G-SEC WORKING PAPER No.25
On the Relationship between the Provision of Waste
Management Service and Illegal Dumping

Daisuke ICHINOSE† and Masashi YAMAMOTO‡
May, 2009

Abstract
The illegal dumping of waste has been a serious environmental concern of mostcountries in the world. This paper examines the relationship between the provision ofwaste treatment facilities and the frequency of illegal dumping.
While the amount of illegal dumping has a positive correlation with the provision oflandfill, our results show that a shortage of intermediate waste treatment facilities has
played an important role in increasing the frequency of illegal dumping.
Keywords: illegal dumping, count data panel model, industrial waste
JEL Classification: Q53, C23, Q24
The authors are very grateful for many helpful suggestions by Professor Eiji Hosoda (Keio University). We also
appreciate comments by Prof. Thomas Kinnaman (Bucknell University), Prof. Masao Ogaki (Ohio State
University), Prof. Masaya Sakuragawa (Keio University), and Prof. Takayoshi Shinkuma (Kansai University). An
earlier version of this paper was presented at the 4th East Asian Symposium on Environmental and Natural
Resource Economics at Taiwan National University. We are grateful for all the comments during the symposium.
Any remaining errors are our own.

† Corresponding author. Research Associate, Faculty of Economics, Keio University, Address: 2-15-45 Mita,

Minato-ku, Tokyo, 108-8345 Japan, Email:d.ichinose111@gmail.com‡Associate professor, Center for Far Eastern Studies, University of Toyama, Address: 3190, Gofuku, Toyama-shi,

930-8555 Japan

1

Introduction

The illegal dumping of waste has been a serious environmental concern of most countries in the world. According to USEPA (1998), for example, illegal dumping is “amajor problem for many communities throughout the United States”. The UK Environment Agency also reports that “[i]t is estimated to cost £100-£150 million every
year to investigate and clear up” illegal dumping.1 In response to these situations,several countries are starting to reexamine their regulations for illegal dumping andintroduce more stringent rules and/or penalties for waste crimes. UK’s Defra hasjust launched tougher penalties against waste crimes like illegal dumping,2 while
the Japanese government has within the last decade repeatedly strengthened thepenalties laid down in the Waste Disposal and Public Cleansing Law.
Although illegal dumping occurs for various reasons, one of the main factors
inducing illegal dumping is thought to be a shortage of proper waste treatment facilities. In the absence of enough proper waste treatment facilities, the cost of proper
waste disposal will increase. As a result, people are more likely to dispose of theirwaste illegally to reduce the cost of such disposal when there is a shortage of waste
treatment facilities. Munton (1996), for instance, noted that “[i]ncreased demand
coupled with insufficient disposal capacity ‘threatens to increase illegal disposal, aresult which would exact a terrible price in terms of environmental degradation anddanger to human health’ ”.3 This logic is quite intuitive, but there is little quantitative evidence to support the argument that the shortage of waste treatment facilities
increases the frequency of illegal dumping. In this paper, then, we empirically show
how the shortage of waste treatment facilities influences the frequency of illegal
dumping.
We shall show how the shortage of waste treatment facilities influences the frequency of illegal dumping. For this purpose, we use prefecture-level panel data of2

illegal dumping in Japan. In order to measure the shortage of waste disposal facilities, as we do in the following section, we use the number of landfill sites andthe number of intermediate waste treatment facilities, where waste is incinerated or
reduced in weight before being disposed in landfills.
The present study is mostly related to that of Sigman (1998), to our knowledge
the only study besides that of Kim et al. (2008) that empirically examines the issueof illegal dumping.4 Sigman studied the illegal dumping of used oil in the UnitedStates and shows the relationship between the number of illegal dumping incidents
and restrictions on the disposal of used oil, the salvage value of oil, and so on. She
did not, however, examine the effect of a shortage of waste treatment facilities, which
is what we mainly focus upon in this paper. Kim et al. (2008) also examined illegal
dumping, but from a different point of view. They argued that illegal dumping has
been induced by the introduction of unit pricing of municipal solid waste in Korea.
They urged that authorities be careful about increasing unit prices.
The main results of our study are as follows. First, we statistically show that(1) increasing the number of intermediate waste treatment facilities decreases thenumber of illegal dumping incidents, yet (2) increasing the number of landfill sites
leads to increased illegal dumping, and (3) there is a positive relationship between
the weight of waste discharge and frequency of illegal dumping. We also find that (4)stronger penalties for illegal dumping deter illegal dumping. In the next section we
describe the state of waste discharge and its illegal dumping in Japan. In Section 3
we provide economic and econometric models and the properties of the relevant data.
In Section 4 we present an estimated result and its policy implications. Finally, in
Section 5 we provide some concluding remarks.

3

2

Waste management and illegal dumping in JapanAll waste in Japan is classified as either municipal solid waste or industrial waste.
Chapter 2 of the Waste Disposal and Public Cleansing Law lists 20 types of waste
produced by business activity and defines them as industrial waste, while all therest of the waste is defined as municipal solid waste.5 In the following study wefocus on the illegal dumping of industrial waste, since data on illegal dumping ofmunicipal solid waste is not available. For the sake of notational convenience werefer to industrial waste as ‘waste’ in our analysis. Since the weight of industrial
waste discharge is much larger than that of municipal solid waste,6 we believe ouranalysis reflects the major features of illegal dumping in Japan.

2.1

The state of waste discharge in JapanFirst, we briefy summarize the state of waste discharge and treatment in Japan.
Figure 1 shows the weight of discharged waste, and how the discharged waste wastreated. In 2005 about 421 million tons of waste was discharged in Japan. Thisweight has not changed much during the last 15 years. About 22% of the waste was
directly recycled, while 75% was treated in intermediate waste management facilities
where waste is incinerated or reduced in weight. Only 3% of the total waste was
directly disposed of in landfills.
/// Insert Figure 1 around here. ///After being treated in the intermediate waste management facilities, 30% of thewaste was recycled and 3% was disposed of in landfills. As a result, about 52% of
the waste was recycled, 6% of the waste was land?lled, and the rest was reduced inintermediate waste management facilities. From this observation we can say that,in Japan, intermediate waste management plays a large role in waste treatment,
compared to that of landfill.
4

2.2

How often and how much illegal dumping is reported

Figure 2 shows the changes in the reported incidents and weight of illegal dumping
since 1996. Although the number of cases increased until around 1999 to 2001, it has
been dramatically decreasing since 2001. The number of cases in 2006 came to about
half of the figures reported in 2001. The amount of illegal dumping, however, did
not follow the tendency of the number of cases. The largest amount in the ten-year
period (from 1997 to 2006) was reported in 2003, and it amounted to 2.3% of thetotal weight of landfill in that year.
This asynchrony happens because of a few exceedingly large illegal dumping
incidents. In 2004, for example, about half of all the volume of illegally dumped
waste was accounted for by a single incident in Numazu City. Since the reported
weight of illegal dumping is greatly influenced by these few exceptional incidents,
the reported amount and the reported number show a different trend.
/// Insert Figure 2 around here. ///As we see above, we can use two types of data (the volume, and the number ofincidents), as indicators of the degree of illegal dumping. However, as Sigman (1998)
points out, measuring the weight of illegal dumping is technically difficult, and so
its figures are not always reliable. On the other hand, the problem of using number
and ignoring amount is that one pile of illegally dumped material is counted as one
time of illegal dumping regardless of its size. Since massive illegal dumping is usually
the result of repeated dumping on one pile, the meaning of the reported number ofillegal dumpings can be ambiguous if there are many cases of such huge piles.
Still, only 0.6% of illegal dumpings were classified as huge illegal dumpings –morethan 5,000 tons according to the Ministry of Environment (2007c; hereafter, MOE)–
between 2000 and 2006, and many cases involved less than 50 ton.7 For these reasons5

in the following analysis we follow Sigman (1998) and use the reported number ofillegal dumpings, not weight, as an indicator of the degree of illegal dumping.8

2.3

What is dumped by whom

According to MOE (2007c), in 2005 composite waste from construction sites, including debris, was a major component of illegally dumped wastes, both in termsof weight and numbers. It made up 393 incidents out of 558 (70.4%) and 143,000
tons out of 172,000 tons (83.3%). As for the total discharge of industrial waste, thelargest portion was discharged by the utility industry, which was followed by the
agricultural industry, with the construction industry being the third largest with ashare of just 19.0%.
/// Insert Table 1 around here. ///MOE (2007c) also tells us by whom the reported illegal dumping was done.
The information is summarized in Table 1. In these statistics, a discharging firm
is defined as one that discharges any kind of waste. This thus includes not only
manufacturers’ factories but also intermediate waste treatment firms. In Japan, theWaste Disposal and Public Cleansing Law prescribes that a firm needs a license when
it runs a business dealing with waste. The term ‘licensed contractor’ means that thefirm in question has this license, while a ‘non-licensed contractor’ does not. Table 1
shows that the majority of specified illegal dumpings were done by discharging firms.
From the point of view of the number of incidents, it amounts to two-thirds of thespecified incidents. When we look at the weight of the illegal dumping, however,licensed contractors play a major role. Even though discharging firms still dump
the most, the share of the licensed contractors (who are expected to deal with waste
properly) is the second largest, making up about 25% of the specified weight.

6

2.4

Geographic Features

In order to clarify the spatial features of illegal dumping, we plot the data on illegal
dumping and each prefecture’s income level in Figure 3.9 Note that the numberof illegal dumpings is aggregated for a period of ten years (from 1997 to 2006).
Economic levels are quite high in the prefectures between Osaka and Tokyo, thelargest city in Japan.
/// Insert Figure 3 around here. ///According to MOE (2007c), Tokyo recorded the smallest number of illegal dumpings even though huge amounts of industrial waste were generated in Tokyo for thoseten years. Osaka, the second biggest prefecture in Japan, also recorded a small number of illegal dumpings. This phenomenon indicates that waste is transported fromprefecture to prefecture, and waste generated in one prefecture is illegally dumped
in other prefectures. We try to control this phenomenon in our explanation at theend of section 3.

3

The Model

In this section, we provide a model of illegal dumping. Initially we develop an
economic model to identify the determinants of an illegal dumping incident. Then
we confirm the results of the economic model by using an econometric method,showing the main results of the study.

3.1

The Economic Model

Suppose that in a market there are k number of identical firms that discharge waste
and n identical firms disposing of the waste. We assume that all players are pricetakers. A discharging firm possesses G amount of waste to be disposed of in total.
Once the waste is handed over to a disposing firm, it can be disposed of in an7

appropriate treatment process (denoted by L). Or the discharging firm can dump thewaste illegally. The amount of illegal dumping is denoted by mI, which represents thenumber of illegal dumpings (m) and the fixed amount of waste dumped per numberof dumpings (I). We assume that it costs cL to dispose of the waste properly andcI to dump mI amount of waste illegally. Although the cost for the proper waste
management cL ( in other words price of a legal waste management service) is a key
determinant of the number of illegal dumping incidents, there is no available datathat can be used as an proxy variable for in Japan. Thus we cannot characterizecL as an exogenous variable. To alleviate this problem, we characterize cL as an
endogenous variable, which is determined in market equilibrium. This enables us toderive a function for cL with the variables which we can find appropriate real data.
Furthermore, illegal dumping would be punished if detected. Let p(mI), withp0 > 0, p00 > 0 be the probability of the environmental authority detecting the illegal
dumping. Then the individual discharging firm i faces the following problem:

min cL Li + cI mi I + p(mi I)F,(1)

s.t. Li + mi I = G,

(2)

Li ,miwhere F stands for the fine for illegal dumping. By minimizing (1) with the constraint, we get the demand function of the proper treatment of waste as follows:cL − cI − p0 (G − Li )F = 0(3)

Let Ldi be the individual firm’s demand for legal waste treatment; then, from (3),the individual firm’s demand function can be written as Ldi = l(cL , cI , G, F ). Since
we suppose all firms are identical, total demand for legal waste treatment iskX

Ldi = kl(cL , cI , G, F ).

i=1

8

(4)

If we now look at the disposing firms, each of them are also assumed to have thesame supply function of waste treatment service. That is,
Lsj = fj (cL ),(5)where j is the indicator of each disposing firm. We suppose fj (·) is twice differentiable
and its first derivative is positive. Since all disposing firms are homogeneous, theaggregate supply function can be written as follows:nX

Lsj=1 = nfj (cL ).

(6)jwhere n is the number of imposing firms. At the equilibrium,Pdi Li =Psj Lj mustbe satisfied. That is,kl(c∗L , cI , G, F ) − nf (c∗L ) = 0,

(7)

must be met. We are interested in how a small change in a variable affects the othervariable at the above equilibrium. In particular our main concern is to examine eachparameter’s effect on the amount of proper treatment. To focus on the relationshipbetween the number of facilities, n, and the number of illegal dumping, m, let us
assume that k = 1.Then a little bit of algebra results in the following (see Appendix
for details):dL∗i(∂cL /∂n)
=−
>0
dn
p00 F
dL∗i
(∂cL /∂G) − p00 F
=−
>0
dG
p00 F

dL∗i
(∂cL /∂cI ) − 1
=−
>0
dcI
p00 F
dL∗i
(∂cL /∂F ) − p0
=−
>0
dF
p00 F

(8)
(9)

Note that the sign of the illegal dumping is just the opposite of that for proper
treatment since G is given10 . Let M stand for the total number of illegal dumpings,
P
namely, M (n, cI , G, F ) ≡i m(n, cI , G, F ). The comparative static on M is as

9follows:
X dm∗
dM
i
=
<0
dn
dn
i
X dm∗
dM
=
<0
dcI
dcI
i
¶
X dm∗ X µ
dM
dL∗i
=
=
1−
>0
dG
dG
dG
i
i
X dm∗
dM
=
<0
dF
dF

(10)
(11)
(12)
(13)

i

Thus, theoretically speaking, we can say from (10) that an increase in the number ofwaste treatment facilities decreases the number of illegal dumpings. We also have therelationship between the number of illegal dumpings and other factors: (11) indicatesthat a high cost of illegal dumping decreases illegal dumping, (12) indicates that alarge amount of waste discharge increases illegal dumping, and (13) indicates thatstrict punishment decreases illegal dumping. In the following subsection we try toverify these theoretical results by using econometric inference.

3.2

The Econometric Model

The number of illegal dumping incidents, which we use as a dependent variable inour estimation, can take only non-negative integer values. This means that our datais count data. In analyzing count data, application of the conventional OLS method
is inappropriate. The usual model in count data analysis is a Poisson regression
model, which assumes that each dependent variable follows Poisson distribution.
But since that model is based on a strict assumption that requires the equality ofconditional mean and conditional variance, an assumption that is often violated ina count model, we adopt a negative binomial regression model that allows a moregeneral relationship between conditional mean and conditional variance.11 A panel
data version of a negative binomial model was first developed by Hausman, Hall andGriliches (1984), and our estimation method basically depends upon their work.12
10

3.2.1

Reported and Actual Number of Incidents

Since we can only use the data from “reported” illegal dumping incidents, which
always differ from the actual number of incidents, there may be a bias in estimation
if the frequency of reporting is affected by covariates. To handle this problem, weapply the approach developed by Sigman (1998), who assumes that the observed
number of illegal dumping incidents is the product of the actual number of incidents
and the“reporting propensity”. We specify the relationship as follows:
Mtob = Mt−1 (n, cI , e, G, F )ξt (r)(14)

where t indicates year t. The LHS of (14), Mtob is the observed number of incidents.
Mt−1 (n, cI , e, G, F ) is the actual number of incidents explained by variables we indicated in the theoretical model, while ξt (r) is the probability of reporting explainedby covariates vector r. As Sigman (1998) noted, this approach cannot avoid the bias
perfectly, but we believe this is the best approach we can take to reduce the bias.13
Unlike Sigman (1998), we assume that there is a time lag of one period between
the occurrence and detection (or reporting) of an illegal dumping incident. This isbecause, in the real world, there are quite a number of illegal dumpings that arereported some time after the dumping takes place. In Japan, extensive investigationmust be carried out before authorities finally announce that something has been
illegally dumped. Such investigation requires considerable time, so a time lag between occurrence and detection/reporting has to be taken into account. To reflect
this fact, we assume that the reported number of illegal dumping incidents in thepresent period is obtained by the actual number of illegal dumpings in the last period
multiplied by the probability of detection/reporting in the present.

11

3.2.2Data

In the previous subsection we derived the theoretical model regarding the numberof illegal dumpings. Our next step is to assign the real data for each argument in(14) in order to execute an econometric inference. We prepared the data for a tenyear period (from 1997 to 2006) for each prefecture. Since there are 47 prefecturesin Japan, we have 470 observations in this data set. Table 2 gives the descriptive
statistics of the data we use for the count data panel model. The correlation matrixof the data is shown later in Table Table 3.
/// Insert Table 2 around here. ///Let us first explain the dependent variable. We adopted the number of illegal
dumping incidents (no) as a dependent variable because of the reliability of data.
The data was taken from MOE (2007a).14 The rest of the variables in Table 2
are independent variables. First, lwaste denotes a one-year lag of industrial waste
discharge in terms of weight. This data can be found in MOE (2007a) and is assigned
to G in (14).
The number of landfill and intermediate waste treatment facilities can be foundin MOE (2007b). To use them as independent variables, we divided them by unit
area and adopted a one-year lag for each of them. These two are represented byllastm and lmidm, which means we have two alternatives for n in (14).
As an index of strengthened regulation, we selected years of sentence for illegal
dumping and named it ljail. It plays the role of F in (14). This punishment isdetermined in chapter 25 of the Waste Disposal and Public Cleansing Law, and atpresent it stands at five years (or less) of imprisonment if arrested. The severity ofthe punishment varies from year to year, yet it is identical in all prefectures.15 Notethat a one-year lag is also adopted for ljail. In other words, we assume that there12

is some range of a time lag before toughening of the punishment becomes effective.
For the “cost” of the illegal dumping, cI , we use the ratio of cultivated acreageand label it as lcultiv. Since we assume that the cost of illegal dumping influences
the actual number of illegal dumpings, not the reported number, a one-year lag is
adopted for lcultiv. It is reasonable to take the ratio of cultivated acreage for thecost because the level of ease in dumping illegally depends very much upon how easy
it is to find a place to dump and how many people may be nearby when waste isdumped. So we assume that the cost of illegal dumping decreases as the ratio of
cultivated acreage increases. We also use population density, denoted by ldense, as
an index of the cost of illegal dumping. This is the ratio of the population divided
by the area in each prefecture, with a one-year lag adopted. This was taken from
National Census of 2005. As in the case of lcultiv, if population density is high,then it is difficult to find a place to dump waste, and so the cost of illegal dumping
will increase as population density increases.
For rit in (14), pclaim is adopted. This is the per capita number of complaints
about pollution grievances in each prefecture. To avoid the endogeneity problem, we
exclude grievances about illegal dumping from the total number of grievances. The
statistics regarding the complaints are available from the Environmental Dispute
Coordination Commission (2007). This survey reports the number of grievances
accepted at a pollution grievances inquiry counter established in each local publicagency. Since more complaints means people are aware of their environment andwatching for environmental crime more attentively, we can assume that if pclaim ishigh, then the probability of reporting is high.
As was mentioned in the last part of Section 2, there is evidence that waste
moves beyond a prefecture’s borders, and that waste generated in one prefecture isillegally dumped in another prefecture. So we have to consider the interprefectural

13

transportation aspect of waste to provide unbiased estimation.
Although there is no available data that can directly measure trends in wastetransportation, we can use the cost of illegal dumping, cI , as a proxy indicator. It issaid that waste tends to go to places where it is easy to dump the waste. In otherwords, we can assume that waste moves from a prefecture where the cost of dumping
is high to a prefecture where the cost of dumping is relatively low. Thus we can use
the cost of illegal dumping as a transport-related variable. Although it does notenable us to avoid the bias perfectly, we believe it is the best way to reduce the bias.

4

Estimated results

Tables 4 and 5 present the coefficient estimates and standard errors for the model
explained above. As shown in the tables, we considered five types of negative binomial models. Each of them is estimated by the fixed effect model and random effectmodel.
/// Insert Table 4 around here. ///Our main finding is that the coeffcient of lmidm is negative and statisticallysignificant, at least at the 5% level in all models that include lmidm as an explanatory variable. In other words, the number of illegal dumping incidents decreases(increases) as the number of intermediate waste treatment facilities per unit area
increases (decreases). This estimation result empirically confirms the common belief
that the proper provision of waste treatment facilities deters illegal dumping. It isalso consistent with the result of our economic model.
On the other hand, llastm is not always significant and the sign is different
from the theoretical result. Our empirical result indicates that the number of illegal
dumpings increases as the number of landfill sites increases. This result is quite

14

different from the result of our economic model, and also different from the popularbelief that provision of waste treatment facilities decreases illegal dumping. One
reason for this contradiction may stem from the fact that landfill sites are always
located at a place convenient for those who intend to dump waste illegally. Since a
landfill site may cause environmental pollution, such as land or water pollution, it isusually built away from populated areas. This is why a location that is appropriate
for a landfill will be also suitable for illegal dumping. Another reason is that illegal
dumping takes place even at legal landfill sites. There are two types of illegal dumping
at a landfill. One involves extending the landfill illegally. Most landfills suffer fromexhaustion of their capacity. Not a few landfill sites extend their boundaries without
official permission, and this is deemed to be illegal dumping (MOE, 2007c). Therefore
the more landfill sites there are, the more illegal dumping there will be. The othertype involves dumping waste that is prohibited from being dumped. Each landfill is
assigned to receive a specific type of waste. For example, most landfills prohibit the
dumping of toxic waste. If a toxic substance is found at a landfill, it is recorded as
an illegal dumping. Again, this leads to more illegal dumping, the more landfill sites
there are, something that has not been considered in the theoretical part.
We also find other interesting results from the estimation. First, the weight ofindustrial waste discharge (lwaste) has a positive effect on the number of illegal
dumping incidents, and it is statistically significant at the 1% level in almost allmodels that includelwaste as an explanatory variable. This result tells us that the
number of illegal dumpings increases as waste discharge increases. Second, we foundthat the penalty for illegal dumping, denoted by ljail, does deter illegal dumping.
In particular, the number of illegal dumpings decreases as the length of jail terms
increases. Yet these results are not always statistically significant.

15

Finally, the results indicate that the cost of illegal dumping is negatively correlated with the number of illegal dumpings. The positive signs for the coefficient oflcultive show that if the cultivated acreage is large, where the cost for illegal dumping is relatively low, then the number of illegal dumpings increases. On the other
hand, the negative signs for the coefficient of ldense indicate that if the population
density is relatively high, where the cost for illegal dumping is also relatively high,then the number of illegal dumpings decreases. As in the case of ljail, however,these results are not always statistically significant.
/// Insert Table 6 around here. ///Table 6 is a summary of estimated results in comparison with theoretical results.
The ‘number of significant models’ in Table 6 shows in how many models eachvariable is statistically significant at least at the 10% level. It is apparent that
only llastm behaves improperly, since the signs of the two columns differ (for thereasons we discussed above), while all the other variables seem to fit well.

5

Policy implication for waste management

Our results support the common belief that the proper provision of waste treatment
facilities decreases illegal dumping. Specifically, we show that the number of illegal
dumpings decreases as the number of intermediate waste management facilities increases. Each estimated result consistently showed that the number of intermediate
waste treatment facilities has played an important role in the frequency of illegal
dumping. This result suggests the probability that not only strict punishment but
also the provision of waste management facilities, especially intermediate waste management facilities, may deter illegal dumping. If there are sufficient numbers of legal
intermediate waste management facilities, the cost of legal dumping will decrease,and the incentive to dump illegally will be dampened.
16

In comparison, the analysis shows that the number of landfill sites has a positiveeffect on the number of illegal dumpings. This result contradicts the common belief.
There is, however, an estimation result that llastm is statistically insignificant. Thismeans we cannot draw conclusions about how the provision of landfill sites affects
the number of illegal dumpings. To explain the relationship between landfills andillegal dumping is something we aim to look at in further research.
/// Insert Figure 4 around here. ///Another robust conclusion is that the volume of industrial waste discharge has
a positive effect on the number of illegal dumping incidents. The straightforward
implication from this is that we should introduce a policy that encourages firms to
reduce their industrial waste discharge. In order to do more to promote a resource
circulation society, Japan s Ministry of Environment has implemented a variety ofmeasures, especially since the enactment of the Circulated Social System Promotion
Standard Law in June 2000. One of the aims of this law is to reduce the amount ofwaste emitted. Nevertheless, emission has not decreased, neither in industrial waste
nor in municipal solid waste.
Figure 4 shows the change in emission, landfill, and recycling of waste from 1997
to 2005. Each set of data is normalized by setting the data in 1997 as 100. It isapparent from Figure 4 that waste discharge has not changed, while the other two
indexes are successful. Since this phenomenon is observed not only in Japan but
also in many other countries,16 we are skeptical about a policy targeted at emission reduction in order to reduce illegal dumping. In such circumstances, therefore,
along with a penalty for illegal dumping, the proper provision of waste management
facilities can be an effective policy to deter illegal dumping.

17

Appendix
The results of comparative statics can be derived by using the following equations.
Initially the comparative statics of discharging firm’s demand function becomes asfollows:∂Ldi
1= − 00 < 0
∂cL
p F
d
∂Li
−1
= − 00 > 0
∂cI
p F
−p0
∂Ldi
= − 00 > 0
∂F
p F
d
∂Li
−p00 F= − 00
=1>0
∂G
p F

(15)
(16)
(17)
(18)

Since all firms are price takers, cL is constant for discharging firms. Then, comparative statics of equilibrium price of legal waste management is as follows:
∂c∗L
−f
=−
<0
∂n
kLcL − nf 0

(19)
1

∂c∗L
LcI
1
p00 F
=−
=−
=
∈ (0, 1)k
0 p00 F
0∂cI
klcL − nf 0
1+
nf
− p00 F − nf
∂c∗L
kLF
p0
=−
=
>0
∂F
kLcL − nf 0
1 + nf 0 p00 F
∂c∗L
kLG
1
p00 F
=−
=
−
=
>0
∂G
kLcL − nf 0
lcL − nf 0
1 + nf 0 p00 F

18

(20)
(21)
(22)

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Dumping of Industrial Waste in FY 2006.
[12] Munton, D. (1996). Hazardous Waste Siting and Democratic Choice, Georgetown University Press.
[13] Peterson, M. A. (2009). “Estimating Standard Errors in Finance Panel Data
Sets: Comparing Approaches,” Review of Financial Studies, vol. 22, no. 1, pp.
435-480.
[14] Porter, R. (2002). Economics of Waste, Resource for the Future Press.
[15] Sigman, H. (1998). “Midnight Dumping: Public Policies and Illegal Disposal of
Used Oil,” RAND Journal of Economics, vol. 29, no. 1, pp. 157-178.
[16] USEPA(1998) Illegal Dumping Prevention Guidebook, EPA905-B-97-001.
[17] Winkelmann, R. D. (2003). Econometric Analysis of Count Data, 4th ed.,Springer.
[18] Wooldridge, J. (2002). Econometric Analysis of Cross Section and Panel Data,
MIT Press.

20

Tables
Table 1: The number and amount of illegal dumping by whomnumber of cases
amount of dumping(tons)
discharging firm
271
47,810non-licensed contractor
63
10,499licensed contractor
58
34,869combination of above
14
26,109not specified
148
11,947
total
554
131,234
Source: Ministry of Environment (2007c)

21

Variable
noTable 2: Summary statistics
Description of Data
number of illegal dumping incidents

Mean
18.5880(Std. Dev.)
28.0385

lwastedischarge of industrial waste (million tons)

8.60387.3429llastmnumber of landfill sites per 1000 km2
in each prefecture

8.78377.5722

lmidm

number of intermediate waste treatment
facilities per 1000 km2

64.190161.9313ldensedensity of population

639.2518

1098.329

ljail

year of sentence for illegal dumping

2.65632.0302

lcultiv

ratio of cultivated acreage

12.80415.7766per capita number of complaints about
environmental problems
the number of observations

0.68060.3000pclaim22

470

lmidmllastmlwastelcultiv
ljail
ldensepclaimlmidm
1.00000.43950.47110.07050.13370.75500.1690

Table 3: Correlation Matrix
llastm lwaste lcultiv ljail

ldenseclaim

1.00000.12340.3213-0.04130.07380.12861.00000.10421.00001.00000.16660.00290.5323-0.062923

1.0000-0.0505-0.10760.13641.00000.00460.231224

470-1343.106
58.4558

Significance level: ∗∗ : 1%, ∗ : 5%, † : 10%N
Log-likelihood
χ2

Variablelmidm
llastmlwastelcultiv
ljail
ldensepclaimIntercept
470-1625.3678
75.9040

Model 1
Fixed EffectRandom Effect
Coef.
(S.E.)
Coef.
(S. E.)-0.0102∗∗ (0.0023) -0.0066∗∗ (0.0019)
0.0467∗∗ (0.0125)
0.0299∗∗ (0.0106)∗∗
0.0494(0.0131)0.0430∗∗ (0.0104)
0.0058(0.0173)
0.0313∗(0.0135)-0.0176(0.0233) -0.0314(0.0221)0.0823(0.1808) -0.2496∗(0.1180)
0.4813(0.3075)
0.4890†(0.2738)
0.5151†(0.2774)0.3603(0.2381)470-1629.3313
43.4384470-1349.5113
70.7426

Model 2
Fixed EffectRandom Effect
Coef.
(S.E.)
Coef.
(S.E.)-0.0081∗∗ (0.0023) -0.0041∗(0.0017)0.0449∗∗ (0.0129)
0.0413∗∗ (0.0101)
0.0248(0.0161)
0.0418∗∗ (0.0124)-0.0304(0.0228) -0.0450∗(0.0216)0.0702(0.1869) -0.3259∗∗ (0.1126)
0.4933(0.3052)
0.4933†(0.2691)∗
0.6066(0.2725)0.4043†(0.2333)

Table 4: Estimation results (1)470-1353.0763
37.0885470-1631.5253
65.4864

Model 3Fixed EffectRandom Effect
Coef.
(S.E.)
Coef.
(S.E.)
0.0323∗(0.0131)0.0126(0.0097)
0.0462∗∗ (0.0132)
0.0410∗∗ (0.0103)
0.0016(0.0171)
0.0287∗(0.0133)∗∗-0.0657(0.0212) -0.0631∗∗ (0.0205)-0.4743∗∗ (0.1265) -0.5211∗∗ (0.0867)
0.3717(0.3101)0.4203(0.2733)
0.5804∗(0.2737)0.4114†(0.2346)25

470-1350.7161
39.3757

Significance level: ∗∗ : 1%, ∗ : 5%, † : 10%N
Log-likelihood
χ2

Variablelmidm
llastmlwastelcultiv
ljail
ldensepclaimIntercept
470-1639.2859
43.0759470-1359.0219
23.7535470-1651.7361
218.5305

Table 5: Estimation results (2)Model 4Model 5
Fixed EffectRandom Effect
Fixed EffectRandom Effect
Coef.
(S.E.)
Coef.
(S.E.)
Coef.
(S.E.)
Coef.
(S.E.)-0.0073∗∗ (0.0016) -0.0072∗∗ (0.0013)∗†
0.0245(0.0122) 0.0192(0.0101)
0.0531∗∗ (0.0116) 0.0449∗∗ (0.0103) 0.0327∗∗ (0.0120) 0.0202†(0.0111)-0.0409∗(0.0207) -0.0403∗(0.0203) -0.0612∗∗ (0.0210) -0.0616∗∗ (0.0208)††
0.5263(0.3027) 0.5219(0.2786) 0.2300(0.3169) 0.2051(0.2942)0.8678∗∗ (0.2129) 0.9482∗∗ (0.1953) 0.6111∗∗ (0.2329) 0.7852∗∗ (0.2106)

Table 6: Summary of Empirical Results in Comparison with Theoretical Results
variablesign
sign
number of(empirical)(theoretical)
signif. models
lmidm
−
−
2/2
llastm＋−1/2
lwaste
＋
＋
3/3
ljail
−
−
2/3
lcultiv＋＋
2/3
ldense−−2/3
pclaim++1/3

26

Figure Titles

Source: Ministry of Environment (2007a)

Figure 1: Discharge and Treatment of Industrial Waste in Japan (FY2005, per thousand tons)

27

1200
1100

70

1000

60

800

900

number50
volume

40

700

30

600

20
1996

1998

2000

2002

2004

2006

1996

year

1998

2000

2002

2004

year

Source: Ministry of Environment (2007c)Figure 2: Weight and Numbers of Illegal Dumping in Japan

28

2006

GDP: per capita gross regional income in 2005 (unit: thousand yen)Frequency: total number of illegal dumpings from 1997 to 2006

Figure 3: Geographic pattern of illegal dumping in Japan29

Municipal Solid Waste (1997−2005)

Industrial Waste (1997−2005)

150
emission

0

0

50

100

150
100
50

emission

Emission
Landfill
Recycling

Emission
Landfill
Recycling

1998

2000

2002

2004

1998

year

2000

2002
year

Source: Ministry of Environment(2007a)

Figure 4: Change in weight of emission, landfill, and recycling

30

2004

Notes1

http://www.environment-agency.gov.uk/subjects/waste/1029679/1032559/?lang= e
See the news release (http://www.defra.gov.uk/news/2008/080613a.htm) on July 13, 2008 fordetails.
3
Munton (1996), p.7.
4
Contrary to the empirical study, there are several theoretical studies focusing on illegal dumping,such as Fullerton and Kinnaman (1995).
5
Broadly speaking, waste is defined as industrial waste if it is emitted from a firm. It would becalled municipal solid waste if it is emitted from a household.
6
In 2005, for example, the volume of industrial waste discharge was about 421 million tons, whilethe volume of municipal solid waste was about 50 million tons.
7
According to the Ministry of Environment (2007c), about 60% of illegal dumping incidents
involve less than 50 tons.
8
Note that illegal dumping must be reported only when the weight of waste is over ten tons
altogether.
9
The indicator is cited from Japan’s SNA.
10
The only exception is when we consider the change against G, where both L and mI have thesame sign as G.
11See, for example, Cameron and Trivedi (1998) for details
12
The estimation was done by STATA. For the actual procedure of the estimation, see the manualof STATA. For further theoretical issues, see Hausman, Hall, and Griliches (1984).
13
See Sigman (1998) for details.
14
Regarding the source of data, we only mention the latest issue of the publication. Some of thedata we used were taken from the older editions.
15
Even though the fine’s data is available, we do not use it because the data made little change
relative to the prison time between 1997 and 2006.
16
See, for example, Johnstone and Labonne (2004).
2

31