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Resources, Conservation and Recycling 54 (2010) 864–871



                                                               Contents lists available at ScienceDirect


                                             Resources, Conservation and Recycling
                                             journal homepage: www.elsevier.com/locate/resconrec




The costs of municipal waste and recycling programs
Robert A. Bohm a , David H. Folz b , Thomas C. Kinnaman c,∗ , Michael J. Podolsky d
a
  Department of Economics, University of Tennessee, United States
b
  Department of Political Science, University of Tennessee, United States
c
  Department of Economics, Bucknell University, Lewisburg, PA 17837, United States
d
  Shumaker, Loop, and Kendrick, LLP, United States




a r t i c l e        i n f o                            a b s t r a c t

Article history:                                        This paper estimates cost functions for both municipal solid waste collection and disposal services and
Received 30 April 2009                                  curbside recycling programs. Cost data are obtained from a national survey of randomly selected munic-
Received in revised form 12 January 2010                ipalities. Results suggest, perhaps unsurprisingly, that both marginal and average costs of recycling
Accepted 12 January 2010
                                                        systems exceed those of waste collection and disposal systems. Economies of scale are estimated for
                                                        all observed quantities of waste collection and disposal. Economies of scale for recycling disappear at
Keywords:
                                                        high levels of recycling—marginal and average cost curves for recycling take on the usual U-shape. Waste
Solid waste
                                                        and recycling costs are also estimated as functions of factor costs and program attributes.
Recycling
Cost functions                                                                                                            © 2010 Elsevier B.V. All rights reserved.




1. Introduction                                                                        a few important exceptions) on understanding the costs of munic-
                                                                                       ipal waste and recycling services. Data limitations may have also
   The percentage of municipal solid waste (MSW) recycled in the                       hampered investigations into costs.
United States increased from 6.4% in 1960 to 32.5% in 2006 (US                             But both waste and recycling services are costly and require
EPA).1 The nearly 82 million tons of materials recycled in 2006                        financing from local taxpayers and/or state governments to oper-
can be attributed to 8817 municipal curbside recycling programs                        ate. This paper uses a national sample of municipal-level data to
serving 51% of the United States population, to 10,500 drop-off pro-                   estimate the costs of municipal waste and curbside recycling ser-
grams, and to 3260 yard waste composting programs (US EPA). This                       vices. Results suggest, perhaps unsurprisingly, that the costs to
growth in curbside recycling programs peaked between 1994 and                          collect, separate, process, market, and transport recyclable house-
2000 when 6108 programs were initiated. Even though waste dis-                         hold materials exceed the costs to collect and dispose the material
posal costs (known as tipping fees) increased between 1980 and                         as waste. Economies of scale are estimated across all observed
2000 as landfills satisfied new state and federal guidelines, and mar-                   waste quantities. But for recycling, economies of scale are estimated
ket prices for recycled materials spiked sharply in 1995 and 1996,                     for only low quantities—the marginal and average cost curves for
the empirical literature has found no causal link between these                        recycling take on the common U-shaped appearance.
economic variables and the municipality’s decision to implement                            Not all municipal waste and recycling programs are identi-
curbside recycling. Instead, the increase in curbside recycling has                    cal. The data also contain observations on local economic factor
been attributed to state mandates and local preferences for curb-                      costs and program attributes. Economic variables include market
side recycling services (Kinnaman, 2005). The growth in curbside                       prices for labor, capital, fuel, and tipping (disposal) fees. Program
recycling has presumably evolved independently of costs and, per-                      attributes include whether recyclable materials are separated by
haps for this reason, the economics literature is largely silent (with                 households prior to collection or later in a central facility, whether
                                                                                       recycling systems are operated by municipal governments or by
                                                                                       private firms, the size of the collection crews, the frequency of
                                                                                       collection, and a host of other specific program attributes. Results
    The authors wish to acknowledge assistance from the following individuals and      estimate how each of these variables affect the costs of municipal
organizations: Alexandra Ellis, Matt Murray, Amy Ando, John Mayo, Jean Peretz,
                                                                                       waste and recycling services.
Bruce Tonn, Marcia Prewitt, Jonathon Bricker, The University of Tennessee Waste
Management Research and Education Institute, The Joint Institute for Energy and            These results could help local policymakers estimate the costs
Environment, Knoxville, TN and the U.S. Environmental Protection Agency. None of       and benefits of increasing or decreasing the quantity of recy-
these individuals or organizations are responsible for the views expressed by the      cled materials—where the benefits of increasing recycling include
authors in this paper.                                                                 reductions in waste collection and disposal costs. Cost estimates
  ∗ Corresponding author. Tel.: +1 570 577 3465.
    E-mail address: kinnaman@bucknell.edu (T.C. Kinnaman).
                                                                                       could also be useful to a broader policymaking community inter-
  1
    These percentages include materials recovered and composted.                       ested in knowing the costs of waste and recycling services.

0921-3449/$ – see front matter © 2010 Elsevier B.V. All rights reserved.
doi:10.1016/j.resconrec.2010.01.005
R.A. Bohm et al. / Resources, Conservation and Recycling 54 (2010) 864–871                                               865


Confusion over the private marginal cost of waste collection and                 Callan and Thomas (2001) use 1996–1997 data on 101 municipal-
disposal contributed to a recent dispute published in the Comments               ities in Massachusetts and are the first to estimate economies of
section of the Journal of Economic Perspectives, where researchers               scale in curbside recycling. In a related body of literature, Criner et
debated whether the private marginal cost of recycling was $80                   al. (1995), Steuteville (1996) and Renkow and Rubin (1998) esti-
per ton or $209 per ton (Dijkgraaf et al., 2008). Results here suggest           mate the costs of municipal composting programs. Criner et al.
marginal costs vary with quantity, but achieve a minimum at about                (1995) estimate composting is worthwhile for landfill disposal fees
$75 per ton.                                                                     between $75 and $115 per ton. Renkow and Rubin (1998) examined
                                                                                 19 cases to find composting preferred to landfilling when disposal
2. The empirical literature on waste and recycling services                      costs are high.

    This paper contributes to two literatures—one estimating the                 3. Solid waste and recycling cost functions
costs of waste collection and the other estimating the costs of
recycling. The literature estimating the costs of waste disposal                     Let QiG be the quantity of solid waste collected and disposed in
services originated nearly 50 years ago. Early studies lacked appro-
                                                                                 municipality i and TCG be the total cost of collecting and disposing
                                                                                                      i
priate data and therefore employed proxy variables for the quantity
                                                                                 municipal solid waste. A flexible functional form for a simple cost
of waste collected and disposed. Proxies included the number of
                                                                                 function is given by
garbage trucks in operation (Hirsch, 1965; Kemper and Quigley,
1976; Collins and Downes, 1977; Petrovic and Jaffee, 1978) and                                                                     2
                                                                                 ln(TCG ) = ˛G + ˇ1 ln(QiG ) + ˇ2 [ln(QiG )] +
                                                                                      i
                                                                                                  G             G                         G
                                                                                                                                          i
                                                                                                                                                                  (1)
the municipal population (Kitchen, 1976). These papers estimated
economies of scale in waste collection and disposal services differed                         G
                                                                                 where ˛G , ˇ1 , and ˇ2 are parameters to be estimated and G rep-
                                                                                                      G
                                                                                                                                                i
on whether such returns to scale were decreasing or constant.                    resents all unobserved variables affecting the total cost of waste
    As waste quantity data became available following the Resource               collection and disposal with mean zero and variance (       2 )G . The

and Conservation Recovery Act of 1976, Stevens (1978), Tickner                   quadratic term in log output allows for a non-linear relationship
and McDavid (1986) and Dubin and Navarro (1988) expanded                         between quantity and both marginal and average costs of waste col-
the literature estimating waste collection and disposal costs by                 lection and disposal. This cost equation will be expanded in Section
using observed solid waste quantity measures. All find evidence of                4 below to include other exogenous variables.
economies of scale in the collection and disposal of municipal solid                The corresponding total cost function for the collection of recy-
waste. Other variables found to increase costs include an increase               clable materials is
in the frequency of waste collection, the use of backdoor collection                                                              2
service rather than curbside or alley service, and the use of munic-             ln(TCR ) = ˛R + ˇ1 ln(QiR ) + ˇ2 [ln(QiR )] +
                                                                                      i
                                                                                                  R             R                        R
                                                                                                                                         i
                                                                                                                                                                  (2)
ipal resources in collection rather than contracting with a private
                                                                                 where QiR represents the tons of materials recycled, TCR represents
                                                                                                                                            i
collector. This paper expands upon this literature by using national
                                                                                 the total cost of collecting recyclable materials, and R represents
                                                                                                                                            i
data.
                                                                                 unobserved variables thought to affect costs with mean zero and
    Empirical research on the economics of recycling can be divided
                                                                                 constant variance. This cost equation will also be expanded in Sec-
into three main areas of inquiry. The first is multi-disciplinary
                                                                                 tion 4 below to include economic and program attribute variables.
in nature and focuses on estimating household participation in
                                                                                     Even though the assumptions made for each individual equation
municipal recycling programs. Variables found to influence house-
                                                                                 satisfy the conditions necessary for ordinary least-squares esti-
hold participation rates include income (Saltzman et al., 1993;
                                                                                 mates of the coefficients to be unbiased and efficient, the equations
Feiock and West, 1996), education (DeYoung, 1990), the conve-
                                                                                 collectively exhibit serial correlation if unobserved variables affect-
nience of the recycling program (Feiock and West, 1996; Judge and
                                                                                 ing the cost of waste disposal also affect the cost of recycling. Owing
Becker, 1993; Jenkins et al., 2003), citizen involvement in program
                                                                                 to the many similarities between municipal waste and recycling
design (Folz, 1991), and social and personal standards (Hopper
                                                                                 processes, ordinary least squares of the two seemingly unrelated
and Neilson, 1991). Studies have also linked household participa-
                                                                                 equations would be inefficient if the independent variables dif-
tion rates to bag/tag unit pricing for waste disposal (Reschovsky
                                                                                 fer across the two equations. Efficient estimates of the parameters
and Stone, 1994; Hong et al., 1993; Miranda et al., 1994; Fullerton
                                                                                 in Eqs. (1) and (2) are instead obtained with the seemingly unre-
and Kinnaman, 1996; Podolsky and Spiegel, 1999; Kinnaman and
                                                                                 lated regression (SUR) model developed by Zellner (1962) using
Fullerton, 2000; Folz and Giles, 2002), and subscription-based
                                                                                 Generalized Least Squares (GLS).
volume pricing programs (Repetto et al., 1992; Reschovsky and
                                                                                     The data available to estimate costs are derived from a national
Stone, 1994; Callan and Thomas, 1997; Podolsky and Spiegel, 1999;
                                                                                 survey of municipal recycling programs conducted in 1997 and
Jenkins et al., 2003). Jakus et al. (1996) and Tiller et al. (1997)
                                                                                 described by Folz (1999a,b).2 The data gathering process first iden-
estimate costs and benefits of drop-off recycling among rural
                                                                                 tified all municipalities in the United States that maintained “at
households. The role of social norms in household recycling behav-
                                                                                 least some operational control of solid waste recycling services.”
ior is explored by Bruvoll and Nyborg (2004), Berglund (2006) and
                                                                                 From this population of 5044 municipalities, a stratified random
most recently by Halvorsen (2008).
                                                                                 sampling procedure produced a sample of 2096 municipalities.
    A second branch of the economics literature estimates the
                                                                                 Each of the municipalities in the sample received a survey, and
demand for recycled materials. Nestor (1992), for example, exam-
                                                                                 1021 survey responses were obtained (a 48.7% response rate). Con-
ines the demand for recycled newsprint by the paper industry.
                                                                                 tact with each municipality consisted of an initial mailing, two
Berglund and Soderholm (2003) compare utilization rates across
                                                                                 follow-up mailings, and telephone conversations on matters of
49 countries. Hervani (2005) considers the effect of oligopsony on
the demand for recycled newspapers.
    This research contributes most directly to the third branch of
                                                                                   2
the literature on recycling. Only two studies are known to estimate                  The survey was funded by a grant from the University of Tennessee Waste Man-
the costs of collecting recyclable materials at the curb, but both use           agement Research and Education Institute. The survey instrument was comprised
                                                                                 of 38 questions and expanded upon the survey developed by Folz (1991). Modifica-
data from a single state. Carroll (1995) initiated the literature by             tions to the initial survey were made to enhance the specificity of queries concerning
estimating recycling costs with 1992 data from 57 municipalities                 type and amount of materials collected, program costs and costs related to program
in Wisconsin. Carroll found no economies of scale in those data.                 characteristics.
866                                                 R.A. Bohm et al. / Resources, Conservation and Recycling 54 (2010) 864–871

Table 1
Quantity and cost variables.

  Variable                                                                     N        Minimum                  Maximum            Mean              Median       Std. deviation
                                            G
  Tons of solid waste collected in 1996 (Q )                                   428        40                      1,389,000            36,610              7312      110,968
  Total cost (in $s) to collect and dispose solid waste in 1996 (TCG )         428      5407                     92,913,000         2,290,700            513,763   6,882,700
  Tons of recyclable materials collected in 1996 (QR )                         428         3                         70,000             3232               1200        6808
  Total cost (in $s) of curbside recycling program in 1996 (TCR )              428       300                      6,230,000           371,060            110,000     782,271


Table 2                                                                                     Table 3
Waste collection and disposal costs (dependent variable = ln(TCG )).                        Curbside recycling costs (dependent variable = ln(TCR )).

  Variable             Coefficient               Standard error           Significance           Variable                Coefficient               Standard error      Significance

  Constant             ˛G = 4.4576              0.8080                   1% level              Constant                ˛R = 7.2926              0.4705              1% level
  ln(QiG )             ˇ1 = 1.1302
                        G
                                                0.1808                   1% level              ln(QiR )                ˇ1 = 0.3736
                                                                                                                        R
                                                                                                                                                0.1453              1% level
  (ln(QiG ))2          ˇ2 = −0.0169
                        G
                                                0.0099                   5% level              (ln(QiR ))2             ˇ2 = 0.0330
                                                                                                                        R
                                                                                                                                                0.0111              1% level
                2                                                                                            2
  N = 428; R = 0.7154                                                                          N = 428; R = 0.6378



clarification. Although these data have been used by Folz (1999a,b,                          decrease to $59.70 and $40.54 for the municipality collecting the
2004), Folz and Giles (2002) and Folz et al. (2005), the data have                          median (7312 tons) and mean (36,610) quantity of waste. The esti-
not been used by economists to estimate costs functions.                                    mated marginal cost of collecting and disposing the 1,389,000th
    Each variable defined above is defined and summarized in                                  ton of waste (the maximum in the sample) is $12.19.
Table 1. Perhaps due to imperfect information on the part of the                                These estimated cost curves are long run in the sense that they
survey responders, not all questions were answered by the 1028                              are obtained by comparing the total costs of municipalities with
responding communities. Only 428 communities reported total                                 varying levels of physical capital and administrative overhead. The
costs and quantities for both solid waste and recycling. If the rea-                        costs to set up a solid waste collection system necessary to col-
sons for imperfect information are uncorrelated with observed                               lect and dispose just 1 ton of waste (roughly two 20-pound bags
quantities and costs, then using the smaller sample will not intro-                         of garbage per week) are small and perhaps consist only of the
duce bias to the estimated coefficients.                                                     cost for the occasional use of a pick-up truck. Costs increase with
    The sample is comprised of a broad range of municipality sizes.                         expanding waste collection and disposal services due to purchases
As is summarized in Table 1, the quantity of municipal sold waste                           of additional collection trucks, establishing transfer stations, paying
collected and disposed or incinerated (QG ) varies between 40 tons                          disposal (tipping) fees and creating the necessary administrative
and 1,389,000 tons. For TCG , each municipality reported “the total                         structure.
collection and disposal cost for all non-recycled municipal solid                               As illustrated in Fig. 1, economies of scale in waste collection
wastes”. These annual costs vary in the sample between $5407 and                            and disposal are estimated for all observed quantities of waste in
$92,913,000. That the median of all variables in Table 1 are less than                      the sample. Both marginal and average total costs decrease with
their mean suggests the sample is comprised of a large number of                            quantity. Increasing waste quantities may allow for the division of
small municipalities relative to the number of large cities—perhaps                         factor resources into specialized tasks. Stevens (1978) and Tickner
consistent with the overall distribution of municipal sizes in the                          and McDavid (1986) also find evidence of economies of scale in the
United States.                                                                              provision of waste collection and disposal services.3 In contrast,
    GLS estimates of the parameters in (1) are provided in Table 2.                         Callan and Thomas (2001) estimate constant returns to scale in
All estimated coefficients are statistically significant at the 5% level.                     waste collection and disposal with constant marginal cost of $77.82
The value of R2 (0.7154) suggests a large portion of the variation in                       per ton.
cost data can be explained by the quantity variables—perhaps not                                Variables used to estimate the recycling cost equation (Eq. (2))
surprising given the large range of quantity and cost data observed                         are also defined and summarized in Table 1. According to the
in the sample. These results can be used to estimate the marginal                           survey, the recycling quantity variable (QR ) represents the total
and average total cost curves for waste collection and disposal.                            tons of “non-composted, recyclable materials recovered or col-
These curves are illustrated in Fig. 1. The marginal cost of collect-                       lected as part of the local recycling program.” This variable includes
ing and disposing the 40th ton of solid waste (the minimum in the                           both materials collected at the curb and materials recovered at
sample) is estimated at $111.40. This marginal cost is estimated to                         drop-off facilities. Recycling quantities varied between 3 tons and
                                                                                            70,000 tons across the municipalities in the sample with a mean of
                                                                                            3232 tons. The total cost of recycling (TCR ) represents “all direct
                                                                                                                                         i
                                                                                            and indirect costs and any payments made to contractors” and
                                                                                            “excludes revenue earned from the sale of recyclable materials.”
                                                                                            The sample captured a broad range of program sizes, as recycling
                                                                                            costs varied between $300 and $6,230,000 across the sample with
                                                                                            a mean of $371,060.
                                                                                                GLS estimation results for the recycling equation are provided in
                                                                                            Table 3. All three parameters are significant at the 1% level. Marginal
                                                                                            and average total costs curves based upon the GLS estimates of the
                                                                                            parameters from Eq. (2) are illustrated in Fig. 2. The marginal cost of
                                                                                            recycling the 3rd ton of material (the minimum amount recycled in



                                                                                              3
                                                                                                Stevens (1978) finds constant returns to scale among municipalities with pop-
             Fig. 1. Estimated costs of solid waste collection and disposal.                ulations in excess of 50,000.
R.A. Bohm et al. / Resources, Conservation and Recycling 54 (2010) 864–871                                    867


                                                                                       cost of collecting and disposing solid waste or operating a curbside
                                                                                       recycling program. Let Zi1 , Zi2 , . . ., ZiK denote variables potentially
                                                                                       influencing the marginal cost of operating waste or recycling sys-
                                                                                       tems in municipality i. The total cost function in Eq. (1) or (2) can
                                                                                       be expanded to

                                                                                       ln(TCi ) = ˛ + ˇ1 ln(Qi ) + ˇ2 [ln(Qi )]2 +   1 Zi1   +   2 Zi2

                                                                                                   + ... +     K ZiK   +    i                                (3)


                                                                                           Each determines how a change in each of the corresponding Z
                                                                                       variables affects costs. Once again the seemingly unrelated regres-
                                                                                       sion (SUR) econometric model is used to estimate the coefficients
                                                                                       in (3).
                Fig. 2. Estimated costs of curbside recycling.                             Economic variables expected to affect the costs of municipal
                                                                                       waste and recycling services include costs for labor, capital, and
the sample) is estimated at $342.80. The marginal costs of recycling                   fuel. Because the survey does not contain detailed budget infor-
the 1200th ton and the 3232nd ton – the median and mean in the                         mation, data on these variables are derived from outside sources.
sample – are estimated at $76.53 and $72.81, respectively. As illus-                   WAGE is the 1996 average hourly earnings of production workers
trated in Fig. 2, the marginal cost of recycling initially decreases at                in each Bureau of Labor Statistics labor market area. INTEREST rep-
low quantities but eventually increases. Factor specialization and                     resents the municipality’s opportunity cost of acquiring physical
utilization reduces marginal costs at low quantity levels, but dimin-                  capital based on Moody’s full faith and credit bond rating. FUEL is
ishing returns to recycling factors may cause marginal costs to then                   the prevailing local price of regular gasoline in 1996, obtained from
rise at high quantity levels. The marginal cost of recycling reaches a                 United States Department of Energy. In terms of program attributes,
minimum of $72.57 at the 4600th ton recycled. Average total costs                      all variables obtained from the survey that might potentially affect
are minimized at a value of $75.18 at the 13,200th ton recycled.                       the cost of waste collection and disposal and/or the cost of recycling
Examining the raw data, municipalities in the sample that recycle                      are included in the model. All economic and program attribute vari-
an amount close to 13,200 tons of materials have populations of                        ables are listed in Table 4. Descriptive statistics are summarized
about 80,000 persons.                                                                  in Table 5. Also in Table 5 is an indication of which cost model
    These estimates suggest economies of scale are present in the                      the variable belongs to (garbage—G, and/or recycling—R). Because
curbside collection only for quantity levels below 13,200 tons per                     the survey was designed to gather data primarily on recycling pro-
year. Callan and Thomas (2001) also found economies of scale for                       grams, the amount of variables available to the recycling equation
recycling using a sample with an annual mean of 11,098 tons of                         far exceeds the number available to waste. Due to missing value
recyclable materials. Callan and Thomas (2001) also estimate the                       in the new variables, the number of usable observations decreased
marginal cost of recycling at $13.55 per ton—in stark difference                       from 428 to 284.
to the range of estimates reported above. Carroll (1995) estimated
constant returns to scale in recycling using a sample of 57 munici-
                                                                                       4.1. The costs of waste collection and disposal
palities averaging 4468 tons per year.
    These results are useful to any municipality contemplating
                                                                                           SUR estimates of the coefficients for the waste equation are
an expansion or contraction of their recycling program. Consider
                                                                                       listed in Table 6. The coefficient on the log of quantity (ˇ1 ) and
a municipality collecting and disposing the median quantity of
                                                                                       its squared term (ˇ2 ) do not change substantially by adding the
waste (7312 tons) and recycling the median quantity of materials
                                                                                       extra variables. The coefficient on the factor cost for fuel is positive
(1200 tons). By recycling one additional ton of waste that would
                                                                                       and significant in the waste equation. A 1% increase in the price
have otherwise been disposed, the municipality would save an esti-
                                                                                       of gasoline is estimated to increase waste collection and disposal
mated $59.70 in collection and disposal costs but incur $76.53 in
                                                                                       costs by 1.653%. Labor costs (WAGE) and capital costs (INTEREST)
added recycling costs. This extra cost is reduced by any revenues
                                                                                       are also estimated to increase waste collection and disposal costs,
gained from selling the recyclable materials. The municipality can
                                                                                       but not with statistical significance. A 1% increase in disposal costs
then compare the expected change in costs with the expected gain
                                                                                       (TIPFEE) is estimated to increase costs by 0.17%.
in environmental quality attributable to the reduction in waste
                                                                                           20% of the municipalities in the sample levy per-bag user fees to
disposal or increase in recycling.
                                                                                       finance a portion of solid waste collection and disposal costs. Such
    Household source reduction efforts presumably complement
                                                                                       programs require municipal resources to print and distribute spe-
recycling practices. Households that increase recycling may simul-
                                                                                       cial tags, stickers, or bags required for households to dispose of each
taneously seek ways to reduce the use of shopping bags and
                                                                                       bag of waste. Holding the quantity of waste constant, such efforts
beverage containers. If source reduction indeed accompanies recy-
                                                                                       are estimated to increase the overall costs of waste collection and
cling, then the 1 ton increase in recycling discussed above could
                                                                                       disposal by 18.1%, but this estimate is not statistically significant.
lead to more than a 1 ton decrease in waste quantity. If a munic-
                                                                                           Collecting waste on the same day as recyclables (SAMEDAY) is
ipality that increases recycling by 1 ton experiences a 1.28 ton
                                                                                       estimated to decrease the marginal cost of waste collection and dis-
reduction in solid waste due to source reduction, then the marginal
                                                                                       posal by an insignificant 12.6%. Callan and Thomas (2001) suggest
cost of the additional recycling ($76.53) would equal the marginal
                                                                                       that municipalities share resources across the two collection pro-
benefit of lower waste collection and disposal costs (also $76.53).
                                                                                       grams. Finally, DENSITY, obtained from the U.S. Census (City and
4. Specific determinants of the costs of curbside recycling                             County Data Book) represents the density of the population in per-
and waste collection                                                                   sons per square mile. A 1% increase in the population density is
                                                                                       estimated to increase waste costs by 0.092%. Waste generated in
    Cost functions for both waste and recycling can be expanded                        high-density municipalities may incur high costs to transport waste
to include other variables thought to affect the long-run marginal                     to remote landfills for disposal.
868                                                    R.A. Bohm et al. / Resources, Conservation and Recycling 54 (2010) 864–871

                    Table 4
                    Variables affecting the costs of solid waste or recycling.

                      Factor costs
                        WAGE                                           Average hourly earnings of production workers in 1996 (Bureau of Labor Statistics)
                        INTEREST                                       Opportunity cost of capital based upon Moody’s bond rating
                        FUEL                                           Price of regular gas
                        TIPFEE                                         Cost of waste disposal ($ per ton)

                      Scope of services
                        MFAM                                           Recyclables are collected from multi-family dwellings
                        FREQUENCY                                      Number of curbside recycling collections per month
                        STAFFDROP                                      Community offers staffed facility for households to drop-off recyclable materials
                        UNSTAFFDROP                                    Community offers un-staffed facility for households to drop-off recyclable materials
                        PARTICIPATE                                    The percentage of eligible households participating in curbside recycling

                      Collection practices
                        BINSTOHHS                                      Recycling containers provided to households
                        COLLSEP                                        Recyclable materials are separated by collectors
                        CENTSEP                                        Recycled materials are separated in a centralized facility
                        VARFEE                                         Municipality levies a variable fee for waste collection
                        CREW                                           Size of collection crew for recyclable materials
                        SAMEDAY                                        Recyclables collected on the same day as solid waste collection
                        CITYCOLL                                       City crews collect recyclable materials using city trucks
                        CITYPROC                                       City owned and operated materials recovery facility processes recyclables
                        RECTRUCK                                       Specialized recycling truck used to collect materials

                      Exogenous variables
                        DENSITY                                        Population density of municipality (persons per square mile)
                        STMANDATE                                      State mandate or recycling goal is very significant for continuation of recycling
                        VINTAGE                                        Number of years a community recycling program has been in operation (as of 1996)



4.2. The costs of recycling                                                                    But the cost of fuel was significant and positive in the waste equa-
                                                                                               tion. Perhaps waste is transported over long distances to reach
    Variables expected to affect the costs of operating a curbside                             remote waste disposal facilities. The distance to recycling facilities
recycling program are listed in Table 4, and descriptive statistics                            may be comparatively small.
for each variable are summarized in Table 5. Results suggest a 1%                                  Several program-specific attributes could potentially affect
increase in interest rates increases recycling costs by an estimated                           recycling costs. The first is a binary variable that indicates whether
21.903%. That INTEREST, the cost of capital, is positive in the recy-                          multi-family residents are included in the curbside recycling pro-
cling equation but insignificant in the waste equation could suggest                            gram. As summarized in Table 5, 79% of municipal recycling
recycling systems are more capital intensive than waste systems.                               programs collect materials from multi-family residences such
Perhaps trucks are the only capital necessary to most waste collec-                            as apartment buildings (MFAM = 1). Residents in multi-family
tion and disposal systems (land being the other important input).                              dwellings might individually transport their recycled materials to
Capitalized processing facilities in addition to collection trucks may                         a single on-premise depot, making municipal collection from these
be necessary inputs to recycling systems.                                                      households easier than for single-family dwellings. But this coeffi-
    Labor and fuel costs are not significant predictors of                                      cient is not statistically different from zero.
recycling costs. Wages were also insignificant in the waste                                         The frequency of curbside service (FREQUENCY) varied in the
equation—apparently neither of these systems is labor intensive.                               sample from as few as one collection per month to as many as two


Table 5
Descriptive statistics of variables affecting costs.

                                     N                     Model                    Minimum                    Maximum                   Mean                 Std. deviation

  WAGE                               284                   G and R                  9.80                          18.32                   12.84                 1.62
  INTEREST                           284                   G and R                  5.10                           5.34                    5.24                 0.04
  FUEL                               284                   G and R                  1.07                           1.56                    1.26                 0.10
  TIPFEE                             284                   G                        0                            110                      38.86                18.68

  MFAM                               284                   R                        0                              1                       0.79                 0.41
  FREQUENCY                          284                   R                        1                              8                       3.44                 1.01
  STAFFDROP                          284                   R                        0                              1                       0.30                 0.46
  UNSTAFFDROP                        284                   R                        0                              1                       0.31                 0.46
  PARTICIPATE                        284                   R                        1                            100                      70.98                22.41

  BINSTOHHS                          284                   R                        0                              1                       0.81                 0.40
  COLLSEP                            284                   R                        0                              1                       0.32                 0.47
  CENTSEP                            284                   R                        0                              1                       0.44                 0.50
  VARFEE                             284                   G                        0                              1                       0.20                 0.40
  CREW                               284                   R                        1                              5                       1.63                  .76
  SAMEDAY                            284                   G and R                  0                              1                       0.80                  .40
  CITYCOLL                           284                   R                        0                              1                       0.44                  .50
  CITYPROC                           284                   R                        0                              1                       0.13                 0.34
  RECTRUCK                           284                   R                        0                              1                       0.43                  .50

  DENSITY                            284                   G and R                  8                          4397                      944.31               751.05
  STMANDATE                          284                   R                        0                             1                        0.48                 0.50
  VINTAGE                            284                   R                        1                            28                        8.16                 4.43
R.A. Bohm et al. / Resources, Conservation and Recycling 54 (2010) 864–871                                                   869

Table 6                                                                                    collectors separating recyclable materials may be welfare improv-
The costs of waste collection (dependent variable = ln(TCG )).
                                                                                           ing.
  Variable               Coefficient           Standard error          Significance              About 44% of the sample also collects co-mingled recyclables
  CONSTANT                −5.944              9.522                   –                    from households and then separates the materials at a centralized
  ln(QG )                  1.098              0.228                   1% level             facility (CENTSEP = 1). The municipality must allocate resources to
  [ln (QG )]2             −0.016              0.012                   –                    operate the separating facility, but this practice could ease the col-
  ln(WAGE)                  0.225             0.271                   –                    lection process as collection crews simply dump all co-mingled
  ln(INTEREST)              4.057             5.745                   –                    recyclable materials at once into a single truck for subsequent sep-
  ln(FUEL)                  1.653             0.669                   5% level             aration. Results suggest the costs of recycling decreases by 36.3%
  ln(TIPFEE)                0.170             0.086                   5% level
                                                                                           per ton. This result suggests centralized separating facilities reduce
  VARFEE                   0.166              0.116                   –                    costs, especially if requiring households to pre-separate recycled
  SAMEDAY                 −0.119              0.119                   –                    materials is costly to households.5
  ln(DENSITY)              0.092              0.051                   10% level
                                                                                               Municipalities vary with respect to the size of crews sent out
  N = 284; R2 = 0.775                                                                      to collect recyclable materials from households (CREW). As few as
                                                                                           one and as many as four workers comprise a collection crew in the
                                                                                           sample. The average crew size is 1.63 members. The marginal cost
collections per week. More frequent service holding constant the
                                                                                           of recycling can be expected to rise with the additional labor costs
quantity collected would presumably increase costs. The estimated
                                                                                           incurred with larger crews and fall as additional crew members
coefficient is positive, as expected (costs are estimated to increase
                                                                                           allow for specialization and the division of labor in collection. The
by 3.6% with each additional pick-up per month), but insignifi-
                                                                                           data suggest changes in the crew size do not affect marginal costs,
cant. Callan and Thomas (2001) estimate average costs increase
                                                                                           perhaps suggesting that these two forces negate each other.
with frequency, while Carroll (1995) finds no connection between
                                                                                               79% of municipalities in the sample collect recyclable materials
frequency of collection and total costs.
                                                                                           on the same day as regular waste collection (SAMEDAY = 1), perhaps
    As summarized in Table 5, 30% of responding communi-
                                                                                           to make recycling more convenient to households. The marginal
ties reported a staffed drop-off facility (STAFFDROP = 1) and 31%
                                                                                           cost of collecting recyclable materials does not statistically change
reported an un-staffed drop-off facility (UNSTAFFDROP = 1). The
                                                                                           for municipalities with same-day service.
model controls for the presence of either a staffed or un-staffed
                                                                                               CITYCOLL is a binary variable that indicates whether curbside
drop-off facility when estimating costs because the quantity vari-
                                                                                           recycling is conducted by municipal employees or by a private
able, QR , includes all materials recovered via curbside collection
                                                                                           contracted company. 44% of municipalities in the sample utilize
and at drop-off facilities. The marginal cost is expected to fall to the
                                                                                           municipal employees. The marginal cost of recycling is estimated
extent the municipality’s recycling costs decreases when house-
                                                                                           to rise by 26.6% with municipal employees relative to a private
holds rather than collection crews transport materials to recycling
                                                                                           contracted collector. Carroll (1995) also found costs increase with
facilities. Results do not confirm this prediction. Neither variable is
                                                                                           municipal provision, but Callan and Thomas (2001) find no effect.
statistically different from zero.
                                                                                           Kemper and Quigley (1976) estimate that competitive markets are
    The data also include the estimated participation rate among eli-
                                                                                           25–36% more expensive than a single collector, and that contract
gible households in each municipality (PARTICIPATE).4 Holding the
                                                                                           or franchise agreements reduce costs over municipal collections by
quantity of material collected constant, an increase in the participa-
                                                                                           another 13–30% (depending on the level of service). Stevens (1978)
tion rate might increase the cost of collecting material—collecting
                                                                                           estimates that the contract or franchise agreements reduce costs by
a lot of material from a few households is presumably cheaper than
                                                                                           26–48% below that of a competitive private market and by 27–37%
collecting a little material from many households. These data con-
                                                                                           below that of municipal provision (for cities with populations over
firm the suggestion, every 1% increase in the reported participation
                                                                                           50,000). Costs could increase with public provisions if wage pay-
rate increases costs by 0.50%.
                                                                                           ments to municipal workers exceed those to workers of the private
    Just over 81% of the municipalities in the sample provide recy-
                                                                                           marketplace or if the municipality lacks competitive pressure to
cling bins, bags, or carts at no costs to participating residents
                                                                                           minimize costs.
(BINSSTOHH = 1). Costs of recycling rise with the costs of purchas-
                                                                                               Similarly, 14% of municipalities in the sample feature a munic-
ing, storing, and delivering these bins to households and fall to the
                                                                                           ipally run processing facility (CITYPROC = 1). Other municipalities
extent these bins make collection more efficient. Results suggest
                                                                                           rely upon private companies, a non-profit association, or other gov-
the two affects could offset each other—the overall affect of provid-
                                                                                           ernmental unit to process the recycled materials. A city-operated
ing bins to households on recycling costs is not statistically different
                                                                                           processing facility is estimated here to have no statistical affect on
from zero.
                                                                                           costs.
    Roughly 32% of municipalities in the sample allow households
                                                                                               Another variable that could affect the long-run marginal cost of
to co-mingle all recyclable materials in a single container. The
                                                                                           recycling is the type of vehicle used to collect materials at the curb.
collectors then separate the materials at the time of collection
                                                                                           Low-cost options could include ordinary refuse trucks, pick-up
(COLLSEP = 1). The marginal cost of recycling increases to the extent
                                                                                           trucks, or dump trucks. Alternatively, a municipality could pur-
these separating services increase the time necessary to complete
                                                                                           chase a designated recycling truck with individual compartments
a collection route. But the data suggest the marginal cost is unaf-
                                                                                           engineered for each material (RECTRUCK = 1). 43% of the sample
fected by this option. Collection crews do not appear advantaged
                                                                                           made such a choice. The presence of such a truck is estimated to
by the pre-separated materials, as they may still have to deposit
                                                                                           increase recycling costs by 6.4%, but this variable is not statistically
each material into separate sections of the recycling truck. Thus, if
                                                                                           significant.
separating materials are costly to households, then a program with


   4                                                                                         5
     The survey asked municipalities how they estimated participation rates. Each              The remaining option to COLLSEP and CENTSEP is to require households to sepa-
municipality could select “all that apply” from a list of 7 options. Only 10% uti-         rate materials into different recycling bins or bags. Because this variable is omitted to
lize actual sign-ups or subscriptions to recycling programs to observe participation.      avoid colinearity with the constant term, the coefficients on COLLSEP and CENTSEP
About half of the municipalities indicated their estimate was based upon subjective        are interpreted as cost differences relative to having households pre-separate the
“field observations.”                                                                       material.
870                                                R.A. Bohm et al. / Resources, Conservation and Recycling 54 (2010) 864–871

Table 7                                                                                    centralized separation rather than curbside separation enjoy lower
The costs of recycling (dependent variable = ln(TCR )).
                                                                                           costs. These results could prove useful to municipal officials inter-
  Variable                Coefficient           Standard error         Significance          ested in implementing changes in these approaches to recycling
  CONSTANT                −24.775              11.480                 5% level             (Table 7).
  ln(QR )                   0.321               0.195                 10% level                Note, finally, that results as depicted in Table 3 and Fig. 2 might
  ln(QR )2                  0.033               0.014                 5% level             mistakenly be interpreted to suggest that all municipalities can
  ln(WAGE)                 −0.483               0.332                 –                    minimize average recycling costs by selecting the quantity that
  ln(INTEREST)             21.903               6.847                 1% level             minimizes long-run average total costs (13,200 tons). But the total
  ln(FUEL)                 −0.502               0.868                 –                    quantity of waste material available for recycling (G + R) is largely
  MFAM                      0.025               0.138                 –                    determined by local populations and income levels, factors that
  FREQUENCY                 0.035               0.057                 –                    are exogenous to the municipal government. A very small munic-
  STAFFDROP                −0.051               0.127                 –                    ipality, for example, is obviously unable to achieve this minimum
  UNSTAFFDROP              −0.009               0.123                 –
                                                                                           average cost if the total quantity of household material available for
  PARTICIPATE               0.005               0.003                 10% level
                                                                                           recycling (G + R) is less than 13,200. These results could prove use-
  BINSTOHHS                 0.205               0.146                 –                    ful to municipalities interested in estimating the marginal cost of
  COLLSEP                  −0.057               0.145                 –
  CENTSEP                  −0.310               0.132                 5% level
                                                                                           increasing or decreasing the quantity of materials recycled. Results
  CREW                      0.092               0.077                 –                    could also inform adjoining municipalities that are interested in the
  SAMEDAY                   0.201               0.147                 –                    cost implications of combing recycling programs.
  CITYCOLL                  0.236               0.134                 10% level
  CITYPROC                  0.059               0.180                 –
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  RECTRUCK                  0.062               0.114                 –

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The Costs Of Municipal Waste

  • 1. Resources, Conservation and Recycling 54 (2010) 864–871 Contents lists available at ScienceDirect Resources, Conservation and Recycling journal homepage: www.elsevier.com/locate/resconrec The costs of municipal waste and recycling programs Robert A. Bohm a , David H. Folz b , Thomas C. Kinnaman c,∗ , Michael J. Podolsky d a Department of Economics, University of Tennessee, United States b Department of Political Science, University of Tennessee, United States c Department of Economics, Bucknell University, Lewisburg, PA 17837, United States d Shumaker, Loop, and Kendrick, LLP, United States a r t i c l e i n f o a b s t r a c t Article history: This paper estimates cost functions for both municipal solid waste collection and disposal services and Received 30 April 2009 curbside recycling programs. Cost data are obtained from a national survey of randomly selected munic- Received in revised form 12 January 2010 ipalities. Results suggest, perhaps unsurprisingly, that both marginal and average costs of recycling Accepted 12 January 2010 systems exceed those of waste collection and disposal systems. Economies of scale are estimated for all observed quantities of waste collection and disposal. Economies of scale for recycling disappear at Keywords: high levels of recycling—marginal and average cost curves for recycling take on the usual U-shape. Waste Solid waste and recycling costs are also estimated as functions of factor costs and program attributes. Recycling Cost functions © 2010 Elsevier B.V. All rights reserved. 1. Introduction a few important exceptions) on understanding the costs of munic- ipal waste and recycling services. Data limitations may have also The percentage of municipal solid waste (MSW) recycled in the hampered investigations into costs. United States increased from 6.4% in 1960 to 32.5% in 2006 (US But both waste and recycling services are costly and require EPA).1 The nearly 82 million tons of materials recycled in 2006 financing from local taxpayers and/or state governments to oper- can be attributed to 8817 municipal curbside recycling programs ate. This paper uses a national sample of municipal-level data to serving 51% of the United States population, to 10,500 drop-off pro- estimate the costs of municipal waste and curbside recycling ser- grams, and to 3260 yard waste composting programs (US EPA). This vices. Results suggest, perhaps unsurprisingly, that the costs to growth in curbside recycling programs peaked between 1994 and collect, separate, process, market, and transport recyclable house- 2000 when 6108 programs were initiated. Even though waste dis- hold materials exceed the costs to collect and dispose the material posal costs (known as tipping fees) increased between 1980 and as waste. Economies of scale are estimated across all observed 2000 as landfills satisfied new state and federal guidelines, and mar- waste quantities. But for recycling, economies of scale are estimated ket prices for recycled materials spiked sharply in 1995 and 1996, for only low quantities—the marginal and average cost curves for the empirical literature has found no causal link between these recycling take on the common U-shaped appearance. economic variables and the municipality’s decision to implement Not all municipal waste and recycling programs are identi- curbside recycling. Instead, the increase in curbside recycling has cal. The data also contain observations on local economic factor been attributed to state mandates and local preferences for curb- costs and program attributes. Economic variables include market side recycling services (Kinnaman, 2005). The growth in curbside prices for labor, capital, fuel, and tipping (disposal) fees. Program recycling has presumably evolved independently of costs and, per- attributes include whether recyclable materials are separated by haps for this reason, the economics literature is largely silent (with households prior to collection or later in a central facility, whether recycling systems are operated by municipal governments or by private firms, the size of the collection crews, the frequency of collection, and a host of other specific program attributes. Results The authors wish to acknowledge assistance from the following individuals and estimate how each of these variables affect the costs of municipal organizations: Alexandra Ellis, Matt Murray, Amy Ando, John Mayo, Jean Peretz, waste and recycling services. Bruce Tonn, Marcia Prewitt, Jonathon Bricker, The University of Tennessee Waste Management Research and Education Institute, The Joint Institute for Energy and These results could help local policymakers estimate the costs Environment, Knoxville, TN and the U.S. Environmental Protection Agency. None of and benefits of increasing or decreasing the quantity of recy- these individuals or organizations are responsible for the views expressed by the cled materials—where the benefits of increasing recycling include authors in this paper. reductions in waste collection and disposal costs. Cost estimates ∗ Corresponding author. Tel.: +1 570 577 3465. E-mail address: kinnaman@bucknell.edu (T.C. Kinnaman). could also be useful to a broader policymaking community inter- 1 These percentages include materials recovered and composted. ested in knowing the costs of waste and recycling services. 0921-3449/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.resconrec.2010.01.005
  • 2. R.A. Bohm et al. / Resources, Conservation and Recycling 54 (2010) 864–871 865 Confusion over the private marginal cost of waste collection and Callan and Thomas (2001) use 1996–1997 data on 101 municipal- disposal contributed to a recent dispute published in the Comments ities in Massachusetts and are the first to estimate economies of section of the Journal of Economic Perspectives, where researchers scale in curbside recycling. In a related body of literature, Criner et debated whether the private marginal cost of recycling was $80 al. (1995), Steuteville (1996) and Renkow and Rubin (1998) esti- per ton or $209 per ton (Dijkgraaf et al., 2008). Results here suggest mate the costs of municipal composting programs. Criner et al. marginal costs vary with quantity, but achieve a minimum at about (1995) estimate composting is worthwhile for landfill disposal fees $75 per ton. between $75 and $115 per ton. Renkow and Rubin (1998) examined 19 cases to find composting preferred to landfilling when disposal 2. The empirical literature on waste and recycling services costs are high. This paper contributes to two literatures—one estimating the 3. Solid waste and recycling cost functions costs of waste collection and the other estimating the costs of recycling. The literature estimating the costs of waste disposal Let QiG be the quantity of solid waste collected and disposed in services originated nearly 50 years ago. Early studies lacked appro- municipality i and TCG be the total cost of collecting and disposing i priate data and therefore employed proxy variables for the quantity municipal solid waste. A flexible functional form for a simple cost of waste collected and disposed. Proxies included the number of function is given by garbage trucks in operation (Hirsch, 1965; Kemper and Quigley, 1976; Collins and Downes, 1977; Petrovic and Jaffee, 1978) and 2 ln(TCG ) = ˛G + ˇ1 ln(QiG ) + ˇ2 [ln(QiG )] + i G G G i (1) the municipal population (Kitchen, 1976). These papers estimated economies of scale in waste collection and disposal services differed G where ˛G , ˇ1 , and ˇ2 are parameters to be estimated and G rep- G i on whether such returns to scale were decreasing or constant. resents all unobserved variables affecting the total cost of waste As waste quantity data became available following the Resource collection and disposal with mean zero and variance ( 2 )G . The and Conservation Recovery Act of 1976, Stevens (1978), Tickner quadratic term in log output allows for a non-linear relationship and McDavid (1986) and Dubin and Navarro (1988) expanded between quantity and both marginal and average costs of waste col- the literature estimating waste collection and disposal costs by lection and disposal. This cost equation will be expanded in Section using observed solid waste quantity measures. All find evidence of 4 below to include other exogenous variables. economies of scale in the collection and disposal of municipal solid The corresponding total cost function for the collection of recy- waste. Other variables found to increase costs include an increase clable materials is in the frequency of waste collection, the use of backdoor collection 2 service rather than curbside or alley service, and the use of munic- ln(TCR ) = ˛R + ˇ1 ln(QiR ) + ˇ2 [ln(QiR )] + i R R R i (2) ipal resources in collection rather than contracting with a private where QiR represents the tons of materials recycled, TCR represents i collector. This paper expands upon this literature by using national the total cost of collecting recyclable materials, and R represents i data. unobserved variables thought to affect costs with mean zero and Empirical research on the economics of recycling can be divided constant variance. This cost equation will also be expanded in Sec- into three main areas of inquiry. The first is multi-disciplinary tion 4 below to include economic and program attribute variables. in nature and focuses on estimating household participation in Even though the assumptions made for each individual equation municipal recycling programs. Variables found to influence house- satisfy the conditions necessary for ordinary least-squares esti- hold participation rates include income (Saltzman et al., 1993; mates of the coefficients to be unbiased and efficient, the equations Feiock and West, 1996), education (DeYoung, 1990), the conve- collectively exhibit serial correlation if unobserved variables affect- nience of the recycling program (Feiock and West, 1996; Judge and ing the cost of waste disposal also affect the cost of recycling. Owing Becker, 1993; Jenkins et al., 2003), citizen involvement in program to the many similarities between municipal waste and recycling design (Folz, 1991), and social and personal standards (Hopper processes, ordinary least squares of the two seemingly unrelated and Neilson, 1991). Studies have also linked household participa- equations would be inefficient if the independent variables dif- tion rates to bag/tag unit pricing for waste disposal (Reschovsky fer across the two equations. Efficient estimates of the parameters and Stone, 1994; Hong et al., 1993; Miranda et al., 1994; Fullerton in Eqs. (1) and (2) are instead obtained with the seemingly unre- and Kinnaman, 1996; Podolsky and Spiegel, 1999; Kinnaman and lated regression (SUR) model developed by Zellner (1962) using Fullerton, 2000; Folz and Giles, 2002), and subscription-based Generalized Least Squares (GLS). volume pricing programs (Repetto et al., 1992; Reschovsky and The data available to estimate costs are derived from a national Stone, 1994; Callan and Thomas, 1997; Podolsky and Spiegel, 1999; survey of municipal recycling programs conducted in 1997 and Jenkins et al., 2003). Jakus et al. (1996) and Tiller et al. (1997) described by Folz (1999a,b).2 The data gathering process first iden- estimate costs and benefits of drop-off recycling among rural tified all municipalities in the United States that maintained “at households. The role of social norms in household recycling behav- least some operational control of solid waste recycling services.” ior is explored by Bruvoll and Nyborg (2004), Berglund (2006) and From this population of 5044 municipalities, a stratified random most recently by Halvorsen (2008). sampling procedure produced a sample of 2096 municipalities. A second branch of the economics literature estimates the Each of the municipalities in the sample received a survey, and demand for recycled materials. Nestor (1992), for example, exam- 1021 survey responses were obtained (a 48.7% response rate). Con- ines the demand for recycled newsprint by the paper industry. tact with each municipality consisted of an initial mailing, two Berglund and Soderholm (2003) compare utilization rates across follow-up mailings, and telephone conversations on matters of 49 countries. Hervani (2005) considers the effect of oligopsony on the demand for recycled newspapers. This research contributes most directly to the third branch of 2 the literature on recycling. Only two studies are known to estimate The survey was funded by a grant from the University of Tennessee Waste Man- the costs of collecting recyclable materials at the curb, but both use agement Research and Education Institute. The survey instrument was comprised of 38 questions and expanded upon the survey developed by Folz (1991). Modifica- data from a single state. Carroll (1995) initiated the literature by tions to the initial survey were made to enhance the specificity of queries concerning estimating recycling costs with 1992 data from 57 municipalities type and amount of materials collected, program costs and costs related to program in Wisconsin. Carroll found no economies of scale in those data. characteristics.
  • 3. 866 R.A. Bohm et al. / Resources, Conservation and Recycling 54 (2010) 864–871 Table 1 Quantity and cost variables. Variable N Minimum Maximum Mean Median Std. deviation G Tons of solid waste collected in 1996 (Q ) 428 40 1,389,000 36,610 7312 110,968 Total cost (in $s) to collect and dispose solid waste in 1996 (TCG ) 428 5407 92,913,000 2,290,700 513,763 6,882,700 Tons of recyclable materials collected in 1996 (QR ) 428 3 70,000 3232 1200 6808 Total cost (in $s) of curbside recycling program in 1996 (TCR ) 428 300 6,230,000 371,060 110,000 782,271 Table 2 Table 3 Waste collection and disposal costs (dependent variable = ln(TCG )). Curbside recycling costs (dependent variable = ln(TCR )). Variable Coefficient Standard error Significance Variable Coefficient Standard error Significance Constant ˛G = 4.4576 0.8080 1% level Constant ˛R = 7.2926 0.4705 1% level ln(QiG ) ˇ1 = 1.1302 G 0.1808 1% level ln(QiR ) ˇ1 = 0.3736 R 0.1453 1% level (ln(QiG ))2 ˇ2 = −0.0169 G 0.0099 5% level (ln(QiR ))2 ˇ2 = 0.0330 R 0.0111 1% level 2 2 N = 428; R = 0.7154 N = 428; R = 0.6378 clarification. Although these data have been used by Folz (1999a,b, decrease to $59.70 and $40.54 for the municipality collecting the 2004), Folz and Giles (2002) and Folz et al. (2005), the data have median (7312 tons) and mean (36,610) quantity of waste. The esti- not been used by economists to estimate costs functions. mated marginal cost of collecting and disposing the 1,389,000th Each variable defined above is defined and summarized in ton of waste (the maximum in the sample) is $12.19. Table 1. Perhaps due to imperfect information on the part of the These estimated cost curves are long run in the sense that they survey responders, not all questions were answered by the 1028 are obtained by comparing the total costs of municipalities with responding communities. Only 428 communities reported total varying levels of physical capital and administrative overhead. The costs and quantities for both solid waste and recycling. If the rea- costs to set up a solid waste collection system necessary to col- sons for imperfect information are uncorrelated with observed lect and dispose just 1 ton of waste (roughly two 20-pound bags quantities and costs, then using the smaller sample will not intro- of garbage per week) are small and perhaps consist only of the duce bias to the estimated coefficients. cost for the occasional use of a pick-up truck. Costs increase with The sample is comprised of a broad range of municipality sizes. expanding waste collection and disposal services due to purchases As is summarized in Table 1, the quantity of municipal sold waste of additional collection trucks, establishing transfer stations, paying collected and disposed or incinerated (QG ) varies between 40 tons disposal (tipping) fees and creating the necessary administrative and 1,389,000 tons. For TCG , each municipality reported “the total structure. collection and disposal cost for all non-recycled municipal solid As illustrated in Fig. 1, economies of scale in waste collection wastes”. These annual costs vary in the sample between $5407 and and disposal are estimated for all observed quantities of waste in $92,913,000. That the median of all variables in Table 1 are less than the sample. Both marginal and average total costs decrease with their mean suggests the sample is comprised of a large number of quantity. Increasing waste quantities may allow for the division of small municipalities relative to the number of large cities—perhaps factor resources into specialized tasks. Stevens (1978) and Tickner consistent with the overall distribution of municipal sizes in the and McDavid (1986) also find evidence of economies of scale in the United States. provision of waste collection and disposal services.3 In contrast, GLS estimates of the parameters in (1) are provided in Table 2. Callan and Thomas (2001) estimate constant returns to scale in All estimated coefficients are statistically significant at the 5% level. waste collection and disposal with constant marginal cost of $77.82 The value of R2 (0.7154) suggests a large portion of the variation in per ton. cost data can be explained by the quantity variables—perhaps not Variables used to estimate the recycling cost equation (Eq. (2)) surprising given the large range of quantity and cost data observed are also defined and summarized in Table 1. According to the in the sample. These results can be used to estimate the marginal survey, the recycling quantity variable (QR ) represents the total and average total cost curves for waste collection and disposal. tons of “non-composted, recyclable materials recovered or col- These curves are illustrated in Fig. 1. The marginal cost of collect- lected as part of the local recycling program.” This variable includes ing and disposing the 40th ton of solid waste (the minimum in the both materials collected at the curb and materials recovered at sample) is estimated at $111.40. This marginal cost is estimated to drop-off facilities. Recycling quantities varied between 3 tons and 70,000 tons across the municipalities in the sample with a mean of 3232 tons. The total cost of recycling (TCR ) represents “all direct i and indirect costs and any payments made to contractors” and “excludes revenue earned from the sale of recyclable materials.” The sample captured a broad range of program sizes, as recycling costs varied between $300 and $6,230,000 across the sample with a mean of $371,060. GLS estimation results for the recycling equation are provided in Table 3. All three parameters are significant at the 1% level. Marginal and average total costs curves based upon the GLS estimates of the parameters from Eq. (2) are illustrated in Fig. 2. The marginal cost of recycling the 3rd ton of material (the minimum amount recycled in 3 Stevens (1978) finds constant returns to scale among municipalities with pop- Fig. 1. Estimated costs of solid waste collection and disposal. ulations in excess of 50,000.
  • 4. R.A. Bohm et al. / Resources, Conservation and Recycling 54 (2010) 864–871 867 cost of collecting and disposing solid waste or operating a curbside recycling program. Let Zi1 , Zi2 , . . ., ZiK denote variables potentially influencing the marginal cost of operating waste or recycling sys- tems in municipality i. The total cost function in Eq. (1) or (2) can be expanded to ln(TCi ) = ˛ + ˇ1 ln(Qi ) + ˇ2 [ln(Qi )]2 + 1 Zi1 + 2 Zi2 + ... + K ZiK + i (3) Each determines how a change in each of the corresponding Z variables affects costs. Once again the seemingly unrelated regres- sion (SUR) econometric model is used to estimate the coefficients in (3). Fig. 2. Estimated costs of curbside recycling. Economic variables expected to affect the costs of municipal waste and recycling services include costs for labor, capital, and the sample) is estimated at $342.80. The marginal costs of recycling fuel. Because the survey does not contain detailed budget infor- the 1200th ton and the 3232nd ton – the median and mean in the mation, data on these variables are derived from outside sources. sample – are estimated at $76.53 and $72.81, respectively. As illus- WAGE is the 1996 average hourly earnings of production workers trated in Fig. 2, the marginal cost of recycling initially decreases at in each Bureau of Labor Statistics labor market area. INTEREST rep- low quantities but eventually increases. Factor specialization and resents the municipality’s opportunity cost of acquiring physical utilization reduces marginal costs at low quantity levels, but dimin- capital based on Moody’s full faith and credit bond rating. FUEL is ishing returns to recycling factors may cause marginal costs to then the prevailing local price of regular gasoline in 1996, obtained from rise at high quantity levels. The marginal cost of recycling reaches a United States Department of Energy. In terms of program attributes, minimum of $72.57 at the 4600th ton recycled. Average total costs all variables obtained from the survey that might potentially affect are minimized at a value of $75.18 at the 13,200th ton recycled. the cost of waste collection and disposal and/or the cost of recycling Examining the raw data, municipalities in the sample that recycle are included in the model. All economic and program attribute vari- an amount close to 13,200 tons of materials have populations of ables are listed in Table 4. Descriptive statistics are summarized about 80,000 persons. in Table 5. Also in Table 5 is an indication of which cost model These estimates suggest economies of scale are present in the the variable belongs to (garbage—G, and/or recycling—R). Because curbside collection only for quantity levels below 13,200 tons per the survey was designed to gather data primarily on recycling pro- year. Callan and Thomas (2001) also found economies of scale for grams, the amount of variables available to the recycling equation recycling using a sample with an annual mean of 11,098 tons of far exceeds the number available to waste. Due to missing value recyclable materials. Callan and Thomas (2001) also estimate the in the new variables, the number of usable observations decreased marginal cost of recycling at $13.55 per ton—in stark difference from 428 to 284. to the range of estimates reported above. Carroll (1995) estimated constant returns to scale in recycling using a sample of 57 munici- 4.1. The costs of waste collection and disposal palities averaging 4468 tons per year. These results are useful to any municipality contemplating SUR estimates of the coefficients for the waste equation are an expansion or contraction of their recycling program. Consider listed in Table 6. The coefficient on the log of quantity (ˇ1 ) and a municipality collecting and disposing the median quantity of its squared term (ˇ2 ) do not change substantially by adding the waste (7312 tons) and recycling the median quantity of materials extra variables. The coefficient on the factor cost for fuel is positive (1200 tons). By recycling one additional ton of waste that would and significant in the waste equation. A 1% increase in the price have otherwise been disposed, the municipality would save an esti- of gasoline is estimated to increase waste collection and disposal mated $59.70 in collection and disposal costs but incur $76.53 in costs by 1.653%. Labor costs (WAGE) and capital costs (INTEREST) added recycling costs. This extra cost is reduced by any revenues are also estimated to increase waste collection and disposal costs, gained from selling the recyclable materials. The municipality can but not with statistical significance. A 1% increase in disposal costs then compare the expected change in costs with the expected gain (TIPFEE) is estimated to increase costs by 0.17%. in environmental quality attributable to the reduction in waste 20% of the municipalities in the sample levy per-bag user fees to disposal or increase in recycling. finance a portion of solid waste collection and disposal costs. Such Household source reduction efforts presumably complement programs require municipal resources to print and distribute spe- recycling practices. Households that increase recycling may simul- cial tags, stickers, or bags required for households to dispose of each taneously seek ways to reduce the use of shopping bags and bag of waste. Holding the quantity of waste constant, such efforts beverage containers. If source reduction indeed accompanies recy- are estimated to increase the overall costs of waste collection and cling, then the 1 ton increase in recycling discussed above could disposal by 18.1%, but this estimate is not statistically significant. lead to more than a 1 ton decrease in waste quantity. If a munic- Collecting waste on the same day as recyclables (SAMEDAY) is ipality that increases recycling by 1 ton experiences a 1.28 ton estimated to decrease the marginal cost of waste collection and dis- reduction in solid waste due to source reduction, then the marginal posal by an insignificant 12.6%. Callan and Thomas (2001) suggest cost of the additional recycling ($76.53) would equal the marginal that municipalities share resources across the two collection pro- benefit of lower waste collection and disposal costs (also $76.53). grams. Finally, DENSITY, obtained from the U.S. Census (City and 4. Specific determinants of the costs of curbside recycling County Data Book) represents the density of the population in per- and waste collection sons per square mile. A 1% increase in the population density is estimated to increase waste costs by 0.092%. Waste generated in Cost functions for both waste and recycling can be expanded high-density municipalities may incur high costs to transport waste to include other variables thought to affect the long-run marginal to remote landfills for disposal.
  • 5. 868 R.A. Bohm et al. / Resources, Conservation and Recycling 54 (2010) 864–871 Table 4 Variables affecting the costs of solid waste or recycling. Factor costs WAGE Average hourly earnings of production workers in 1996 (Bureau of Labor Statistics) INTEREST Opportunity cost of capital based upon Moody’s bond rating FUEL Price of regular gas TIPFEE Cost of waste disposal ($ per ton) Scope of services MFAM Recyclables are collected from multi-family dwellings FREQUENCY Number of curbside recycling collections per month STAFFDROP Community offers staffed facility for households to drop-off recyclable materials UNSTAFFDROP Community offers un-staffed facility for households to drop-off recyclable materials PARTICIPATE The percentage of eligible households participating in curbside recycling Collection practices BINSTOHHS Recycling containers provided to households COLLSEP Recyclable materials are separated by collectors CENTSEP Recycled materials are separated in a centralized facility VARFEE Municipality levies a variable fee for waste collection CREW Size of collection crew for recyclable materials SAMEDAY Recyclables collected on the same day as solid waste collection CITYCOLL City crews collect recyclable materials using city trucks CITYPROC City owned and operated materials recovery facility processes recyclables RECTRUCK Specialized recycling truck used to collect materials Exogenous variables DENSITY Population density of municipality (persons per square mile) STMANDATE State mandate or recycling goal is very significant for continuation of recycling VINTAGE Number of years a community recycling program has been in operation (as of 1996) 4.2. The costs of recycling But the cost of fuel was significant and positive in the waste equa- tion. Perhaps waste is transported over long distances to reach Variables expected to affect the costs of operating a curbside remote waste disposal facilities. The distance to recycling facilities recycling program are listed in Table 4, and descriptive statistics may be comparatively small. for each variable are summarized in Table 5. Results suggest a 1% Several program-specific attributes could potentially affect increase in interest rates increases recycling costs by an estimated recycling costs. The first is a binary variable that indicates whether 21.903%. That INTEREST, the cost of capital, is positive in the recy- multi-family residents are included in the curbside recycling pro- cling equation but insignificant in the waste equation could suggest gram. As summarized in Table 5, 79% of municipal recycling recycling systems are more capital intensive than waste systems. programs collect materials from multi-family residences such Perhaps trucks are the only capital necessary to most waste collec- as apartment buildings (MFAM = 1). Residents in multi-family tion and disposal systems (land being the other important input). dwellings might individually transport their recycled materials to Capitalized processing facilities in addition to collection trucks may a single on-premise depot, making municipal collection from these be necessary inputs to recycling systems. households easier than for single-family dwellings. But this coeffi- Labor and fuel costs are not significant predictors of cient is not statistically different from zero. recycling costs. Wages were also insignificant in the waste The frequency of curbside service (FREQUENCY) varied in the equation—apparently neither of these systems is labor intensive. sample from as few as one collection per month to as many as two Table 5 Descriptive statistics of variables affecting costs. N Model Minimum Maximum Mean Std. deviation WAGE 284 G and R 9.80 18.32 12.84 1.62 INTEREST 284 G and R 5.10 5.34 5.24 0.04 FUEL 284 G and R 1.07 1.56 1.26 0.10 TIPFEE 284 G 0 110 38.86 18.68 MFAM 284 R 0 1 0.79 0.41 FREQUENCY 284 R 1 8 3.44 1.01 STAFFDROP 284 R 0 1 0.30 0.46 UNSTAFFDROP 284 R 0 1 0.31 0.46 PARTICIPATE 284 R 1 100 70.98 22.41 BINSTOHHS 284 R 0 1 0.81 0.40 COLLSEP 284 R 0 1 0.32 0.47 CENTSEP 284 R 0 1 0.44 0.50 VARFEE 284 G 0 1 0.20 0.40 CREW 284 R 1 5 1.63 .76 SAMEDAY 284 G and R 0 1 0.80 .40 CITYCOLL 284 R 0 1 0.44 .50 CITYPROC 284 R 0 1 0.13 0.34 RECTRUCK 284 R 0 1 0.43 .50 DENSITY 284 G and R 8 4397 944.31 751.05 STMANDATE 284 R 0 1 0.48 0.50 VINTAGE 284 R 1 28 8.16 4.43
  • 6. R.A. Bohm et al. / Resources, Conservation and Recycling 54 (2010) 864–871 869 Table 6 collectors separating recyclable materials may be welfare improv- The costs of waste collection (dependent variable = ln(TCG )). ing. Variable Coefficient Standard error Significance About 44% of the sample also collects co-mingled recyclables CONSTANT −5.944 9.522 – from households and then separates the materials at a centralized ln(QG ) 1.098 0.228 1% level facility (CENTSEP = 1). The municipality must allocate resources to [ln (QG )]2 −0.016 0.012 – operate the separating facility, but this practice could ease the col- ln(WAGE) 0.225 0.271 – lection process as collection crews simply dump all co-mingled ln(INTEREST) 4.057 5.745 – recyclable materials at once into a single truck for subsequent sep- ln(FUEL) 1.653 0.669 5% level aration. Results suggest the costs of recycling decreases by 36.3% ln(TIPFEE) 0.170 0.086 5% level per ton. This result suggests centralized separating facilities reduce VARFEE 0.166 0.116 – costs, especially if requiring households to pre-separate recycled SAMEDAY −0.119 0.119 – materials is costly to households.5 ln(DENSITY) 0.092 0.051 10% level Municipalities vary with respect to the size of crews sent out N = 284; R2 = 0.775 to collect recyclable materials from households (CREW). As few as one and as many as four workers comprise a collection crew in the sample. The average crew size is 1.63 members. The marginal cost collections per week. More frequent service holding constant the of recycling can be expected to rise with the additional labor costs quantity collected would presumably increase costs. The estimated incurred with larger crews and fall as additional crew members coefficient is positive, as expected (costs are estimated to increase allow for specialization and the division of labor in collection. The by 3.6% with each additional pick-up per month), but insignifi- data suggest changes in the crew size do not affect marginal costs, cant. Callan and Thomas (2001) estimate average costs increase perhaps suggesting that these two forces negate each other. with frequency, while Carroll (1995) finds no connection between 79% of municipalities in the sample collect recyclable materials frequency of collection and total costs. on the same day as regular waste collection (SAMEDAY = 1), perhaps As summarized in Table 5, 30% of responding communi- to make recycling more convenient to households. The marginal ties reported a staffed drop-off facility (STAFFDROP = 1) and 31% cost of collecting recyclable materials does not statistically change reported an un-staffed drop-off facility (UNSTAFFDROP = 1). The for municipalities with same-day service. model controls for the presence of either a staffed or un-staffed CITYCOLL is a binary variable that indicates whether curbside drop-off facility when estimating costs because the quantity vari- recycling is conducted by municipal employees or by a private able, QR , includes all materials recovered via curbside collection contracted company. 44% of municipalities in the sample utilize and at drop-off facilities. The marginal cost is expected to fall to the municipal employees. The marginal cost of recycling is estimated extent the municipality’s recycling costs decreases when house- to rise by 26.6% with municipal employees relative to a private holds rather than collection crews transport materials to recycling contracted collector. Carroll (1995) also found costs increase with facilities. Results do not confirm this prediction. Neither variable is municipal provision, but Callan and Thomas (2001) find no effect. statistically different from zero. Kemper and Quigley (1976) estimate that competitive markets are The data also include the estimated participation rate among eli- 25–36% more expensive than a single collector, and that contract gible households in each municipality (PARTICIPATE).4 Holding the or franchise agreements reduce costs over municipal collections by quantity of material collected constant, an increase in the participa- another 13–30% (depending on the level of service). Stevens (1978) tion rate might increase the cost of collecting material—collecting estimates that the contract or franchise agreements reduce costs by a lot of material from a few households is presumably cheaper than 26–48% below that of a competitive private market and by 27–37% collecting a little material from many households. These data con- below that of municipal provision (for cities with populations over firm the suggestion, every 1% increase in the reported participation 50,000). Costs could increase with public provisions if wage pay- rate increases costs by 0.50%. ments to municipal workers exceed those to workers of the private Just over 81% of the municipalities in the sample provide recy- marketplace or if the municipality lacks competitive pressure to cling bins, bags, or carts at no costs to participating residents minimize costs. (BINSSTOHH = 1). Costs of recycling rise with the costs of purchas- Similarly, 14% of municipalities in the sample feature a munic- ing, storing, and delivering these bins to households and fall to the ipally run processing facility (CITYPROC = 1). Other municipalities extent these bins make collection more efficient. Results suggest rely upon private companies, a non-profit association, or other gov- the two affects could offset each other—the overall affect of provid- ernmental unit to process the recycled materials. A city-operated ing bins to households on recycling costs is not statistically different processing facility is estimated here to have no statistical affect on from zero. costs. Roughly 32% of municipalities in the sample allow households Another variable that could affect the long-run marginal cost of to co-mingle all recyclable materials in a single container. The recycling is the type of vehicle used to collect materials at the curb. collectors then separate the materials at the time of collection Low-cost options could include ordinary refuse trucks, pick-up (COLLSEP = 1). The marginal cost of recycling increases to the extent trucks, or dump trucks. Alternatively, a municipality could pur- these separating services increase the time necessary to complete chase a designated recycling truck with individual compartments a collection route. But the data suggest the marginal cost is unaf- engineered for each material (RECTRUCK = 1). 43% of the sample fected by this option. Collection crews do not appear advantaged made such a choice. The presence of such a truck is estimated to by the pre-separated materials, as they may still have to deposit increase recycling costs by 6.4%, but this variable is not statistically each material into separate sections of the recycling truck. Thus, if significant. separating materials are costly to households, then a program with 4 5 The survey asked municipalities how they estimated participation rates. Each The remaining option to COLLSEP and CENTSEP is to require households to sepa- municipality could select “all that apply” from a list of 7 options. Only 10% uti- rate materials into different recycling bins or bags. Because this variable is omitted to lize actual sign-ups or subscriptions to recycling programs to observe participation. avoid colinearity with the constant term, the coefficients on COLLSEP and CENTSEP About half of the municipalities indicated their estimate was based upon subjective are interpreted as cost differences relative to having households pre-separate the “field observations.” material.
  • 7. 870 R.A. Bohm et al. / Resources, Conservation and Recycling 54 (2010) 864–871 Table 7 centralized separation rather than curbside separation enjoy lower The costs of recycling (dependent variable = ln(TCR )). costs. These results could prove useful to municipal officials inter- Variable Coefficient Standard error Significance ested in implementing changes in these approaches to recycling CONSTANT −24.775 11.480 5% level (Table 7). ln(QR ) 0.321 0.195 10% level Note, finally, that results as depicted in Table 3 and Fig. 2 might ln(QR )2 0.033 0.014 5% level mistakenly be interpreted to suggest that all municipalities can ln(WAGE) −0.483 0.332 – minimize average recycling costs by selecting the quantity that ln(INTEREST) 21.903 6.847 1% level minimizes long-run average total costs (13,200 tons). But the total ln(FUEL) −0.502 0.868 – quantity of waste material available for recycling (G + R) is largely MFAM 0.025 0.138 – determined by local populations and income levels, factors that FREQUENCY 0.035 0.057 – are exogenous to the municipal government. A very small munic- STAFFDROP −0.051 0.127 – ipality, for example, is obviously unable to achieve this minimum UNSTAFFDROP −0.009 0.123 – average cost if the total quantity of household material available for PARTICIPATE 0.005 0.003 10% level recycling (G + R) is less than 13,200. These results could prove use- BINSTOHHS 0.205 0.146 – ful to municipalities interested in estimating the marginal cost of COLLSEP −0.057 0.145 – CENTSEP −0.310 0.132 5% level increasing or decreasing the quantity of materials recycled. Results CREW 0.092 0.077 – could also inform adjoining municipalities that are interested in the SAMEDAY 0.201 0.147 – cost implications of combing recycling programs. 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