E VALUATING U NIT- BASED P RICING OF R ESIDENTIAL S OLIDWASTE : A PANEL DATA A NALYSIS
Takehiro Usui ∗†Kenji Takeuchi

1 March 2012

Abstract
Municipalities introduced unit-based pricing (UBP) with the aim of achieving a decrease in household
waste generation and for the replacement of unsorted waste with recycling. Although many studies haveshown that UBP has a short-run effect on recycling, few works have tackled the long-run effect on waste
generation and recycling. By using panel data for 665 Japanese cities over 8 years, we examine the long-run
effect of UBP on waste generation and recycling. The estimation results in waste generation suggest that
there is a rebound effect, though a small one. We confirm that the effect of UBP on recycling sustains forthe long run. We also find that the short- and long-run responses to an economic incentive for recycling
activities differ with income groups. Recycling among the high-income group has not been promoted by
implementation of UBP, but people in that group are willing to participate in recycling without an economic
incentive. In contrast, recycling activity within the low-income group is strongly motivated by UBP for
many years.
JEL classification: R20, Q53, Q58
Key words: Unit-based pricing, Waste reduction, Recycling, Panel data analysis, Long-run effect∗†Corresponding author: Takehiro Usui, Faculty of Economics, Soka University, 1-236, Tangi-cho Hachioji, Tokyo 192-8577, Japan.
Tel: +81(42)691-4016; Fax: +81(42)691-8232; E-mail: usui@soka.ac.jp1

1

Introduction

The unit-based pricing (UBP) of residential solid waste collection has been implemented in many parts of theworld, including municipalities1 in the United States, the EU, and South Korea (OECD, 2006). This also applies
to Japan, a country with limited space for landfill waste, where its Ministry of the Environment has encouraged
and supported municipalities to introduce UBP.2 Expecting to achieve waste reduction and the substitution
effect of recycling, the proportion of municipalities introducing UBP increased from 50% to 78% between
1999 and 2009 (Ministry of Environment, Japan, 2001, 2011). However, several years after the introductionof UBP, some municipalities reported an increase in waste generation over what was generated during the firstyear of the introduction of UBP. This paper focuses on the long-run reduction and substitution effects of UBP.
Government and municipality officials in Japan suspect there is a long-run reduction effect connected with
UBP, though they keep encouraging its introduction. The Ministry of Environment added a caveat against UBP,
saying “We are concerned about the erosion of the reduction effect after a few years have passed, because
people become less and less sensitive to the price burden and do not attempt to reduce their waste. In orderto retain the reduction effect, it is efficacious to supply information to citizens to help their understanding”(Ministry of Environment, Japan, 2007, p. 41).3 Yamaya (2007) also points out that municipalities need to takeadditional steps to promote waste reduction after they introduce UBP.
Many municipalities hesitate to introduce UBP because they believe that, even if a bag price is introduced,
a rebound will occur within a couple of years. For example, Nagano City stated that, “Since the introductionof UBP, we continuously check the reduction effect and take steps to prevent the rebound effect” (Nagano City,
2007, p.4). Otsu City maintained that “Rubbish generation increased by 1.1 times that of the previous year. In
the examples of other cities we see a continuous gradual increase in a waste rebound under UBP” (Otsu City,
2011, p.1). However, these claims do not seem to be based on conventional statistical analysis.
In spite of the anxiety expressed by municipalities, the rebound effect of UBP has not received muchattention in previous studies on the economics of household waste management. For example, among thestudies reviewed by Kinnaman (2006), most of them used cross-sectional or panel data gathered for less than
three years and focused on the short-run effect of UBP. Policy evaluation based on these estimates will be
misleading in the long run, if there is any possibility of a rebound effect. A few studies have addressed thelong-run effect of UBP (Amano et al., 1999; Yamakawa and Ueta, 2002; Linderhof et al., 2001; Dijkgraaf
and Gradus, 2009), but they have shortcomings with regard to geographical coverage of data or econometric
1
2

In Japan a municipality is defined as a village, town, or city.
Japan’s direct landfill rate was only 11% in 1995, the lowest among OECD countries. Compare this figure with, for example, 57%

in the USA and 83% in the UK (see OECD 2008, p.16). In Japan municipal solid waste is mainly dealt with by having households first
separate out recyclable materials and then incinerating what is left, thus reducing the volume of waste.
3This manual is distributed to all municipalities in Japan. This is our partial summary and translation of the caveat.

2

techniques, as we will see more precisely in the next section.
Our study adds to the contributions of earlier literature by estimating the long-run price effects in UBP,
paying careful attention to the problems of sample representation and endogeneity. We estimate the long-run
reduction effect using figures on the municipal solid waste of all Japanese cities, obtained through a panel data
set. We also test the long-run effects of UBP with respect to waste reduction and recycling. Note that UBP has
been adopted not only to encourage households to reduce overall waste and reallocate waste from the unsorted
waste pile to the recycling pile, but also to make people continue these activities over the years. Also, thelong-run effect of UBP might be different between income groups, since there is a difference in opportunity
cost of time and environmental awareness. To sum up, our research goal is to 1) test whether or not there is arebound effect in waste generation; 2) test for a rebound effect in recyclable waste collection; and 3) test for arebound effect between different income groups.
The paper is organized as follows. The next section explores previous relevant literature on estimation of
the short-run and long-run effects of UBP, and it clarifies their relation with a rebound effect. Sections 3 and 4
describe the econometric model employed and the type of data used, respectively. Next, Section 5 presents a
detailed report of the estimation results. In Section 6 we go on to discuss the long-run effect of UBP in different
income groups. The final section contains the concluding remarks.

2

Relevant Literature

Before summarizing the relevant literature, in Subsection 2.1 we first explain the short-run and long-run effects
of UBP as well as the rebound effect, and clarify their mutual relationships. In Subsections 2.2 and 2.3 we summarize previous studies of short-run and long-run effects, respectively, concluding the latter with a statementof our research goal: to clarify whether a rebound effect occurs or not.

2.1

Explanation of short-run, long-run, and rebound effects

The short-run effect of UBP means the price effect of UBP relative to waste generation over only 1 year
(month), estimated by cross-section or panel data analysis, while the long-run effect, over a longer period oftime, is almost always estimated by panel data analysis, and very rarely by cross-section analysis.4
A rebound effect that is part of a long-run effect can be defined as an effect of UBP in the long run that canbe attributed to the UBP, not some other cause. Figure 1 gives a simple illustration of the rebound effect, withthe amount of waste per capita in the vertical axis and time in the horizontal axis. In this figure, we assume
that there is a positive time trend in waste generation. When UBP is introduced at time t1 , the amount of waste
decreases from r0 to r1 . The vertical distance between r0 and r1 can be regarded as the short-run price effect.
4

To our knowledge there are no studies of time-series analysis.

3

A few years after the introduction of UBP, waste generation per capita might seem to increase and approach
the level before the introduction. This may not necessarily be a rebound effect, since there might be a positive
time trend in waste generation. As long as the predicted waste generation (the dashed line) and realized waste
generation (the solid line) run parallel, there is no rebound because the increasing tendency cannot be attributed
to the effect of UBP. But if the slope of the latter is greater than the former, there is a rebound effect. To detect
the effect, it is necessary to consider the impact of UBP on waste reduction under the ceteris paribus condition,for example, by controlling the varying waste composition with the age structure of residents or with an increasein consumption during the period. On the other hand, if waste generation decreases below the dashed line in
Figure 1, we define this as an inverse rebound effect. Different rebound effects in the long run arise becauseof a mixed effect of negative and positive changes in citizens’ behavior. An example of a negative effect isawareness erosion: the effectiveness of UBP erodes over time if households get used to paying for waste andrevert to their old behavior (Dijkgraaf and Gradus, 2009). Another example is “Seattle stomp”, a term coined
when Seattle residents responded to a unit-pricing program by compacting garbage into fewer bags (Fullerton
and Kinnaman, 1996). An example of a positive effect is the learning effect, whereby citizens gradually learn
how to reduce general waste by sorting out recyclable objects and choosing goods with less packaging.

2.2

Short-run studies

There is a considerable amount of empirical literature on waste reduction through UBP in the short run. Notably,
there has been an increase in studies on the practice of separating recyclable objects from waste, which hasprovided an incentive to UBP (e.g., Hong et al., 1993; Saltzman et al., 1993; Reschovsky and Stone, 1994;
Callan and Thomas, 1997; Nestor and Podolsky, 1998; Hong and Adams, 1999; Bartelings and Sterner, 1999;
Kinnaman and Fullerton, 2000; Jenkins et al., 2003; Callan and Thomas, 2006; Allers and Hoeben, 2010;
Huang et al.2011). For a recent review, see Ferrara (2008).
However, analyses that treat policy changes as natural experiments may yield biased estimates of the impact of the policy (Besley and Case 2000). The estimated marginal effect of UBP might be the result of anunobserved variable that is correlated to the price level of UBP. For example, it might be correlated with theenvironmental awareness of the community. The omitted variable might also increase the probability of a municipality implementing policies such as a curbside recycling program, and this results in the marginal effectof UBP being overestimated. In this way, an exogeneity assumption of a UBP variable results in a biased
estimator.
There are two types of solution, one of which is to use an instrumental variable method to control unobserved variables. Kinnaman and Fullerton (2000), who estimated the demand equations for solid waste andrecycling considering the endogeneity of a unit-pricing and curbside-recycling policy, used the two-stage least4

square (2SLS) method to obtain an unbiased estimator. They estimated the price elasticity of garbage and recyclables, including the correction of endogenous local policy variables. They found that the reduction effectof waste generation is statistically significant, but the substitution effect of recyclable waste is not statisticallysignificant. Callan and Thomas (2006), Usui (2008) and Allers and Hoeben (2010) extended the approach of
Kinnaman and Fullerton. Detailed estimation results of each study are shown in Table 1.
Another type of treatment is to use panel data method. Van Houtven and Morris (1999), Dijkgraaf andGradus (2004, 2009), and Allers and Hoeben (2010) applied a fixed-effect model that is represented at communitylevel dummies. For example, Dijkgraaf and Gradus (2004) provide an empirical analysis of the effects ofunit/weight-based pricing of household waste using panel data for The Netherlands. Panel data analysis caneasily address the inconsistency of the self-selection bias by controlling for the unobserved intrinsic effect using
the fixed-effect model and the random-effect model. They separate out the effect of environmental activism,
which stems from the differences in citizen’s concern about the waste problem.

2.3

Long-run studies

There have been fewer studies of the long-run effect of UBP. In Japan, Amano et al.(1999) and Yamakawa
and Ueta (2002) examined the long-run reduction effect of UBP. The former analyzed four municipalities that
introduced UBP and compared waste generation per capita for several years before and after the introductionof UBP. They concluded that two municipalities increased waste reduction after the year in which UBP was
adopted, but they did not clarify why waste generation increased in their municipalities. Since they defined therebound as the difference between waste generation per capita before and after UBP, that rebound effect may
be partially due to the factor of demographic change, such as greater income or changes in the waste items
collected.
To analyze the long-run effect of UBP properly, demographic change needs to be controlled. Yamakawa
and Ueta (2002) also tried to clarify the long-run reduction effect of UBP by using cross-sectional data at themunicipality level. Their estimation strategy was to divide the price variable into two groups, depending on
the number of years elapsed after the introduction of UBP. Controlling the other demographic factors, theyfound that the price effect lasts more than 10 years on average. However, they have a problem of identificationin terms of two variables: magnitude of bag price and number of years elapsed, because they used a dummy
taking the value 1 if the municipality introduced UBP more than T years earlier, and 0 otherwise. As a result,
their long-run reduction effect contains an effect of price and an effect of years passed. A more serious problem
is the endogeneity problem discussed in the subsection on short-run studies. To sum up, these long-run studies
have some serious problems, such as sample representativeness, uncontrolled demographic change, and theendogeneity issue.

5Some studies used a panel data analysis to estimate the long-run effect while avoiding such problems.
Linderhof et al.(2001) is the first study that used a panel data set to estimate the effect of UBP. They usedpanel data for all households in a Dutch municipality in order to estimate the short-run as well as the long-run
price effects through weight-based pricing (WBP) for the amounts of both compostable and non-recyclable
household waste. They found evidence that the long-term price elasticity was 30% more elastic than its shortterm counterpart; moreover, they concluded that the long-term effect of WBP in compostable waste would
sustain in the future. They explained that the introduction of WBP boosted people’s environmental awareness,and people composted waste rather than putting it out on the curbside.
The study by Dijkgraaf and Gradus (2009) presumed a long-run effect with regard to total waste, unsorted
waste, compostable waste, and recyclables using the panel data of municipalities on The Netherlands. They
specified the continuity of reduction by applying a dummy variable, as Yamakawa and Ueta (2002) had; thatis, 1 for all years for each municipality that introduced a UBP system in year t. Moreover, they estimated theenvironmental activism effect, and it was decreasing over time, which means the most environment-friendly
municipalities implemented UBP or WBP pricing systems the earliest. In addition, they showed that the volume
effects of the different UBP systems were rather stable over time. These 2001 and 2009 studies are summarizedin Table 2.
These long-run panel studies, however, involve a few problems of their own. First of all, Linderhof
et al.(2001) use survey data on waste collection, collected from each individual for 42 months. So the survey might be affected by attrition bias in the context of social experiment (Levitt and List, 2009).5 Secondly,
their study may have a problem of randomization bias.6 Oostzaan, which they chose as the survey field, wasthe first municipality to introduce WBP in The Netherlands. This can be a source of bias, since households inthat setting are more eager to cooperate in waste reduction; there could be endogeneity in choosing the WBP
policy.7 This would lead to long-run estimates tending to be upper-biased because Oostzaan citizens may be
more environment-conscious. A third problem concerns the validity of dynamic panel analysis: the approachis valid only when the price changes frequently, yet to our knowledge the price seldom changes once a pricing
system starts.8 Therefore the elasticity of their estimates may be overestimated.
Dijkgraaf and Gradus (2009) have some of the same problems Yamakawa and Ueta (2002) have. They only
showed the long-run effect of WBP and UBP by means of dummy variables. To use dummy variables for the5

Attrition bias refers to systematic differences between the treatment and control groups because of differential losses of participants.

For further details about social experiment, see Levitt and List (2009).
6
Randomization bias in social experiment is a situation in which the experimental sample is different from the population of interest
because of randomization (Levitt and List, 2009).
7
Linderhof et al.(2001) said that “The largest political party in Oostzaan is Groen Links (Green Left), which is the most environmentally orientated political party in The Netherlands.”
8
In Japan the bag price introduced in 1995 stayed unchanged up to 2002 in all the municipalities where it was introduced.

6

pricing period is not good because it mixes together the effect of the price dummy and the effect of the year
dummy. So they cannot separate the price effect and the long-run effect.
Note that we have been referring to previous studies with regard to short-run and long-run price effects.
Short-run studies have mainly been interested in detecting evidence of the substitution effect of UBP, using
techniques for overcoming the endogeneity problem; however, few studies have focused on examining thesource-reduction effect of UBP. Long-run studies have mainly been interested in identifying the number ofyears for which the waste substitution effect continues.
Our estimation strategy is to use a method for testing the Environmental Kuznets Curve (EKC) hypothesis.
This enables us to interpret the results, both short-run and long-run, simultaneously. Using panel data, we canseparate the effect of the price level of UBP and the effect of the number of years elapsed.
We also consider the problems of previous studies, such as Linderhof et al.(2001) and Dijkgraaf and Gradus(2009). Firstly, we adopt panel data for every city in Japan regardless of whether it has introduced UBP or not,whereas Linderhof et al.used only one municipality’s data. Secondly, we use actual bag price data and its
introduced year data, whereas Dijkgraaf and Gradus (2009) used only a dummy variable of introduction.
It is also important to point out that this paper is the first study to clarify why the long-run price effect,
including the reduction and substitution effects, continues or not. In particular, we compare the difference inlong-run effects between income groups. Because of differences in opportunity cost of time or environmental
awareness, there might be heterogeneity in their rebound effect.
In the next section we explain the estimation method used to identify the short-run and long-run priceeffects.

3

Estimation Strategy

In this section we show our strategy to estimate the short-run and long-run reduction and substitution effects of
the UBP policy to affect household demand for a waste collection service. We employ a regression model of apanel data analysis, and apply a fixed-effect model. By doing this we can correct the bias caused by an omitted
variable, such as the introduction of a price and a recyclables collection, which have been perceived as being
endogenous in cross-section models.9
First of all, we need to split the short-run and long-run effects of UBP on waste collection. We define
the explanatory variable, ln p, as the natural log of bag price per bag or tag (yen per 40-50 liters bag), and y
9

A fixed-effect model is not free of problems. Another type of endogeneity problem might arise if there is spatial dependency in the

introduction of the waste policy. To correct this bias, Allers and Hoeben (2010) have treated the spatial auto-correlation. We know theimportance of such a treatment, but we cannot apply that estimation method. Since we use only city-level data, the adjacency relation
among municipalities is imperfect. Our study is, therefore, obliged to abandon the correction of spatial autocorrelation. However,municipal fixed effects may partly mitigate this type of endogeneity problem.

7

represents the number of years that have elapsed since the introduction of UBP, which is important to detect
the short-run and long-run reduction effects because they are believed to be dependent on the years that haveelapsed since the introduction of UBP. To capture the dynamic effect of UBP, we borrow the method used totest empirical evidence for the existence of an Environmental Kuznets Curve (EKC) hypothesis (Dinda, 2004),
and introduce interaction terms of ln p times y and ln p times y squared. We define the demand for a waste
collection service and assume a double log model as follows:2
ln wsit = α + β1 ln pit + β2 ln pit × yit + β3 ln pit × yit+ γ ′ Zit + ai + λt + uit(1)

where the dependent variable, ln ws , is the natural log of the amount of s types of waste per capita per day(gram), whose s is total waste, unsorted waste, or recyclable waste; Z is a vector of other demographic variables
that have an influence on waste generation. Further, the subscripts i and t indicate the municipality and the time,
respectively; a and λ are the unobserved heterogeneity that is invariant across time and cross-section change,
respectively; β and γ ′ are parameters and a parameter vector, respectively, in this linear model. We assume theerror term u is normally and independently distributed with mean as 0, and variance σ 2 .
If we partially differentiate ln ws with respect to ln p in equation 1, we get the price elasticity of demand
for s types of waste collection service, ϵ,∂ ln ws= ϵ = β1 + β2 y + β3 y 2 .
∂ ln p(2)Note that this elasticity depends on the number of years and its square. We can give a diagnosis using estimatedsigns of parameters. For further details, see Appendix A. For example, there is a rebound effect of waste
reduction if the signs of parameters are a case (iii) in Appendix A, such as (β1 < 0, β2 > 0 and β3 = 0),(β1 < 0, β2 = 0 and β3 > 0), or (β1 < 0, β2 > 0 and β3 > 0).
The rebound effect can be attributed to awareness erosion or phenomena like “Seattle stomp.” Awarenesserosion occurs when the effect of UBP erodes over time if households get used to pricing waste and go back totheir old behavior (Dijkgraaf and Gradus, 2009). “Seattle stomp” is a well-known phenomenon wherein people
densely compact their waste to reduce the number of bags they have to pay for, thereby increasing the bulk
density of waste per bag (Fullerton and Kinnaman, 1996).
To judge how shapes validate in the long run, an F -test and t-test are applied to estimated parameters. We
confirm them in Section 5.

4

Data

In this section we define the dependent and other demographic variables and then explain our panel data.

8

4.1

Definition of the Dependent Variable

We want to clarify the short-run and long-run effects of waste reduction and waste recycling by citizens. To
examine this, we focus on different types of waste, namely total waste, unsorted waste, and recyclable waste.
We use the amount of these three types of waste per capita per day as our dependent variable.
Total waste generation

UBP will bring about waste reduction, so we analyze its effect by defining waste

generation as the dependent variable. Total waste generation is the sum of combustible waste, noncombustible
waste, and recyclables collected by citizens. In the UBP literature, the papers that clarified the reduction effectof total waste generation are Van Houtven and Morris (1999), Dijkgraaf and Gradus (2004, 2009), and Allers
and Hoeben (2010). Of the long-run studies, Dijkgraaf and Gradus (2009) is the only one that examines the
reduction effect of total waste. To clarify the reduction effect, therefore, both the short run and long run are
valuable, especially so we can estimate long-run price elasticity.
Unsorted and recyclable waste

We also define unsorted waste and recyclable waste in order to analyze theamount of unsorted waste redirected into recyclable waste in the short run and long run. Unsorted waste iscomposed of combustible waste and noncombustible waste. Recyclable waste is composed of glass bottles,
plastic containers and packagings, PET bottles, cans (aluminum and steel cans), and paper containers andpackaging. Data on the amount of recycling in each municipality is available since 1995, before the Containers
and Packaging Recycling Law has implemented in 1997. Normally a bag price is levied on unsorted waste but
not on recyclable waste.

4.2

Other Demographic Variables

Our other demographic variables consist of: income per capita, population density, household size, age structure, and other price dummy. lnPopd represents the population density (persons/km2 ). This variable will be
a proxy of housing space, since it is more difficult to stock recyclable waste in a smaller house. lnIncome
represents taxable income per capita (in 1995 Japanese yen). They can be regarded as a proxy for the amountof consumption and time cost to work on waste separation. lnFamily, the household size, may include a scale
merit of consumption, because a large household size will decrease per capita consumption of shared goods
such as newspapers, and therefore waste generation. Further, Under 4 and Over 65 represent the ratios of population in each municipality under age 4 and over age 65, respectively. Dotherprice is a dummy variable for othertypes of UBP. We cannot introduce educational variables that become a proxy for the intelligence of citizens,
because such data is unavailable. Monetary variables, p and Income, are needed to control for the effects ofinflation or deflation. We adjust for inflation using the FY 1995 consumer price index.

9

4.3

Data Sources

We merged three categories of municipal panel data: 1) waste data, 2) bag price data, and 3) demographic
data. First, we used data on 712 cities taken from data on 3200 municipalities provided by the Japan Waste
Management Association (1995–2002)–data pertaining to the total waste, unsorted waste, and recyclable waste
for each city spanning an eight-year period from fiscal years 1995 to 2002.
Second, we used the data on waste pricing and the year each city introduced UBP gathered by Yamaya(2006). Yamaya worked out a comprehensive survey for all 712 cities and 23 wards of Tokyo by means of questionnaires administered through the mail and by telephone in February FY 2005. For cities that were adopting
UBP, he also asked the price of garbage bags for 40-50 liters of waste per garbage bag or a corresponding tag
used to levy a fee on the waste generated.
Figure 2 presents a histogram of the data for such prices. The vertical axis represents the number ofcities, and the horizontal axis represents the price per 40–50 liters of waste per garbage bag.10 The histogramshows that the mode is 30–40 yen per bag, and the median is also the same. Mostly UBP is implemented for
combustible and noncombustible waste. The price level for municipalities without UBP are set at 5 cents perbag, because people must buy any bag to dispose their waste (Usui 2008). Figure 3 presents a histogram ofthe years when UBP was introduced; the vertical axis on the left side gives the number of cities and the rightside gives the percentage of cities that introduced UBP; the horizontal axis represents the year of introduction.
The number of cities adopting UBP increases steadily, and 23% of them have introduced UBP. Finally, otherdemographic data was obtained from Asahi Shinbun (2003), which is a collective database containing data for
all municipalities.
The descriptive statistics and the definitions of the variables are presented in Table 3. A total of 712 cities
are used as the population for this study. We have used eight years of data, from FY 1995 to 2002. This isbecause data on waste generation became available after 1995 and there has been a considerable increase inmunicipal mergers after 2002. Excluding missing values, we used 665 (cities) × 8 (years) unbalanced panel
data for estimation.

5

Estimation Result

We estimate Equation (1) by applying the panel model. In our discussion of the estimation results, we only
consider the fixed-effect estimates.11 Standard errors in the fixed-effect model are corrected for heteroscedas10
11

The currency exchange rate is as follows: 100 Japanese yen equals approximately 1 euro.
We carefully chose our panel model from among three models: pooled OLS, fixed effect, and random effect. We employedsystematic model selection, lest the wrong model generate inconsistent estimates. We also employed the F -test, the Breusch-Pagan
Lagrange Multiplier test (BP test), and the Hausman test (see Hsiao, 2002). Our test strategies were: 1) to choose between the pooled

10ticity.
Table 4 shows our estimation results. First, we show the test result of the long-run effect. The estimation result indicates that long-run elasticity was gradually eroding in total waste and unsorted waste, while an inverted
U-shaped relationship was found in recyclable waste as more and more years elapse after UBP’s introduction.
First of all, we consider the three price variables: ln p, y× ln p, and y2 × ln p. Since F -statistics in total waste,
F (3, 664) = 21.2, are sufficient to reject the null hypothesis, H0 : β1 = β2 = β3 = 0, at the conventional levelof significance, we reject it. Likewise, F -statistics in unsorted waste, F (3, 664) = 25.4, and recyclable waste,
F (3, 664) = 10.6, are also statistically significant.
Next, we examine which combination of the coefficients in Appendix A fits the data. Since the estimatedresult shows β1 < 0, β2 = 0, and β3 > 0, there is a monotonically increasing relationship between longrun elasticity and number of years elapsed: the more years that pass after the introduction of UBP, the more
the waste reduction effect erodes. These results mean there are rebound effects with respect to total waste andunsorted waste, which may be interpreted as caused by UBP, “Seattle stomp,” or awareness erosion. Meanwhile,the long-run price elasticity in recyclable waste shows a U-shaped relationship between ∂ ln w/∂ ln p and y.
This implies that the long-run elasticity of recyclable waste seems to gradually decrease for up to 12 years,
then gradually increase. This suggests a mixed effect: that the learning effect becomes stronger than awarenesserosion in the long run. These results suggest that recycling is successfully promoted by adoption of UBP anda recycling law, while reduction of total waste generation is weak because UBP seems ineffective in changing
people’s purchasing behavior.
Figure 4 shows the predicted values of elasticity for total waste, unsorted waste, and recyclable waste
calculated on the basis of Equation 2. The vertical axis represents the price elasticity of total waste, unsorted
waste, and recyclable waste, and the horizontal axis is the number of years that have passed since the pricepolicy was introduced. The predicted value of curves are, as calculated above, gradually increasing in total
waste and unsorted waste, but they do not seem to exhaust the price elasticity of waste generation as long as atleast 30 years elapse. Likewise, though the estimated result suggests an inverted U-shaped line for recyclable
waste, it appears as a straight line, at least within the 30 years of the actual sample range. This result is inline with the empirical findings of Yamakawa and Ueta (2002) and Dijkgraaf and Gradus (2009). For example,
Yamakawa and Ueta (2002) found that the reduction effect of UBP on unsorted waste sustains at least 10 years.
Dijkgraaf and Gradus (2009) also suggest that the reduction effect of total waste sustains over 7 years.
The estimation results of demographic variables are given in Table 5. In summary, we find that models
on total waste and unsorted waste have the same signs for all variables. The explanatory variable population
OLS and fixed-effect models, we applied the F-test; 2) to choose between the pooled OLS and random-effect models, we applied the
BP test; 3) to choose between the fixed-effect and random-effect models, we applied the Hausman test. In the end we selected thefixed-effect model.

11density does not have a significant effect for any equation. The coefficients of income per capita (lnIncome) are
statistically significant at the conventional level of significance and positive in total waste and unsorted waste
equations. The variable is affected by many channels: (1) it is a proxy for the opportunity cost of time (negative
relation with waste generation); (2) it is a proxy for the amount of consumption (positive relation with waste
generation); and (3) it might be a proxy for the level of education level.12 Due to these combined channels,
the sign of the coefficient is positive in total waste and unsorted waste equations. The average household sizein each city, lnFamily, is not significant for any equation. As suggested in Callan and Thomas (2006), thecoefficient of the average household size lnFamily is negative, but our result is not statistically significant. The
ratio of the population variable Under 4, is significant and negative in all equations. The coefficients of Under
4 for total waste and that for recyclable waste are both negative signs. This result suggests that householdswith children under age 4 are relatively better at waste reduction than at recycling. The ratio of the population
variable Over 65 is significant in all equations, and negative in the total waste and unsorted waste equations but
positive in the recyclable waste equation. This result suggests that retired people may have considerably more
free time and can therefore reduce waste and separate out recyclable waste objects.
Dummy variables of recyclable materials capture the effect of a recycling policy independent of any economic incentive produced by UBP. We find the coefficient of the collection dummy for plastic containers is
statistically significant and negative in total waste and unsorted waste. This effect may be of interest to public
administrators. It means that there is a voluntary reduction effect in total waste, because people reduce theirpurchases of, or refuse to purchase, plastic containers and packaging, even without an economic incentive,
while in unsorted waste, this effect is interpreted as the result of reduction and substitution effects. In the equations of recyclable waste, we find all collection dummy variables except PET bottle are statistically significant
and positive. These results can be interpreted to mean that there is evidence of a willingness to contribute torecycling activities. Dotherprice is a dummy variable for the introduction of other types of a variable pricing
scheme; it is significant in recyclable waste.
All the year dummies are significant and positive in total waste and unsorted waste (compared to the baseline of Year 95), but not significant in recyclable waste collection. These results suggest a trend toward a
gradual increase in waste generation, while recyclable waste is constant in comparison with the flow of waste
generation. To sum up, our results show that there is a rebound effect in waste generation, but the size of theeffect is considerably small.

12

Because of a lack of data with respect to education levels in Japanese cities, we cannot control for the effect of educational levelon waste generation.

126

Discussion

In this section we focus on income level as a possible factor affecting waste generation and recyclable collection
in different ways, in the short and long run, because these activities may be affected by opportunity cost, or
education level, as discussed in the variable of income per capita in Section 5. It is plausible that those who
earn a high income do not respond to the bag price with activities aimed at recycling, because time cost may
be relatively higher. On the other hand, those who earn high incomes have a higher level of education, so they
may be willing to join such activities. In order to check this, we divide our sample into high and low income
based on the median income in 2002. We use the same estimation model described in Section 5 and apply it toeach income group. To sum up the estimation results: the high-income group responds to the bag price throughwaste reduction activity (both short- and long-run), but not through recycling activity, while the low-income
group prefers recycling activity over the long run, but does not continue waste reduction over the long run.
In Table 5 the first, third, and fifth columns are the coefficients and standard errors of the high-income
group, and the second, fourth, and sixth columns show those of the low-income group.13 The results for the
total waste and unsorted waste equations are basically the same as those of Section 5. We do not find a large
difference between the two income groups. The estimated result shows β1 < 0, β2 = 0, and β3 > 0. Thismeans that there is a monotonically increasing relationship between the long-run elasticity and the number ofyears elapsed. However, in the equation of recyclable waste (the fifth column), people who earn a high income
do not react to a bag price by sorting out recyclables in the short run and do even less recycling in the longrun. This is interpreted to mean that high-income people consider that the opportunity cost of time involved
in recycling activity is more expensive than paying the bag price, and so they do not react to the bag price.
Low-income people, on the other hand, are price elastic in the equation of recyclable waste (the sixth column),
because their opportunity cost is relatively smaller than that of high-income people.
Apart from the price effect, we consider the dummy variables for recyclable waste collection. The coefficients for all recyclables except PET bottles are statistically significant. This means the high-income group
voluntarily sorts recyclables regardless of the short- and long-run effects of bag price. This is important, because the evidence shows a willingness to contribute to recycling. For the low-income group, however, only
the coefficients for metal cans and glass collection (sixth column) are significant. These items were being collected before UBP was introduced in most city, and people are used to collecting them without any economic
incentive. Therefore, UBP is effective for the low-income group to induce them to collect and separate otherrecyclables in terms of both the short run and the long run.
To sum up, our estimation results suggest that responses to recycling activities differ between income
groups. For the high-income group, the activity of recycling has not been promoted through an economic
13

The number of observations are different between models because of missing values.

13incentive; they are voluntarily willing to start recycling. In contrast, recycling activity on the part of the lowincome group is strongly motivated by the implementation of UBP, and they continue their recycling activity
for years, stimulated by the economic incentive in the form of UBP.

7

Conclusion

This paper suggests the effect of UBP on waste reduction and recycling lasts a considerable number of years.
Our study expands the findings of previous studies in two directions. First, we can estimate the long-run
elasticities of total waste, unsorted waste, and recyclable waste. The estimation results show a monotonicallyincreasing relationship, which implies that long-run elasticity gradually erodes over time, but the size of theeffect is very small. The long-run elasticity of recyclable waste is an inverted U-shape, with a turning point
12 years after the introduction of UBP, but the curve is also almost flat. For this reason we conclude that thelong-run effect is almost the same as the short-run effect. Second, we examine the long-run effect of UBP
between income levels and find differences in income do affect the long-run substitution effect, and that the
low-income group responds to UBP by contributing to recycling activity.
Our results have significant policy implications. First of all, the rebound effect is very small and so it canbe ignored. Second, the substitution effect is stronger for the low-income group in both the short and long run,so adoption of UBP will be effective for municipalities with a large low-income population. Third, while thehigh-income group does not care about economic incentives, they voluntarily contribute to recycling efforts
wherever a system of recycling is in force. These second and third policy implications suggest that a mix of
UBP and recyclable waste collection policies complement each other, since their effects differ between income
groups. They suggest that we need a different public relations strategy for different income groups if we wish
to promote waste reduction and recycling.

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15

[18] Hong, S. and M. Adams (1999) “Household Responses to Price Incentives for Recycling: Some Further
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[28] Ministry of Environment, Japan (2001) “Nihon no Haikibutsu Shori, 1999 [Solid waste management inJapan in 1999].”(In Japanese).
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16[31] Miranda, M.L. and D.Z. Bynum (1999) “Unit Based Pricing in The United States: A Tally of Communities, Report Submitted to The U.S.” Report submitted to the U.S. Environmental Protection Agency.
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[38] Reschovsky, J.D. and S.E. Stone (1994) “Market Incentives to Encourage Household Waste Recycling:
Paying for What You Throw Away,” Journal of Policy Analysis and Management, 13, 120–139.
[39] Saltzman, C., V.G. Duggal and M.L. Williams (1993) “Income and the Recycling Effort: A Maximization
Problem,” Energy Economics, 15, 33–38.
[40] Strathman, J.G., Rufolo, A.M. and Mildner, G.C.S. (1995) “The Demand for Solid Waste Disposal,” Land
Economics, 71, 57–64.
[41] Suwa, T. and T. Usui (2007) “Estimation of Garbage Reduction and Recycling Promotion under the Containers and Packaging Recycling Law and Garbage Pricing,” Environmental Economics and Policy Studies, 8, 239–254.
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17[43] Van Houtven, G.L., and G.E. Morris, (1999) “Household Behavior under Alternative Pay-as-You-Throw
Systems for Solid Waste Disposal,” Land Economics, 75, 515–537.
[44] Wertz, K. (1976) “Economic Factors Influencing Households’ Production of Refuse,” Journal of Environmental Economics and Management, 2, 263–272.
[45] Yamakawa, H. and K. Ueta (2002) “Waste Reduction through Variable Charging Programs: Its Sustainability and Contributing Factors,” Journal of Material Cycles and Waste Management, 4, 77–86.
[46] Yamaya, S. (2006) “Katei Gomi Yuryoka no Genjo to Kadai [Present state of household waste and whatneeds to be done]. ” Life and Environment, 51, 13–20. (In Japanese).
[47] Yamaya, S. (2007) Gomi-Yuryoka [UBP of waste], Maruzen. (In Japanese).
[48] Yang, H.L. and R. Innes (2007) “Economic incentives and residential waste management in Taiwan: An
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18

A

Testing the Long-Run EffectPartially differentiating ln w with respect to ln p, we get∂ ln ws= ϵ = β1 + β2 y + β3 y 2 .
∂ ln pThis equations indicates the price elasticity of demand for total/non-recyclable/recyclable waste, ϵ, which depends on the number of years and its square.
Here, to test the long-run reduction effect, we partially employ the method used by Dinda (2004) in orderto detect evidence for the Environmental Kuznets Curve (EKC) hypothesis. We now show the implications forthe different signs of the parameters. Please note that the sign of the parameter for β1 is in reverse for the caseof recyclables.
(i) (β1 = β2 = β3 = 0). A flat pattern or no relationship between ∂ ln w/∂ ln p and y. This implies that the
short-run and long-run elasticity is zero.
(ii) (β1 < 0 and β2 = β3 = 0). A flat pattern or no relationship between ∂ ln w/∂ ln p and y. However, this
implies that the short-run and long-run reduction effects are the same.
(iii) (β1 < 0, β2 > 0 and β3 = 0), (β1 < 0, β2 = 0 and β3 > 0), or (β1 < 0, β2 > 0 and β3 > 0). A
monotonically increasing relationship or a linear relationship between ∂ ln w/∂ ln p and y. This implies
that the long-run elasticity is gradually decreasing.
(iv) (β1 < 0, β2 < 0 and β3 = 0), (β1 < 0, β2 = 0 and β3 < 0), or (β1 < 0, β2 > 0 and β3 < 0).
A monotonically decreasing relationship between ∂ ln w/∂ ln p and y. This implies that the long-run
elasticity is gradually increasing.
(v) (β1 < 0, β2 < 0 and β3 < 0). An inverted U-shaped relationship between ∂ ln w/∂ ln p and y. This implies
that the long-run elasticity is gradually decreasing up to a certain period and gradually increasing afterthat period.
(vi) (β1 < 0, β2 < 0 and β3 > 0). A U-shaped relationship between ∂ ln w/∂ ln p and y. This implies thatthe long-run elasticity is gradually increasing up to a certain period and gradually decreasing after that
period.

19

Waste generation

Rebound effect
r0
r1
Inverse rebound effectt1

Figure 1: Definition of long-run effect

20

t

45
40

Number of cities

35
30
25
20
15
10
5
0

Bag price (yen/40-50 liters)

Figure 2: Histogram of bag price

20
18

25%
Number of cities

16
14

20%
Ratio of cities introducing UBP

12

15%

10
8

10%

6
4

5%

2
0

0%

Figure 3: Histogram of years UBP was introduced

21

0.15
0.10
Price elasticity

Total waste
Unsorted waste

0.05

Recyclable waste
0.00
0

5

10

15

20

25

30

-0.05
-0.10

The year of introduction of unit-based pricing

Figure 4: Predicted values of long-run price elasticities
Note: Predicted values are based on estimation results, though insignificant coefficients are dropped.

22

Table 1: Literature survey: short-run effect of unit-based pricing
StudyCountry

SystemOwn-price

Cross-price

Number ofelasticity

elasticity

years

Aggregate municipal data
Allers and Hoeben (2010)

Netherlands

Weight (unsorted waste)

-0.65

-

10

Allers and Hoeben (2010)

Netherlands

Bag (unsorted waste)

-0.39

-

10

Callan and Thomas (1997)

USA

Mixed

-

0.07

1

Callan and Thomas (2006)

USA

n.a.

-0.58

0.05

1(?)

-0.62

-

3

-

0.12

3

Dijkgraaf and Gradus (2004)

Netherlands

Bag (unsorted waste)

Dijkgraaf and Gradus (2004)

Netherlands

Bag

Huang et al.(2011)

NW, USABag (total waste)

-0.62

-

1

Jenkins (1993)

USA

Volume-0.12-

Variousa)

Kinnaman and Fullerton (2000)

USA

Bag

<0

n.s.

1

Podolsky and Spiegel (1998)

USA

Volume (total waste)

-0.39

-

1

Strathman et al.(1995)

USA

Volume

-0.45

-

?

<0

> 0 b)

1

-

0.07

1

Suwa and Usui (2007)

Japan

Bag

Usui (2008)

Japan

Bag (glass botteles)

Usui (2008)

Japan

Bag (PET bottlles)

-

0.12

1

Usui (2008)

Japan

Bag (cans)

-

0.15

1

Usui (2008)

Japan

Bag (paper containers)

-

n.s.

1

Van Houtven and Morris (1999)

USA

Bag (total waste)

-0.15

-

39 (months)

Wertz (1976)

USA

Volume

-0.15

-

1

Yamakawa and Ueta (2002)

Japan

Bag

<0

-

1

Fullerton and Kinnaman (1996)

USA

Bag

-0.23

-

2

Household surveys

Fullerton and Kinnaman (1996)

USA

Weight

-0.06

0.07

2

Hong (1999)

Korea

Bag

-0.15

0.46

1

Hong et al.(1993)

USA

Volume

n.s.

>0

?

Jenkins et al.(2003)

USA

Volume (by material)

-

n.s.

1

Linderhof et al.(2001)

Netherlands

Weight (compostable)

-1.39

-

42 (months)

Linderhof et al.(2001)

Netherlands

Weight (unsorted)

-0.34

-

42 (months)

Nestor and Podolsky (1998)

USA

Volume

(?)

-

(?)

Reschovsky and Stone (1994)

USA

Bag (recyclable)

-

n.s.

1(?)

Reschovsky and Stone (1994)

USA

Bag (compostable)

-

>0

1(?)

Note: This table is based on Jenkins et al.(2003) and Dijkgraaf and Gradus (2004), and is updated by the authors;n.a. means not available.; n.s. means not significant.
a) : This paper used various numbers of years for each municipality.

23

Table 2: Literature survey: short-run and long-run effects of unit-based pricing
Study

Linderhof et al.(2001)

Dijkgraaf and Gradus (2009)

Geographical region

Household data, Oostzaan, The Netherlands

Municipalities, The Netherlands

Dependent variable

Compostable and non-recyclable waste

Total waste, unsorted, compostable, andNumber of periods

Monthly panel data (42 months)

Yearly panel data (8 years)

Pricing data: dummy

ActualDummy variable

Type of pricing

Weight-based pricing

Weight/bag/frequency/volume-based pricing

Years of UBP

No, but using lagged dependent variable

Year dummy: 1,2, ,,, , over 8 years

Evidence

Compostable waste: short-run elasticity:

Total waste introduced bag pricing:-1.10, long-run elasticity -1.39.

short-run reduction effect is smaller thanNon-recyclable waste: short-run elasticity:

long-run (more than 7 years) reduction-0.26, long-run elasticity -0.34.

effect.

recyclable

or actual?

Table 3: Estimation results: Total waste, unsorted waste, and recyclable waste

Definition

N =Variable

Mean

S.D.

Min

Max

ln wtotal

6.84

0.22

5.54

7.65

Natural log of volume of total waste collection per capita per day (grams)

5314

ln wunsorted

6.69

0.25

4.02

7.64

Natural log of volume of unsorted waste collection per capita per day (grams)

5314

ln wrecyclable

4.67

0.72

-1.65

6.75

Natural log of volume of recyclable collection per capita per day (grams)

5250

ln p

1.90

0.75

1.54

4.66

Natural log of price per bag or tag (yen per 40–50 liters)

5320

ln p × y

5.21

18.66

0

124.91Natural log of price × number of years since the introduction of the unit-based pricing

5320

ln p × y 2

106.71

483.1704121.89

Natural log of price × squared number of years

5320

DOtherprice

0.04

0.19

0

1

Dummy for other pricing scheme than UBP

5320

DPaper

0.65

0.48

0

1

Dummy for collecting papers

5320

DMetal

0.83

0.38

0

1

Dummy for collecting metals

5320

DGlass

0.82

0.38

0

1

Dummy for collecting glasses

5320

DPET

0.49

0.50

0

1

Dummy for collecting PET bottles

5320

DPlastic

0.17

0.38

0

1

Dummy for collecting plastic containers

5320

DOther

0.42

0.49

0

1

Dummy for collecting other recyclables

5320

lnPopd

6.66

1.29

2.96

9.53

Natural log of population density (person/km2 )5313

lnFamily

0.29

0.22

-0.50

1.02

Natural log of average household size

5313

lnIncome

1.04

0.12

0.54

1.41

Natural log of income per capita (1 million yen)

5313

Under 4

0.05

0.01

0.02

0.08

Ratio of population under age 4

5313

Over 65

0.17

0.05

0.06

0.36

Ratio of population over age 65

5313

24

Table 4: Estimation results: Total waste, unsorted waste, and recyclable waste
VARIABLES
ln p
ln p × yln p × y 2
lnPopdlnIncome
lnFamily
Under 4
Over 65
DOtherprice
DPaperDMetalDGlassDPET
DPlastic
DOtherYear 96
Year 97
Year 98
Year 99
Year 00
Year 01
Year 02
Constant(1)
ln wtotal(2)
ln wunsorted(3)
ln wrecyclable-0.041***(0.007)-0.000001(0.000020)
0.000003***(0.000001)-0.022(0.055)0.254***(0.090)-0.155(0.183)-3.546***(1.214)-1.957**(0.773)0.018(0.021)
0.003(0.006)0.003(0.009)0.002(0.010)-0.003(0.005)-0.012**(0.005)-0.001(0.004)0.028***(0.006)0.045***(0.012)0.082***(0.017)0.117***(0.021)0.154***(0.026)0.185***(0.030)0.198***(0.035)7.400***(0.260)-0.062***(0.008)0.000029(0.000024)
0.000003**(0.000001)-0.039(0.050)0.266**(0.105)-0.072(0.191)-3.143**(1.508)-3.245***(0.882)0.002(0.029)-0.008(0.008)-0.015(0.011)-0.012(0.012)-0.011(0.007)-0.033***(0.006)-0.007(0.005)0.033***(0.006)0.049***(0.013)
0.093***(0.019)0.132***(0.024)0.165***(0.029)0.205***(0.035)0.223***(0.040)7.422***(0.288)0.124***(0.027)-0.000287***(0.000081)
0.000012**(0.000005)
0.079(0.219)0.801(0.614)-1.166(0.965)-17.536***(6.612)
10.122**(4.406)0.212***(0.066)0.129***(0.040)0.129**(0.059)0.252***(0.059)0.045(0.030)0.070***(0.026)
0.048***(0.019)0.006(0.034)0.013(0.065)-0.025(0.096)-0.017(0.123)0.059(0.155)0.044(0.190)-0.002(0.225)4.081***(1.474)N
5,303
Adjusted R2
0.267
Groups665
Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

25

5,303
0.117
665

5,239
0.388
665

Table 5: Estimation results: Total waste, unsorted waste, and recyclable waste(1)

VARIABLES
ln p
ln p × yln p × y 2
lnPopdlnIncome
lnFamily
Under 4
Over 65
Dotherprice
DPaperDMetalDGlassDPET
DPlastic
DOtherYear 96
Year 97
Year 98
Year 99
Year 00
Year 01
Year 02
Constant(2)
ln wtotalHigh income
Low income-0.017*(0.010)-0.000013(0.000041)
0.000003**(0.000001)-0.002(0.037)0.146(0.130)-0.329(0.203)-3.526***(1.357)-4.596***(1.310)0.017(0.025)0.001(0.007)0.008(0.017)-0.002(0.019)0.000(0.008)-0.015***(0.005)0.002(0.004)0.044***(0.007)0.074***(0.017)0.120***(0.022)0.154***(0.026)
0.198***(0.031)
0.239***(0.038)0.258***(0.044)7.882***(0.361)-0.050***(0.008)-0.000015(0.000023)
0.000003***(0.000001)-0.034(0.200)0.261*(0.134)0.208(0.426)-1.318(2.318)-1.200(1.121)
0.005(0.034)0.004(0.011)-0.001(0.011)-0.001(0.012)-0.002(0.008)-0.008(0.008)-0.005(0.007)0.026***(0.010)0.049**(0.020)0.085***(0.030)0.130***(0.037)0.171***(0.044)0.206***(0.051)0.224***(0.058)6.803***(0.482)N
2,673
Adjusted R2
0.299
Groups336
Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

2,630
0.273
329

(3)

(4)
ln wunsorted
High income
Low income

(5)

(6)
ln wrecyclable
High income
Low income-0.032**(0.015)-0.000021(0.000055)
0.000004*(0.000002)-0.032(0.035)0.094(0.144)-0.334(0.209)-2.754(1.769)-6.976***(1.421)
0.009(0.036)-0.007(0.009)-0.016(0.020)-0.014(0.021)-0.008(0.009)-0.037***(0.007)-0.007(0.006)
0.051***(0.008)0.085***(0.018)0.143***(0.024)0.183***(0.029)0.223***(0.035)0.281***(0.043)0.307***(0.051)8.110***(0.390)-0.073***(0.009)0.000017(0.000027)
0.000002*(0.000001)
0.103(0.215)0.269*(0.159)0.562(0.537)-0.618(2.809)-1.753(1.266)-0.043*(0.026)-0.011(0.013)-0.016(0.013)-0.018(0.014)-0.008(0.011)-0.027**(0.011)-0.009(0.009)0.032***(0.011)
0.056**(0.022)0.099***(0.034)0.148***(0.042)0.185***(0.052)0.224***(0.061)0.247***(0.070)6.584***(0.603)0.034(0.040)0.000003(0.000116)-0.000012**(0.000006)
0.079(0.178)1.531**(0.711)0.558(0.817)-9.322(6.141)
13.208***(4.337)0.158**(0.062)0.137***(0.041)0.152**(0.065)0.139**(0.068)0.047(0.030)0.085***(0.026)0.049**(0.021)0.021(0.024)-0.000(0.052)-0.025(0.073)0.018(0.088)0.109(0.113)0.075(0.135)0.074(0.162)1.651(1.473)0.140***(0.034)-0.000369***(0.000101)
0.000016***(0.000006)-0.544(1.077)0.311(0.962)-7.753***(2.859)-21.383*(12.688)
3.300(7.658)0.365**(0.170)0.115(0.071)0.144*(0.084)0.287***(0.083)0.030(0.048)0.068(0.047)0.045(0.031)-0.040(0.066)-0.044(0.127)-0.122(0.185)-0.177(0.239)-0.135(0.298)-0.162(0.365)-0.275(0.432)11.757***(3.805)

2,673
0.145
336

2,630
0.132
329

2,671
0.411
336

2,568
0.397
329

26