How should Bangkok Government Promote Old
Motorcycle Retirement to Reduce Air Pollution:
A Fee System or a Trade-in Program?
Akinori TOMOHARA*
Assistant Professor
Department of Economics
The City University of New York at Queens College

Abstract
This paper examines the relationship between the government’s financial incentive and market
responses among motorcycle owners using Bangkok’s motorcycle retirement program. Whilethe literature studies only a “Cash for Clunkers” scheme, this quasi-natural experiment allows us
to compare owners’ preferences for alternative schemes. The owners prefer a fee system to a
trade-in program since they worry that the trade-in program may confiscate their means of living
and/or transportation. Factors such as household income are shown to be the determinants ofcompensation amount for losing motorcycles. Owners of old motorcycles are members of lowincome households. We conclude that old motorcycle retirement will be promoted by providing
financial safeguard and substitutive transportation methods.

Keywords: Air Pollution; Contingent Valuation Methods (CVM); Vehicle Retirement;
Willingness to Pay (WTP); Willingness to Accept (WTA)
JEL classification: C8; Q2*

Corresponding Author. 65-30 Kissena Blvd., Flushing NY 11367. Tel: (718) 997-5456; Fax: (718) 997-5466;e-mail: jujodai@yahoo.com

1. Introduction
Growing concern about environmental degradation has been challenging the traditional conceptof economic growth in many countries.

Air pollution from rapidly increasing energy

consumption and motor vehicle usage is an example of how concern for the environment leads toreevaluation of economic growth goals and achievements. Specifically, severe air pollution andassociated health problems are serious policy issues faced by several cities in developing
countries. Air pollution deteriorates the quality of life through respiratory illness and premature
mortality risk. Poor health lowers labor productivity and impairs economic growth of afflicted
countries.
Bangkok is one city suffering from air pollution caused by old motorcycles. In the past
few years, research has been warning of air pollution’s negative health effects. Research has
also studied the costs of illness in Bangkok (e.g., Chestnut et al., 1997). Since Bangkok
contributes nearly half of Thailand’s GDP, reduction of air pollution is a pressing problem facedby the government of Bangkok. Air pollution control improves health conditions of Bangkok
residents. Improving citizens’ health can help to achieve the goals of economic growth.
This paper examines the incentive mechanism of an old motorcycle retirement programthat aims to reduce air pollution in Bangkok. Vehicle emission control policies have been
explored thoroughly in the literature (Gruenspecht, 1982; Kwoka, 1983; Blair et al., 1984;
Bresnahan and Yao, 1985; Greene, 1991; Kling and Rubin, 1991; Kling, 1994). However, few
academic works have explored a “Cash for Clunkers” approach (Alberini et al., 1995; Hahn,
1995; Alberini et al., 1996; Innes, 1996).

As market-based approaches have spurred inenvironmental regulation, policy makers’ interests have shifted from command and control
approaches such as emission standards and inspection and maintenance programs to pollution

2

taxes or subsidies. A specific example is a voluntary old vehicle retirement program becoming a
focus of air pollution policy (Innes, 1996; Fontana, 1999).1
Our analysis is distinct from previous works. We compare the incentive mechanisms oftwo different schemes: a fee system and a trade-in program. In order to improve health andfoster growth, the government plans to introduce either a fee on the usage of old motorcycles orto establish a trade-in program for these motorcycles. Under the fee system, the government willprohibit the use of old motorcycles unless owners pay the fee. Under the trade-in program, thegovernment will ban the use of old motorcycles and confiscate such motorcycles. Owners will
be compensated for the loss of their motorcycles. While the literature studies only trade-in
programs, this quasi-natural experiment allows us to study owners’ participation decisions inboth schemes.
This study is motivated by the following policy concerns. One question is whether
owners prefer one scheme to the other. The determinants of owners’ preferences are clarified byexamining participation decisions. The costs for each scheme as well as disposal rates of old
motorcycles are important questions for the purpose of cost-benefit analysis. We estimate themaximum level of owners’ willingness to pay (WTP) in order to keep using their motorcycles
and the minimum level of willingness to accept (WTA) as compensation for losing theirmotorcycles. We introduce preference uncertainty in the procedure (Carson et al., 1992; Li and
Mattsson, 1995; Champ et al., 1997). Owners are assumed to be unsure of the objective value ofa motorcycle. Finally, we ask what determines the levels of WTP and WTA. From the analysis,policy implications of the proposed schemes can be inferred.

1

Programs in California, Chicago, and Delaware are examples in the U.S. Fontana (1999) has a comprehensive

survey about accelerated vehicle retirement programs in Canada, Europe, and the U.S.

3

We use survey data collected by the Bangkok Metropolitan Authority and the WorldBank in Bangkok in July 2000. Interviews were the source of all data. The owners of theheavily polluting old motorcycles answered survey questions. The questions addressed topics
such as environmental attitudes and perceptions as well as willingness to pay and willingness toaccept. The survey questions are similar to the ones in Alberini et al. (1995). However, oursurvey extends beyond theirs as it includes owners’ environmental perceptions and does not haveblue book values.
The analysis provides governments with potentially useful information for implementing
market-based approaches in dealing with old vehicles and associated air pollution. Owners
prefer to pay fees and are reluctant to cooperate with trade-in programs. Many owners are
members of low-income households.

They are unable to afford new motorcycles unlesscompensation from the trade-in program fully funds a new motorcycle. Such owners worry thatthe trade-in program will essentially confiscate their means of living and/or transportation. The
results of our analysis are in agreement with the Hungarian experience, where a trade-in program
was not successful. Fontana (1999) explains the reason for the Hungarian failure. New vehicles
are too expensive for owners of old polluting vehicles to purchase. The owners of the oldmotorcycles are members of low-income households. However, our results imply that the fee
system can cause equity issues (Walls, 1999). At least, the trade-in program requires thegovernment to arrange complementary policy instruments, including low interest loans, assafeguards.
This is not a general conclusion when governments expropriate property in the interest ofenvironmental protection. Vehicle owners in developing and developed countries may havedifferent attitudes towards compensation.

The analysis shows that the level of WTA is4determined by owners’ financial conditions and benefits from motorcycle usage. The results
differ from those in Alberini et al. (1995) that study a 1992 trade-in program in Delaware.
Alberini et al. show that the level of WTA is determined by the value of the car, but not byhousehold income and the availability of alternative transportation. There is a distinct difference
between the present case and the case in the Alberini et al. study. In the Delaware study, used
vehicles are traded frequently in the market. Owners have a relatively good idea what theirvehicles are worth. This may not be the case for owners in Bangkok. Well-developed markets
for used motorcycles do not exist. Thus, the level of WTA in Alberini et al. is determined by
objective factors. In our study, subjective factors play an important role in determining the levelof WTA.
Our estimates of WTP and WTA are related to the literature on the disparity between
WTP and WTA. We observe that WTA is 2.54 times larger than WTP. This value is similar tothe one reported in a survey of 45 previous WTA versus WTP studies by Horowitz and
McConnell (2002). Previous work provides a few possible explanations for the disparity. The
lack of substitutive vehicles could create a disparity between WTP and WTA (Hanemann, 1991;
Shogren et al., 1994). Property rights may also explain the disparity (Kahneman and Tversky,
1979; Kahneman et al., 1990; Knetsch, 1989; Mitchell and Carson, 1989; Tversky and
Kahneman, 1991).

Another possible explanation is imprecision of motorcycle values(Brookshrine and Coursey, 1987; Coursey et al., 1987; Shogren et al., 1994). We conclude byasserting that the governments’ providing substitutive transportation methods promotes oldvehicle retirement.

5This paper will proceed as follows. Section 2 summarizes the data and the experimental
design. Empirical methods used for the analysis are described in Section 3. Section 4 discusses
the results of the analysis. Section 5 concludes the paper.

2. The Data
We use survey data collected in Bangkok in July 2000 by the Bangkok Metropolitan Authority(BMA) with the cooperation of the World Bank. The two institutions organized a motorcycle
clinic targeting owners of heavily polluting old motorcycles. The clinic aimed to examine thepossibility of accelerated motorcycle retirement based on feedback from motorcycle owners.
The institutions advertised the clinic via media and in fliers.

Potential participants were

informed that they would enjoy free maintenance of their old motorcycles at the site. The fliersalso informed potential participants that they may be issued a rebate coupon towards thepurchase of a new motorcycle that would meet current/anticipated future emission standards, if
the new project were legislated.
About 1500 owners participated in the clinic. They registered for the clinic and enjoyedfree maintenance by manufacturers’ mechanics. After the maintenance, they went throughemissions tests. Among the participants, 331 motorcycles failed the tests. The BMA and theWorld Bank define these motorcycles as heavily polluting old motorcycles. The owners of theheavily polluting old motorcycles were asked to answer survey questions.
All data were collected by interviews. The city police officers for the BMA, who weretrained for this survey, collected the information about socio-economic profiles of motorcycle
owners and the use of their motorcycles at the registration. Experienced interviewers from a
graduate school in Thailand collected the information on environmental attitudes and perceptions

6

as well as willingness to pay and willingness to accept. Both interviews were about 10 to 15
minutes in length.
The interviewers from the graduate school began the survey by explaining why air
pollution in Bangkok is problematic and possible future regulations. Air pollution in Bangkok
has caused thousands of premature deaths and several million cases of respiratory sickness. The
problem, attributed to old motorcycle emissions, has been acute for a decade. Thus, reduction of
air pollution is an urgent policy issue.

The government was planning to implement newlegislation geared toward upgrading motorcycle vehicles and reducing air pollution. There are
two possible regulations that the government believed would achieve their goal. One is the fee
system. Motorcycle owners need to pay money if they still want to use their old motorcycles. If
owners do not pay the fee, they are not allowed to use their old motorcycles. Collected taxes will
be used in helping air quality improvements. The other is the trade-in program. Under the tradein program, the government will ban the use of polluting motorcycles and will confiscate suchmotorcycles. The government will provide compensation to motorcycle owners for their loss ofproperty. Although these programs increase the tax burden on some motorcycle drivers, owners(and their families) will benefit from the new regulation. All citizens will benefit from theimprovements in health resulting from lower levels of air pollution.
After explaining the program options, the interviewers asked whether owners are willingto pay in order to keep using their motorcycles and whether owners are willing to accept
compensation for losing their motorcycles. We received 293 replies for the WTP question and
288 replies for the WTA question. The proposed regulations are not necessary welcomed.
Sixty-two owners are not willing to pay a fee and 97 owners are not willing to accept
compensation for trade-in. We eliminate the protest responses and missing data among the 331

7

respondents.2 After identifying the workable data, 231 owners are used for the WTP analysis,
and 191 owners are used for the WTA analysis. The two analyses share the same data on 155
owners.
Table 1 shows the summary statistics of the data used for each analysis. The two samples
are similar for the most part. However, gross household income in the WTA sample has a larger
mean value than in the WTP sample. As was mentioned before, this survey is similar to theAlberini et al. (1995) survey with the exception being that the current survey includes owners’
environmental perceptions. The aim is to examine whether the victims of air pollution havedifferent attitudes toward the regulations when compared to non-victims. Additionally, thesurvey asks questions relating to occupation, education, and public transportation usage that are
not asked in Alberini et al. Social status may matter if people in specific social class are willingto cooperate with government objectives. Public transportation usage addresses the possibility of
substituting motorcycle usage and public transportation. Although our survey includes someelements that were absent from the Alberini et al. survey, our survey does not have blue bookvalues (i.e., the market price of used motorcycles). In the Delaware study by Alberini et al., used
vehicles are traded frequently in the market. Owners and the state government have a relatively
good idea of the value of used vehicles. In our case, there is not a well-developed market forused motorcycles.

3. Model3.1 Participation2

About 40 owners’ data are missing among the 331 respondents. The BMA reports that several owners left before

the interviews took place.

8

The survey shows that among those who are willing to pay and/or to accept, several owners
support either one proposal or the other, but not both. We examine which factors affect owners’
decisions on participating in the programs.

This clarifies the determinants of owners’

preferences for regulations. The analysis uses a probit model: Pari j = Z i β j + eij , where Pari j isan observed reply, with Pari j = 1 if an owner i agrees to join a program j = {WTP,WTA} and 0

otherwise; Z is the set of variables in Table 1 and e is an error term.
Whereas several papers study the relationship between WTP and WTA, little work has
been done investigating about the participation decisions in WTP and WTA programs. The
literature often uses laboratory experiments, where respondents are randomly assigned to be apart of either program. Previous work is concerned with the disparity between WTP and WTA.
The quasi-natural experiment of this project allows us to study how to promote participation in
incentive-based regulations.

The analysis also provides us potentially useful information

regarding the necessity of complementary policies, such as financial safeguards, when
governments expropriate private property.

3.2 Estimation of WTP and WTA

The government needs to estimate a budget in order to evaluate the proposals. This requires
estimating the levels of WTP and WTA. We introduce preference uncertainty when making
these estimations (Carson et al., 1992; Li and Mattsson, 1995; Champ et al., 1997; Wang andWhittington, 2000). Owners are assumed not to be confident with the objective value of a
motorcycle. One possible interpretation is that owners have incomplete knowledge regarding themarket value of their motorcycle since markets are not well-developed. Li and Mattsson (1995)
show that ignoring this type of uncertainty may lead to biased estimates. Introducing preference

9uncertainty provides more information about respondents and helps to specify their true
evaluations, subsequently correcting for the bias.
The interviewers used a payment card to collect the data of WTP and WTA (Table 2).
The card had six different levels of payments, 300, 600, 1,000, 1,500, 2,000 and 3,000 bahts for
WTP and six levels of compensation, 2,000, 4,000, 6,000, 8,000 and 12,000 bahts for WTA.
These levels were chosen based on the results of a pretest interview. The interviewers asked anowner whether s/he would be willing to pay 300 Thai bahts per year to continue to use theirmotorcycle. S/he selected the likelihood that s/he would agree to pay among five categories inthe table. If s/he agreed, then the interviewers asked the same question with an increased priceoffer. The interviewers continued this procedure until the owner denied the payment. Similarly,
for WTA, the interviewers offered a price and asked whether the owner would be willing toaccept the stated price as compensation for giving up their motorcycles. S/he selected thelikelihood from five categories in the table. If s/he agreed, then the interviewers iterated theprocedure until the offer was denied.
The numeric likelihood values allow for the owner’s subjective valuation of a motorcycle
to be a random variable associated with a valuation distribution. The owner’s WTP or WTA isdefined as the mean value of the distribution. More formally, denote X as the set of thepayments {300,600,1000,1500,2000,3000} and X j as the j -th price, where j = {1,2,3,4,5,6} .
An owner, i , would agree to pay the price as long as it is lower than his/her true valuation of a
motorcycle, WTPi (e.g., Cameron, 1988). However, the owner’s value assessment is expressed
as WTPi = WTPi + ε i due to preference uncertainty, ε i . Denote Pij as the subjective probability
that the owner i agrees to pay the price j , and Pi as his/her likelihood vector (i.e., the set of the10

six subjective probabilities).3 We can write that Pij (Yes) = Pr(WTPi > X j ) = 1 − Fi ( X j ) , where

F is a cumulative distribution function. Suppose the error term is normally distributed, i.e.,

ε i ~ N (0, σ i 2 ) . The probability is rewritten as
Pij = 1 − Φ (X j − WTPi

σi)

X j = WTPi + σ i Φ −1 (1 − Pij ) ,orwhere Φ is the cumulative distribution function of a standard normal variable. The owner i ’s

WTPi is the mean value of the distribution. The standard error, σ i , represents the consistency of
responses.

A smaller standard error implies that the owner is more confident in his/hervaluation. We estimate the two variables, WTPi and σ i , by regressing X on Φ −1 (1 − Pi ) .
We estimate the level of WTA using a similar method. Denote Y as the set proposedcompensation levels {2000,4000,6000,8000,10000,12000} and Y j as the j -th compensation,
where j = {1,2,3,4,5,6} . An owner, i , would not agree to accept a price lower than his/her true
valuation of a motorcycle, WTAi . Let the owner’s value assessment be WTAi = WTAi + υ i ,where υ i is his/her preference uncertainty. The subjective probability that the owner, i , does
agree to accept payment of price, j , is written as Pij ( No) = Pr(WTAi > Y j ) = 1 − Fi (Y j ) .
Assuming the error term is normality distributed, υ i ~ N (0, σ υ2i ) , the probability is rewritten as

Pij = 1 − Φ (Y j − WTAi

σ υi

)

Y j = WTAi + σ υi Φ −1 (1 − Pij ) .

orWe estimate WTAi and σ υi by regressing Y on Φ −1 (1 − Pi ) .

3

Refer to Appendix for technical issues about likelihood vectors.
11

3.3 Determinants of WTP and WTA

We are interested in the determinants of the levels of WTP and WTA. The examination of thedeterminants identifies policy implications of the proposed regulations. We use two empiricalmodels, log WTPi = Z iα + η i and log WTAi = Z i γ + µ i , where the vector Z includes the factors
in Table 1 and η and µ are error terms. The standard regression analysis reveals that only few
factors are statistically significant in explaining the level of WTA. We did not expect theseresults, and suspect the survey suffered from inaccurate responses and/or measurement error.
Some owners may hesitate to declare the exact amount of their income in interviews. Owners
may give inconsistent answers when answering questions regarding subjective evaluation of
motorcycle condition. We could omit independent variables of weak explanatory power fromthe regression. However, we would then risk losing potentially useful information.
We test the robustness of the analysis by using a statistical method in clinical trials. We
introduce a composite of independent variables proposed by Tukey (1991, 1993). He suggests
compounding related independent variables as a method for extracting as much information as
possible from a limited number of observations. Compound variables are constructed as aweighted sum of independent variables.

Greater weight is put on variables with stronger

explanatory power. This method utilizes variables not included in regular regression, and adds
some valuable information.
We employ the compound variables method in conducting our present analysis. First, weclassify independent variables of similar nature into three groups. Group 1 is socio-economic
profile, group 2 environmental perceptions, and group 3 the condition and use of motorcycles.
The three categories are defined as in the original survey prepared by the World Bank. We thenstandardize each variable. This makes each independent variable comparable in unit and allows

12compounding variables from within the same group. We determine the contribution of eachvariable after regressing log WTP (or log WTA) on each standardized variable. As in Tukey, weassign weights according to the p-values: ± 4 if p < .001 , ± 3 if .001 ≤ p < .01 , ± 2 if

.01 ≤ p < .05 , ± 1 if .05 ≤ p < .25 , and 0, otherwise. The sign of weight follows one of thecoefficients. A compound variable of an owner i for group j , C ij , is defined as a linear
combination of standardized variables, z , with their weights, w : C ij = ∑ ws z s , ∀j = 1,2,3 .
s∈G jCompound variables do not contain redundant information.

They make several

independent variables complement each other.4 This enriches the economic interpretation of theresults. Instead, one may want to use a lower standard when selecting independent variables instandard regression analysis. Lower standards lead to the inclusion of more variables. The
compound variable method is preferable in terms of the degree of freedom.5 One drawback ofcompound variables is that their coefficients do not lend themselves easily to interpretation. We
may want to use Tukey’s weight to examine the effect of any individual variables.

4

Let us illustrate the idea with an example. Economic theory suggests that socio-economic status would determine

the levels of WTP and WTA. Suppose, however, a regression analysis indicates that variables such as income,occupation, and education do not have explanatory power for WTP and WTA. We can composite the threevariables.

The compound variable will operate as a good proxy of socio-economic background, specificallyfinancial condition. A compound variable can be a better predictor, when each independent variable does not have astrong explanatory power.
5This method has other technical advantages. Suppose we are regressing y on x1 , x 2 , x3 , where xi is a

categorical variable, either 0 or 1. We have eight possible clusters (e.g ., x1 = 0, x 2 = 1, x3 = 1) . As is often the casein surveys, the number of the observations of each cluster could be very small. Coefficient estimates do not makemuch sense statistically in this case. The use of compound variables will resolve the problem.

13

4. Results of the Analysis
4.1 Participation

Table 3 shows the results of owners’ participation decisions under each regulation. At firstglance, one may wonder why gross household income has a negative coefficient in the WTP
analysis. However, it is reasonable to believe that owners in the lower income class are willingto pay a fee. The WTA analysis yields a positive sign of gross household income coefficient.
This sign difference has an important implication. Owners’ preferences for each regulation
depend on government expropriation and compensation schemes.

The trade-in program

expropriates old motorcycles, but does not compensate owners enough to allow for the purchaseof a new motorcycle. Since it is not easy for low-income owners to afford a new motorcycle,
they are likely to lose their means of transportation and/or way of living.6 Thus, owners with alower income prefer to pay a fee to keep using their motorcycle rather than give the motorcycle
up when compensation will not be enough to afford them a new motorcycle.
We obtain expected results on other coefficients. If owners plan to replace their worn-out
motorcycles in the near future (due to high mileage or old age), they are willing to join the tradein program, but prefer not to pay a fee. Other owners may want to keep their motorcycles due toemotional attachment. This is a case where owners spend more for motorcycle purchase and/or
maintenance costs. Government officials cooperate with trade-in programs but do not want to
pay from their own pockets. In sum, owners with motorcycles in better condition are for the fee
system and against the trade-in program.

6

Taxi drivers receive less gross household income and spend more for their motorcycles. The availability ofalternative transportation method decides owners’ preferences for each regulation.

144.2 Disposal Rates

The interviews use a contingent valuation method (CVM) to collect the data. Thus, the analysis
may be subject to the same criticism made of CVM: strategic bias (e.g., Knetz et al., 1985). The
owners may overstate their true WTA value and understate their true WTP value. To alleviate
the problem, we eliminate protest bids in the samples. Mitchell and Carson (1989) suggest
eliminating protest bids helps minimize strategic bias. After protest bid elimination, the data
gathered from 205 owners are valid to use for the WTP analysis, and the data gathered from 132
owners are valid to use for the WTA analysis.
The level of WTA is substantially higher than the level of WTP. The result agrees with awell-known fact in the literature. Our mean (median) value of estimated WTA is 6,651 (6,664)
bahts with a standard deviation of 2,781 bahts. Our mean (median) value of estimated WTP is584 (507) bahts with a standard deviation of 321 bahts. The WTP is an annual payment. The
average owner expects their motorcycles to last 4.47 more years. Adjusting the unit (i.e., 584
multiply by 4.47) reveals that the WTA is 2.54 times larger than the WTP. Horowitz and
McConnell (2002) examine 45 previous studies of WTA and WTP and find a similar difference.
Horowitz and McConnell find that for private goods WTA is 2.92 times larger than WTP.
Not only are there difference in the levels of WTA and WTP. Estimated costs of the tworegulations differ considerably. Figure 1 depicts the relationship between the level of WTP (or
WTA) and the disposal rate of heavily polluting old motorcycles. Suppose the government plans
to eliminate 90 percent of the polluting motorcycles in the market. The figure predicts that thisgoal can be achieved by either charging an annual fee of 1,000 bahts or by compensating eachowner 10,701 bahts for their trade-in. The BMA and the World Bank report there to be about15

700,000 polluting old motorcycles are in Bangkok. The fee system is estimated to raise 70
million bahts each year as tax revenue. To fund a trade-in program, the government would needto prepare 6.74 billion bahts.

4.3 Determinants of WTP and WTA

Table 4 shows the determinants of WTP and WTA. Coefficients (and the standard errors) are theresults of the standard regression analysis.

These are listed on the top half of the table. The

results of compound variables are summarized in the bottom half of the table. We use the values
in the column of “Weight” (i.e., Tukey’s weights or marginal effects for each variable) to obtainthe compound variables.
The analysis reveals that the determinants of WTP are owners’ future financial
conditions, availability of alternative transportation, and motorcycle conditions. Expected futurefinancial condition matters more than current income level since fee payment occurs in the
future. Government officials expect stable income and, thus, are willing to pay more. Families
with more children are reluctant to pay.

Also, the existence of substitutes is important.

Motorcycles are necessities when public transportation is inaccessible. When substitutes are notavailable, owners are willing to pay more. Owners who plan to replace their motorcycles prefer
not to pay a fee.
The level of WTA is determined by owners’ current financial conditions and evaluation
about transportation services from their motorcycles. Owners with lower income ask for morecompensation. Current financial conditions matter since most owners need to purchase a new
motorcycle once their old one is expropriated. Those who use motorcycles frequently also
request a higher level of compensation.

Their benefit from motorcycle usage is greater.

16

Financial condition could be an interpretation of the positive coefficient of mileages. The mean
mileage value for taxi drivers is higher than the mean mileage for users with other occupations.
Motorcycle taxi drivers are paid less than those in other occupations.
The results from the compound variables analysis are similar.

However, they are

different from those of standard analysis in environmental perceptions. Examining Tukey’s
weights tells us that those who have suffered from air pollution ask for more compensation. This
could be interpreted that the variables of environmental perceptions operate as a proxy for thecurrent income level. In fact, it turns out that victims of air pollution are members of lower
income households. This observation leads one to question: who are the victims? The WorldBank (2000) reports that those who use motorcycles longer have higher risk of suffering fromsevere respiratory symptoms. They “are literally using their lungs to clean the air.” The salary ofheavy motorcycle users (e.g., taxi drivers) is below average.7
The results differ from the ones in Alberini et al. (1995) that study “a vehicle-retirement
program in Delaware in 1992.” They show that the level of WTA is determined by the value ofthe car, but not by household income and the availability of alternative transportation. There is adistinct difference between the present case and the case in the Alberini et al. study. Owners in
the Delaware study have a relatively good idea of the market value of used cars. Used cars aretraded frequently in the market and, thus, published prices are available. This may not be thecase for owners in this study due to the lack of well-developed used motorcycle market. Thus,
the level of WTA in Alberini et al. is determined by objective factors, while subjective factors
play an important role in determining the level of WTA in our study.

7

We classify fuel expenses into two groups, below and above average, and compare the income levels for eachgroup. The Wilcoxon rank-sum test shows a significant difference of incomes between the two groups.

17

4.4. Discussion

Complementary policy instruments are necessary in order to promote the retirement of old
polluting motorcycles. The mean WTP is equivalent to seven percent of the mean gross incomeof the sample and the mean WTA is 80 percent of the mean gross income of the sample. The
ratio of the WTA to the mean income is much higher than the one in the Alberini et al. study,
where it is about three percent. Owners in Bangkok may have overstated the WTA. They may
not have information about the precise value of motorcycles.8 However, another plausible
interpretation is that their income is relatively low compared to motorcycle prices. Many owners
will lose their way of life if the government expropriates old motorcycles without providing
enough compensation to fund the purchase of a new motorcycle. They are unable to afford a
new motorcycle that is compatible with emission standards. The interpretation is consistent withthe literature on the disparity between WTP and WTA. The lack of substitutes creates a larger
difference between WTP and WTA (Hanemann, 1991; Shogren et al., 1994). Our results imply
that the government needs to complement a trade-in program with other policy instruments.
Possible examples include providing loans with low interest rates for new motorcycle purchases
and better public transportation services.

5. Conclusions

Air pollution caused by old motorcycles and the associated health problems are serious policy
issues in Bangkok. Bangkok citizens are suffering from respiratory symptoms and increased18

premature mortality risk. To reduce air pollution, the government was planning to enact an old
motorcycle retirement program. There are two alternative schemes available which are expectedto achieve the goals of the policy. One is to introduce a fee system on the usage of heavily
polluting motorcycles. The other is to establish a trade-in program for these motorcycles.
We examine the relationship between the government’s financial incentive and market
responses among motorcycle owners by using the motorcycle retirement program. The literature
has studied only the effects of trade-in programs. This paper is distinct, since the quasi-naturalexperiment of Bangkok’s program allows us to compare owners’ preferences for two alternative
schemes: a fee system or a trade-in program. The analysis reveals that owners prefer a fee
system to a trade-in program, although a “Cash for Clunkers” scheme is dominant in reality. At
first glance, a trade-in program is preferable to a fee system in terms of equity. However, many
owners worry that the trade-in program may confiscate their means of living and/ortransportation. The owners of old motorcycles are members of low-income households. New
motorcycles are too expensive for these owners to purchase. Such owners prefer to pay a fee
rather than lose their motorcycles in exchange for a low level of compensation.
Similar implications will be of relevance when governments expropriate private property
in the interest of environmental protection, specifically in developing countries. We should be
careful not to interpret a fee system as desirable. A fee system can cause equity issues if
property owners are low-income households. A reasonable interpretation is that a trade-in
program will deteriorates the owners’ life without full compensation (or arranging alternativetransportation methods in this paper). The results imply that the enactment of market-based
8

The literature shows that imprecision (or uncertainty) causes the disparity between WTP and WTA; the disparity

decreases as people learn more about the goods in repeated experiments (Brookshrine and Coursey, 1987; Coursey

19regulations requires further improvement of the social infrastructure, including financial
institutions and public transportation systems, in developing countries.

Acknowledgements

We gratefully acknowledge suggestions by Li Ming Dong, Harvey Gram, Molly Sherlock, andAlan Weinman. The first author would like to thank seminar participants at the City Universityof New York at Graduate Center for their comments on an earlier draft. This study is partly
funded by a research grant from the City University of New York Research Foundation. The
first author appreciates the financial support. Also, we wish to thank the Bangkok Metropolitan
Authority that collected the data in coordination with the World Bank. The views expressed here
are those of the authors and do not reflect official positions of the World Bank. All errors are
ours.

et al., 1987; Shogren et al., 1994).

20

Appendix

A technical note: how to construct likelihood vectors

In practice, we face the problem that both Pij = 1 and 0 do not provide useful information for
estimating WTP. This is because Pij = 1 implies X j = −∞ from Fi ( X j ) = 0 and Pij = 0 implies

X j = +∞ from Fi ( X j ) = 1 . The analysis employs a methodology similar to the one used in theliterature (Li and Mattsson, 1995; Wang and Whittington, 2000).

We use the value of 0.99

instead of the last 1 and use the value of 0.005 instead of the first 0. For example, if thelikelihood vector Pi is {1,1,0.5,0,0,0} , then we transform the vector to {1,0.99,0.5,0.005,0,0} .
This treatment enables us to utilize more samples; otherwise we need to eliminate some samples
from the analysis since we are not able to estimate WTP (e.g., the above example). The
transformation makes regression possible.
Despite the merit, we do not transform all 1 to 0.99 (or all 0 to 0.005).

This makes

estimators very sensitive to tail prices. Suppose we transform the previous likelihood vector to

Pi = {0.99,0.99,0.5,0.005,0.005,0.005} . The WTP will be overestimated. This is because theinfluence of larger prices (i.e., three 0.005s) is stronger than that of smaller prices (i.e., two
0.99s).

The problem is more serious if payment schedules are poorly designed such as

X = {300,600,1000,1500,2000,3000,4000,5000,6000} and a likelihood vector has longer tails
such as Pi = {1,1,0.5,0,0,0,0,0,0} . Transforming only the last 1 and the first 0 avoids such aproblem.
One may wonder whether the results will change if we transform likelihood vectors withdifferent values (e.g., 0.95 instead of 0.99). The authors’ estimation reveals that the results are21not sensitive to the difference.

Rather, we realized that it is important to adjust the different

interval lengths of prices (e.g., 300=600-300 and 1,000=3,000-2000) by using asymmetricprobabilities (0.99 and 0.005 instead of 0.99 and 0.01). Otherwise, we will get biased estimates.
The reason is the same as in the previous paragraph. Symmetric probabilities put more weights
on the tail of larger prices and, thus, overestimate the level of WTP. The price vector used for
WTA has the same interval length. We used symmetric probabilities (the value of 0.99 insteadof the last 1 and use the value of 0.01 instead of the first 0) in transforming likelihood vectors.

22

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26

Table 1 Summary Statistics

mean
Socio-Economic Profile
Age
Number of Children
Number of Household Members
Number of Workers in Household
Gross.Income
Gross.Household.Income
Condition & Use of Motorcycles
Yrs.Own
Yrs.Own.Previously
Mileage (km/day)
Purchase.Payment
Max.IM.WTP
Maintenance.Costs (per month)
Fuel.Expenses (per week)
MCLife.Expectancy
Socio-Economic Profile
Male : Female
Married : Single
HighSchool or less : Bachelor or higher
Employment-Full time : Part timeGovernment employee
Taxi driver & Messenger
Household head : Not HH.head
Savings : No.savings
Environmental Perceptions
HH.RespProbs
HH.HealthProbs
Pay.for.Treatment
Income.Lost
McPollution
Condition & Use of Motorcycles
Business.Use
Condition-Good : Bad
Repaired
Emission.Checked
Own.Other.Vehicles
Pub.Transit.Use- Often : not Often
Sample size

WTP
std.dev.

WTA
mean
std.dev.

33.57
1.84
4.52
2.46
8363
17960

9.29
0.98
2.03
1.37
4636
13458

34.03
1.77
4.56
2.5
8505
20190

8.78
0.96
2.02
1.36
4224
16207

5.23
4.03
68.43
48830
2471
624.1
348.8
4.5
ratio

2.91
3.06
45.41
25089
2324
630.21305.15
2.84
ratio

5.28
4.01
71.63
48140
2410
549.7
332.2
4.38
ratio

2.99
4.08
49.17
25366
2284
487.94181.69
2.84
ratio

0.99
0.68
0.78
0.84
0.07
0.43
0.67
0.37

0.01
0.32
0.22
0.16
0.93
0.57
0.33
0.63

0.99
0.67
0.73
0.84
0.09
0.37
0.66
0.39

0.01
0.33
0.27
0.16
0.91
0.63
0.34
0.61

0.16
0.21
0.30
0.41
0.94
0.95
0.64
0.25
0.22
0.42
0.09
231

0.19
0.21
0.36
0.44
0.95

0.36

0.91

0.94
0.65
0.30
0.22
0.46
0.09
191

Note: The median values of each variable are also similar to the mean values in the table.

27

0.35

0.91

Table 2 Payment Cards

WTP

Baht/year

Definitely
Probably
Not Sure
willing to pay willing to pay (50%)(100%)(75%)

Probably not Definitely notwilling to pay willing to pay
(25%)
(0%)

300

1

2

3

4

5

600
1000
1500
2000
3000

1
1
1
1
1

2
2
2
2
2

3
3
3
3
3

4
4
4
4
4

5
5
5
5
5

WTA

Definitely not Probably not Not Sure(50%)
willing towilling toaccept (100%) accept (75%)

Baht

Definitely
Probably
willing towilling toaccept (25%) accept (0%)

2000

1

2

3

4

5

4000
6000
8000
10000
12000

1
1
1
1
1

2
2
2
2
2

3
3
3
3
3

4
4
4
4
4

5
5
5
5
5

28

Table 3 Participation
WTP
Socio-Economic Profile
Occup.Gov't
Gross.HH.Income
Environmental Perceptions
HH.RespProbs
Condition & Use of MCs
Yrs.Own
Mileage (km/day)
Purchase.Price
Maint.Costs (per month)

WTA

Coeff.

Std.Err.

-0.011**

0.006-0.050**0.006***

Coeff.
0.542***
0.009''

Std.Err.
0.312
0.006

-0.405***0.213

0.004**

0.002-0.266**
0.436***

0.133
0.264

0.027
0.004

Intercept
1.029*
0.255
293
Sample size
* Statistically significant at 1%; ** at 5%; *** at 10%:'' at 12%
Currency Unit: 1,000 bahts

288

Variable description
Marital.Status…0 for Single, including Divorced/Separated, and 1 for Married
Education…0 for up to High School and 1 for Bachelor or higher, including Technical diploma
Employment.Status…0 for Full time and 1 for Part time
HouseHold.Head…0 for Yes and 1 for No
Savings…0 for any savings and 1 for no savings
HH.RespProbs…0 for family members had respiratory symptoms and 1 for otherwise
HH.HealthProbs…0 for family members had health problems by air pollution and 1 forotherwise
Pay.for.Treatment…0 for Yes and 1 for No
Income.Lost…0 for lost income due to sickness and 1 for otherwise
McPollution…0 for agree with the opinion that pollution from motorcycles causes healthproblems and 1 for disagree with the opinion
Total.Yrs.Own…Yrs.Own+ (years owned by previous owners, if second-hand)
Purchase.Payment…owners’ gross payment to obtain their motorcycles
Business.Use;Repaired; Emission.Checked;Own.Other.Vehicles…0 for Yes and 1 for No
Condition…0 for Excellent/Good and 1 for Bad/Very Bad (Owners' subjective judgments)
Max.IM.WTP…maximum WTP/ year for regularly inspection and maintenance of motorcycles,
if required by regulations
MClife.Expectancy…how many more years owners intend to keep their motorcycles.
Pub.Transit.Use…0 for Very often/Often and 1 for Sometimes/Not at all

29

Table 4 Determinants of WTP and WTA
WTP
Socio-Economic Profile
WeightCoeff.
Std.Err. WeightAge00
Marital.Status1
1
Number of Children-10-0.05***
0.03
Education00
Occup.Gov't
2
00.28**
0.12
Occup.TaxiDriver
1
1
Employment.Status
00HouseHold.Head-1-1
Number of HH.Members-10
Number of HH.Working-1-2
Income00
HH.Income0-1
PerCap.HH.Income
0-1
Savings-1-1
Environmental Perceptions
HH.RespProbs
0-3
HH.HealthProbs
0-1
Pay.for.Treatment
0-2
Income.Lost-1-2
McPollution
1
0
Condition & Use of MCs
Yrs.Own00
Total.Yrs.Own
0-1
Mileage (km/day)
0
2
Business.Use
00Purchase.Price
2
1
Condition
00
Repaired
0
1
Max.IM.WTP
0
0
Emission.Checked00Maint.Costs (per month)00Fuel.Expenses (per week)-10
MClife.Expectancy-1
0-0.02***
0.01
Own.Other.Vehicles
01
Pub.Transit.Use2-1
0.25**
0.10
Intercept
6.16*
0.12
R-Squared
0.07
Compound Variables
Intercept
6.26*
0.03
Socio-Economic Profile
0.03*
0.01
Environmental Perceptions
0.02
0.02
Condition & Use of MCs
0.03*
0.01
R-Squared
0.11*Statistically significant at 1% level; ** at 5% level; *** at 10% level;“ p-value is 0.11.

30

WTA
Coeff.

Std.Err.

-0.11”

0.07.002**

.001

9.49*
0.05

0.57

8.70*
0.01”
0.02**
0.03**
0.13

0.04
0.01
0.01
0.01

Figure 1. The disposal rate of polluting motorcycles

Disposal Rate

0.0

0.2

0.4

0.6

0.8

1.0

omparison of Empirical cdfs of air11.31$air11.WTP and rep(NA, dim(air11.31)

500

1000

1500

2000

dotted line is cdf of rep(NA, dim(air11.31)[1])

Level of WTP (Bahts)

0.0

0.2

0.4

0.6

0.8

1.0

mparison of Empirical cdfs of air22.31$air22.bk.WTA and rep(NA, dim(air22.3

2000

4000

6000

8000

10000

dotted line is cdf of rep(NA, dim(air22.31)[1])

Level of WTA (Bahts)

12000