEPA, 2000).
• DropDrop-off recycling centers are less costly to operate
than curbside programs (Saphores
(Saphores et al, 2006).
• DropDrop-off recycling is faster to implement than taketakeback programs or other similar programs involving
manufacturers (Saphores
(Saphores et al, 2006).
• DropDrop-off recycling is financially attractive in areas with
low population density such in rural areas or the
countryside (Tiller, Jakus and Park, 1997).
• Despite its wide implementation and importance,
dropdrop-off recycling has not been well researched.
• Prior research has focused on curbside recycling and
unit pricing schemes, with only a couple of studies
addressing dropdrop-off recycling.
Econometric Estimation
• The dropdrop-off recycling site visitation was estimated using a
random utility travel cost model.
• Weighted Exogenous Sampling Maximum Likelihood
(WESML) (Manski
(Manski and Lerman,
Lerman, 1977) was used to derive
unbiased estimates of model parameters. The likelihood
function is presented as follows:
log L =
N
∑
n =1
Pj
S
j
⎛
⎜
log ⎜⎜
⎜⎜
⎝
i – subscript for household
Pj – proportion of population selecting site j
β – parameter estimate
tc – travel costs
⎞
⎟
exp( β tc tc ik + β q q k ) ⎟
⎟
+
exp(
β
tc
β
q
)
∑
tc
ij
q
j ⎟
⎟
j =1
⎠
j – subscript for dropdrop-off recycling site, j = 1,2,…,8
Sj – proportion of sample interviewed at site j
n – sample members, n=1,…,N
q – vector of site attributes
• To estimate the demand for dropdrop-off recycling sites in
an urban area with several substitute sites using the
random utility model (RUM).
Meridian (0.23)
Delta (0.25)
South St. (0.06)
Spartan (0.005)
• To examine the impact of different dropdrop-off recycling
site characteristics on household recycling behavior.
• To predict the changes in dropdrop-off recycling patterns
given the changes in site characteristics.
Roundtrip travel and time cost from
home to drop-off site
Parameter
Estimate
Marginal
Implicit
Prices
Total operating hours per week
0.01*
$0.09
Number of recyclables accepted
0.21*
$1.41
Number of recycling bins
-0.07
Number of commingled materials
accepted (e.g., commingled plastics)
1.41**
Number of road signs
$9.48
1.45**
$9.79
Number of instruction boards
-0.16**
-$1.08
Dummy for presence of potholes
-4.01**
-$27.01
4.42**
$29.73
N
Adj-R2
Log-likelihood
Williamston (0.03)
Figure 1: Locations & population shares for dropdrop-off recycling sites near Lansing, MI
• Acceptance of commingled materials and the number
of road signs are also regarded as important site
attributes to recyclers.
-0.15**
343
0.533
-5826.8
**Statistically significant at the 1% level
*Statistically significant at the 5% level
• The following variables have significant negative effects on
site visitation: Travel cost, Instruction Boards, and Potholes
State Rd. (0.10)
Valley Ct. (0.05)
Variable
Dummy for yardwaste acceptance
c
Granger (0.27)
Objectives
Table 1. WESML Parameter estimates for random utility
model (dependent variable = site choice).
• The following site attributes have significant positive effects
on site visitation: Operating hours, Number of recyclables
accepted, The number of commingled materials accepted,
the number of road signs, and accepting yardwaste.
yardwaste.
• Marginal implicit prices (MIP) for significant variables are
included in Table 1 to aid the interpretation of the result.
• MIPs are the ratio of a variable’s parameter estimate to the
travel cost parameter, and they ease parameter comparison
by removing the effects of the underlying model variance.
Conclusions
To maximize the use of dropdrop-off recycling, policy makers
should consider the influence of site location and site
attributes when planning and designing facilities. Our
findings demonstrate that:
• The location of a site relative to where people live
clearly affects site visitation.
• Site attributes affect dropdrop-off site visitation with a
generally positive effect for convenience attributes.
References
Manski,
Manski, C.F and S.R. Lerman.
Lerman. 1977. The estimation of choice probabilities
from choice based samples. Econometrica 45:197745:1977-1988.
Saphores,
Saphores, J.M., H. Nixon, O.A. Ogunseitan and A.A. Shapiro. 2006.
Household willingness to recycle electronic waste: An application
application to
California. Environment and Behavior 38:18338:183-208.
Tiller, K., P. Jakus and W. Park. 1997. Household willingness to pay for
drop22:310-320.
drop-off recycling. J. of Agric. and Resource Economics 22:310USEPA. 2000. Municipal Solid Waste. www.epa.gov/msw/recycle.htm
Project Support
United States Environmental Protection Agency
Society for College and University Planning
Michigan Agricultural Experiment Station
Environmental Science and Policy Program, MSU