Hiroyuki˜Shibusawa Katsuhiro˜Sakurai Takeshi˜Mizunoya...

New Frontiers in Regional Science: Asian Perspectives 24

Hiroyuki ShibusawaKatsuhiro SakuraiTakeshi MizunoyaSusumu Uchida Editors

Socioeconomic Environmental Policies and Evaluations in Regional ScienceEssays in Honor of Yoshiro Higano

Hiroyuki Shibusawa • Katsuhiro SakuraiTakeshi Mizunoya • Susumu UchidaEditors

SocioeconomicEnvironmental Policiesand Evaluations in RegionalScienceEssays in Honor of Yoshiro Higano

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Contents

Part I Global Perspectives

Regional Science in the Twenty-First Century . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3Antoine Bailly and Lay Gibson

The Sustainability of Demographic Progress Around the World . . . . . . . . . . . 9David A. Plane

Methodological Advances in Gibrat’s and Zipf’s Laws: AComparative Empirical Study on the Evolution of Urban Systems . . . . . . . . 37Marco Modica, Aura Reggiani, and Peter Nijkamp

How Can Cross-Sector Partnerships Be Made to WorkSuccessfully? Lessons from the Mersey Basin Campaign (1985 –2010) . . . . 61Peter Batey

Consumption and Environmental Awareness: Demographicsof the European Experience. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81Philip S. Morrison and Ben Beer

Cultural Capital and Local Development Nexus: Doesthe Local Environment Matter? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103Roberta Capello and Giovanni Perucca

Who Wants More Open Space? Study of Willingness to BeTaxed to Preserve Open Space in an Urban Environment . . . . . . . . . . . . . . . . . . 125Chunhua Wang, Jean-Claude Thill, and Ross Meentemeyer

On Capital Taxation and Economic Growth and Welfarein a Creative Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147Amitrajeet A. Batabyal

Some Extensions to Interregional Commodity-Flow Models. . . . . . . . . . . . . . . . 161Kieran P. Donaghy

xiii

xiv Contents

Evaluation of Ecosystem Services Through Revealed PolicyPreferences: Exchange Rates Between Scientific Currencies . . . . . . . . . . . . . . . 177Tomaz Ponce Dentinho

Optimal International Investment Policy for the SeaEnvironment in East Asia: Case Study of the Sea of Japan . . . . . . . . . . . . . . . . . 203Katsuhiro Sakurai

Part II Asia-Pacific Perspectives

Spatial Impacts of Endogenously Determined InfrastructureInvestment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227William Cochrane, Arthur Grimes, Philip McCann,and Jacques Poot

Migration Responses to a Loss in Regional Amenities: AnAnalysis with a Multiregional CGE Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249James A. Giesecke and John R. Madden

Valuing the Benefits of Cleaner Air in Jakarta Metropolitan Area . . . . . . . . 279Mia Amalia, Budy P. Resosudarmo, Jeff Bennett,and Arianto Patunru

Study of Fair Trade Products for Regional Development . . . . . . . . . . . . . . . . . . . 299Yoko Mayuzumi and Takeshi Mizunoya

Social Public Spending in the Countries That Comprisethe Andean Community of Nations (CAN) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309Elizabeth Aponte, Emma B. Castro, and Lilian A. Carrillo

Global Backward and Forward Multiplier Analysis: The CaseStudy of Japanese Automotive Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319Sutee Anantsuksomsri, Nattapong Puttanapong, and Nij Tontisirin

Transportation Infrastructure and Economic Growthin China: A Meta-analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339Zhenhua Chen and Kingsley E. Haynes

Statistical Analysis of Sustainable Livelihood in China. . . . . . . . . . . . . . . . . . . . . . 359Guoping Mao

Green Environment Social Economic System for Urban-RuralIntegration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381Bin Li, Wen-Hong Cheng, and Cheng-Long He

Climate Change and Livelihood Adaptation Strategiesof Farmers in Northern Bangladesh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395Md. Fakrul Islam and Wardatul Akmam

Contents xv

Nongovernment Organizations’ Contributions to PovertyReduction and Empowerment of Women through Microcredit:Case of a Village in Gaibandha District, Bangladesh . . . . . . . . . . . . . . . . . . . . . . . . 411Wardatul Akmam and Md. Fakrul Islam

Part III Japan Perspectives

On Environmental Risk Management: The Interactionsof Economic and Noneconomic Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 429Yasuhiro Sakai

Product Design for Recycling and Recycling Industry. . . . . . . . . . . . . . . . . . . . . . . 447Makoto Tawada and Tomokazu Sahashi

Optimal Policy and the Threat of Secession . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 461Moriki Hosoe

Economic Impact of CO2 Emissions and Carbon Taxin Electric Vehicle Society in Toyohashi City in Japan . . . . . . . . . . . . . . . . . . . . . . . 479Yuzuru Miyata, Hiroyuki Shibusawa, and Tomoaki Fujii

Initial Explorations into the Spatial Structure of the JapaneseRegional Economies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 503Geoffrey J. D. Hewings and Michael Sonis

Rural and Agriculture Development in Regional Science . . . . . . . . . . . . . . . . . . . 537Lily Kiminami and Akira Kiminami

Impact of Climate Change on Regional Economies ThroughFluctuations in Japan’s Rice Production: Using Dynamic PanelData and Spatial CGE Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 557Suminori Tokunaga, Mitsuru Okiyama, and Maria Ikegawa

Risk Evaluation of Social Decision Process: A Two-StageAuction Game Model for Japanese Urban Redevelopment Procedure . . . . 581Saburo Saito, Mamoru Imanishi, Kosuke Yamashiro,and Masakuni Iwami

Preference Elicitation in Generalized Data EnvelopmentAnalysis: In Search of a New Energy Balance in Japan . . . . . . . . . . . . . . . . . . . . . 601Soushi Suzuki and Peter Nijkamp

Note on the Framework for Disaster Impact Analysiswith Environmental Consideration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 619Yasuhide Okuyama

Evaluating the Economic Impacts of Hybrid and ElectricVehicles on Japan’s Regional Economy: Input–OutputModel Approach. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 631Hiroyuki Shibusawa and Yuzuru Miyata

xvi Contents

Evaluation of the Water-Environment Policy in the ToyogawaBasin, Japan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 651Katsuhiro Sakurai, Hiroyuki Shibusawa, Kanta Mitsuhashi,and Shintaro Kobayashi

A Management Policy of Demand-Driven Service forAgricultural Water Use in Japan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 667Katsuhiro Sakurai

An Analysis on Social Benefit Derived by Introducing NewTechnology and Optimal Environmental Policy: Case Studyin Lake Kasumigaura . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 683Takeshi Mizunoya

Promotion Policies for Sustainable Energy Technologies: CaseStudies in Japan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 711Susumu Uchida

Valuing the Benefits of Cleaner Air in JakartaMetropolitan Area

Mia Amalia, Budy P. Resosudarmo, Jeff Bennett, and Arianto Patunru

Abstract Air pollution negatively impacts the society in the form of healthproblems, unpleasant odour and low visibility. We estimate the benefit of havingcleaner ambient air for the Jakarta Metropolitan Area’s (JMA) citizens. The valuepeople place on an improvement in air quality resulting from the implementationof three new transportation policies is estimated using choice modelling with fourattributes: restricted activity days (RADs), visibility, odour and implementationcosts. A public survey was conducted in the JMA across approximately 650 respon-dents. The implicit prices for individual attributes are estimated using conditionallogit (CL) and random parameter logit (RPL) models. The results show that therespondents have significantly positive values for a lower number of RADs andless unpleasant odour. On average, respondents in the JMA are willing to payfrom USD54 to USD57 per household per annum over a 3-year period for theimplementation of a new transportation policy. This translates into a total benefitto the community in the range of USD219 million to USD230 million per year.

Keywords Air pollution • Cost-benefit analysis • Choice modelling• Transportation policy • Jakarta Metropolitan Area

M. AmaliaIndonesian National Development Planning Agency, Jakarta, Indonesiae-mail: [email protected]

B.P. Resosudarmo (�) • A. PatunruArndt-Corden Department of Economics, Australian National University, Coombs Building,Canberra, ACT 0200, Australiae-mail: [email protected]; [email protected]

J. BennettCrawford School of Public Policy, Australian National University, Canberra, Australiae-mail: [email protected]

© Springer Science+Business Media Singapore 2017H. Shibusawa et al. (eds.), Socioeconomic Environmental Policies and Evaluationsin Regional Science, New Frontiers in Regional Science: Asian Perspectives 24,DOI 10.1007/978-981-10-0099-7_14

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mailto:[email protected]:[email protected]:[email protected]:[email protected]

280 M. Amalia et al.

1 Introduction

Compared with big cities in other developing and developed countries, total sus-pended particulate (TSP) concentration in the Jakarta Metropolitan Area (hereafterJMA) is among the highest; it is second only to New Delhi (Health Effect Institute2004). The PM-10 concentration in Jakarta was the 11th highest in the world in2002, while in the case of SO2 and NOx, it was ranked 97th and 67th, respectively,in 2001 (The World Bank 2006).1

Emissions from vehicle operation are one of the main sources of air pollutionin JMA (Ostro 1994; Syahril et al. 2002; IMAP 2002). In 1992, vehicular emissionloads for Jakarta were 35 % for NOx and 73 % for TSP (Ostro 1994).2 In 1995, theloads were 49 % for NOx, 28 % for SO2 (Hamonangan et al. 2002) and 70 % forPM-10 (Haryanto 2012). Syahril et al. (2002) predict that the emission load fromvehicles in 2015 will be between 2.73 and 3.68 times higher than the 1998 emissionloads.

Pollution control policies were implemented to tackle the problems. These poli-cies vary from general standards to specific programmes. In JMA, the latter includethe minimum of three passengers in one car requirement (three-in-one programme)for central Jakarta during busy hours, the inspection and maintenance (IM) ofprivate vehicles programme, and public transportation enhancement. Despite theseprogrammes, there has been no significant improvement of air quality in JMA overthe last decade. Effective policies should bring higher net benefits to the society.To measure these net benefits, we need to estimate the benefits of having cleaner airagainst implementation costs. The objective of the research presented in this paper isto estimate the former, i.e. the benefits enjoyed by JMA citizens from having cleanerambient air as a result of the implementation of three new transportation policies.This provides an important information for policymakers in considering the costs toimplement each policy option. Attributes of air pollution used in this research are thenumber of restricted activity days caused by upper and lower respiratory illnesses,visibility level and odour level. The three proposed policies are (1) improvement ofpublic transport facilities, (2) restriction of the number of vehicles in busy areas and(3) reduction in the number of old vehicles.

2 Air Pollution Policies in Jakarta Metropolitan Area

The existing policies in JMA to address the air pollution problem from thetransportation sector are mainly of command-and-control approaches (Sadat et al.2005). The national regulatory instrument is based on the Indonesian National

1PM-10, SO2 and NOx refer to particulate matter smaller than 10�m, sulphur dioxide and nitrogenoxide, respectively.2Vehicular emission load: share of emission from vehicle to ambient air.

Valuing the Benefits of Cleaner Air in Jakarta Metropolitan Area 281

Air Pollution Control Policy (NAPC).3 The NAPC sets the standards for ambientair quality, emissions, noise and air pollution. To meet all these standards, localgovernments are assigned a central role in keeping the air quality above theidentified thresholds (GOI 1999, Article 18). The Ministry of Environment releaseda decree (the Ministry of Environment Decree No. 141/2003) regarding NewType and Current Production Motor Vehicle Exhaust Emission Standards (CPMVstandard). This decree has tried to address the discrepancy between indirect ambientair pollution control and direct pollution control by controlling the source: vehicles.The CPMV standard also considers available air pollution control technology toreduce vehicular emission (KLH 2003).

In addition to national regulation, Jakarta Province has set its own Jakarta AirPollution Control Regulation (Jakarta APC, Jakarta’s Provincial Regulation No.2/2005). With regard to air pollution caused by mobile sources, this policy makesperiodic vehicular emission assessment compulsory for all types of vehicles. It alsospecifies that all public transport and local government fleets should use liquefiedpetroleum gas (LPG) as the energy source instead of petrol or diesel since the LPGemission load is lower than that of petrol and diesel and because LPG stations areavailable in the Jakarta Province area (Bappenas 2006).

Academics and policymakers are discussing newly proposed policies to controlair pollution from the transportation sector in JMA. The first policy involves theimprovement of public transport facilities in Jakarta Province: building bus rapidtransport (BRT) facilities including special bus corridors and bus stops, buildingmonorail facilities, improving light rail facilities and providing walking paths andbike lanes. The scenario for this first policy was such that, if the above planand campaign were successful, JMA citizens would reduce private vehicle usage,especially in the Jakarta Province. The critical stage in scenario development forthis policy was determining changes in vehicle fleet composition resulting fromthe implementation of the new policy. All information used to develop the firstscenario was obtained from studies conducted by the Institute for Transportationand Development Policy (ITDP 2003, 2005, 2007). The ITDP conducted surveysasking current BRT users about their mode of transport prior to the availabilityof BRT. These survey data were used to estimate further mode shifting in thefuture.

The second policy proposed is designed to reduce the number of private vehicleson the streets. The policy would be implemented by raising parking fees in publicareas such as shopping centres, by applying fees in public areas such as offices andby collecting entry fees to ‘busy areas’. Under this policy, busy areas are definedas the five cities in Jakarta Province with the highest PM-10 concentrations. In thisstudy, scenarios developed for the second policy are purely hypothetical since there

3Government Regulation No. 41/1999: National Air Pollution Control, mandated by Law No.23/1997, Environmental Management.


was no previous study available to aid the analysis. The scenarios assume that thenumber of private vehicles, private cars and motorcycles only, operating in five citiesin the Jakarta Province, is reduced to 50 %, 30 % and 10 % below the 2004 levels,respectively. We assume that a reduction in vehicle numbers would reduce PM-10concentration.

This third policy involves raising registration fees for old cars and motorcycles.Old vehicles are defined as vehicles that do not comply with the new standardstated in the Ministry of Environment Decree No. 141/2003 (KLH 2003). Nugrohoet al. (2005, p. 26–27) estimate the ratio of new to old vehicles operating in theJMA streets from 2003 to 2015. They used current 2002 vehicle numbers andavailable vehicle growth data from the National Police Department. For instance,according to their model, in 2015 about 57 % of operating passenger cars will becars that comply with new emission standards as stated in MED No. 141/2003(KLH 2003).

3 Choice Modelling

Choice modelling (CM) is a survey-based approach to estimate changes in welfare(Hanley et al. 2001). This approach has been used in various countries for differentsectors, such as water resource sector in the Australian Capital Territory (Blameyet al. 1999), mangrove management in Malaysia (Othman et al. 2004) and croplandconversion in North West China (Wang et al. 2006). In a CM application, changesin welfare are estimated using the choices that the respondents pick from alternativefuture use options that are described using attributes of the goods in questions.In the various options presented to survey respondents, the attributes take ondifferent levels. One necessary attribute is monetary value. By observing the choicesrespondents make, the analyst can estimate the willingness to pay for improvementsin single attributes or combinations of attributes – using the monetary attribute asthe proxy to price the other attributes. In this research, the good in question is theJMA’s ambient air condition. The good was defined using four attributes: numberof restricted activity days caused by upper and lower respiratory illnesses (numberof RADs), visibility level, odour level and cost of policy implementation. In the CMquestionnaire, all attributes, policies and choice sets were presented using cartoonillustrations which are commonly used in Indonesia for communicating ideas, publiccampaigns and advertisements. This strategy was found to suit Indonesia’s conditionand was appropriate for respondents with diverse educational attainments and whohave a high reliance on spoken rather than written presentation.


Fig. 1 Problem description (Source: Authors’ own design)

A typical CM questionnaire has the following components: problem definition,4

payment of vehicle tax,5 choice sets,6 socioeconomic questions7 and attitudinalquestions8 (Morrison and Bennett 2004). These components need to be definedclearly and should be tested before survey implementation so that all componentscan be well understood by respondents to ensure reliable responses (Blamey et al.1999). In many CM studies, focus group discussions are used to assist with theproblem of statement refinement, attributes and the payment of vehicle tax selectionand also in questionnaire communication (Blamey et al. 2000). In our research, thequestionnaire was refined using inputs from (1) experts on survey and air pollutioncontrol policy, (2) participants of focus group discussions and (3) field tests.

The questionnaire involved the use of seven show cards explaining (1) descrip-tion of the problem (Fig. 1), (2) description of the current condition and proposedpolicies (Fig. 2), (3) explanation of the choice sets and (4) four choice set cards for

4Problem definition: description of the environmental issue and potential solutions.5Payment of vehicle tax: means of paying for the potential solutions used in the questionnaire.Payment of a vehicle tax should be well accepted among respondents, being a secure and reliableindication that the contribution will be used for the right purpose.6Choice set: presentation of the choice options available to respondents, described using attributesthat take on differing levels across choice options.7Socioeconomic question: questions designed to gather information such as monthly expenditureand education attainment.8Attitudinal question: questions designed to obtain respondents’ attitude and opinion towards thegood in question.


Fig. 2 Description of current conditions and new policies (Source: Authors’ own design)

every respondent (Fig. 3). A combination of four vehicle tax payments was used:higher land and property tax, higher vehicle tax, higher parking fee in central Jakartaand payment to enter Central Jakarta. Thus, the choice of an option that involved apayment implied that the respondent would be faced with a mixture of increased feesand charges. The mixed payment vehicle tax was used to overcome issues such ashigh number of protests caused by a specific vehicle tax payment and was designedto link all proposed policies. A respondent has to choose one of the four choice setcards presented.

To construct the choice sets, combinations of attribute levels were used, followingan efficient experimental design (Table 1). Prior values of the implicit prices of theattributes were estimated based on pilot surveys using the orthogonal design. Theefficient design was chosen according to the lowest D-error (Rose et al. 2008). Thefinal 24 choice sets were distributed into 6 blocks (hence the four choice set cards foreach respondent). The orthogonal design for the choice sets used in the pilot surveyswas constructed using the LMA approach (Louviere et al. 2000 cited in Street et al.2005).

The survey was implemented using the following steps: (1) sampling designs, (2)interviewer training, (3) pilot surveys and (4) surveys. A cluster sampling methodwas applied (Figs. 4 and 5), as it is the most suitable sampling method for JMA’scondition where most citizens live in geographically unstructured neighbourhoods.


Fig. 3 Choice sets (Source: Authors’ own design)

Table 1 Attributes and levels

Attributes Unit Status quo levels Experimental design levels

RAD Days 4 3, 2, 1Visibility Metres 10 30, 50, 70Odour – Very disturbing Disturbing, slightly disturbing, not disturbingCost Rupiah 0 100,000; 500,000; 900,000

Thirteen subdistricts were selected from the 166 subdistricts in the JMA. Thesubdistricts were proportionally selected according to PM-10 levels and averageincome. Five villages were selected within every subdistrict and two sub-villageswere selected from every village. Sub-villages were used to create primary samplingunits (PSUs). The PSU was the smallest sampling unit consisting of 50 householdswithin one sub-village. Five households per PSU were interviewed. Interviewertraining began with an explanation of the survey objective and questionnaire content.The researcher explained the objective of every question and discussed appropriateprobes to each. The interviewer then practised reading and pointing to the assignedshow card. A discussion session of possible difficulties in the field and role playingcompleted the training. The pilot survey was conducted in 3 subdistricts and themain survey was conducted in 13 subdistricts.


JMA

cities and districts

sub district

village villagevillage

sub villagesub village

village village

sub district12 sub districts wereselected for everycity/districttrict

5 villages in every subdistrict were selectedrandomly

2 sub villages in everyvillage were selectedrandomly to create 2 PSU

sub district

Fig. 4 Illustration of the cluster sampling method

Fig. 5 Sampling areas


4 Model Specification

4.1 Conditional Logit Model

A CM application is based on the behavioural assumption modelled under therandom utility theory that consists of two parts: observable (Vij) and unobservable("ij) components (Verbeek 2004):

Uij D Vij C "ij (1)

In a conditional logit (CL) model, the utility functions (Vij) consist of observedindividual, choice invariant characteristics (zi) and attributes of the choices (xij)(Greene 2007), and then Vij can be written as

Vij D ˇixij C yijzi (2)

This model has a weakness relating to the assumption that all "ij are independent.This property is called independence of irrelevant alternatives (IIA). To analysewhether or not the IIA is violated, a Hausman and McFadden test is used. Itcompares two models with all variables to a model using a subset of variables.If the property of IIA is violated, other models which relax the IIA property areavailable. These models are multinomial probit model, nested logit model andrandom parameter logit model (Wang et al. 2006).

The indirect utility function, Vij, can take different models, the simplest one beinga linear model (Eq. 3 below) where ASC is the alternative specific constant, ˇ is avector of the estimated parameters and X is a vector of k attributes of the good inquestion from a choice set (Wang et al. 2006). ASCs are included to represent theinfluence of freestanding emotions and presentation effects (Blamey et al. 2000).Such inclusions can also improve the model fit (Adamowicz et al. 1997):

Vij D ASC CX

ˇkXk; where i D 1; : : : ; k (3)

IP D �ˇkˇc

(4)

CS D � 1ˇc.V0 � V1/ (5)

Models are compared based on the log likelihood values, rho-squared (�2) andchi-squared statistics (�2) (Rolfe et al. 2000). Coefficients from the best availablemodel are used to calculate implicit prices (Eq. 4) – the trade-off respondents arewilling to take on average between attributes (Bennett and Adamowicz 2001), whereˇk is the coefficient of the attribute in question and ˇc is the coefficient of costattribute calculated from the model (Rolfe et al. 2000). Compensating surplus is


estimated using Eq. (5) where V0 is the utility of the current or status quo conditionand V1 is the utility of the new proposed conditions (Wang et al. 2006).

4.2 Random Parameter Logit Model

The random parameter logit (RPL) model assumes ‘heterogeneity’ among respon-dents (Liljenstolpe 2008; Lusk et al. 2007). Dispersion around the coefficients’means is an indicator that heterogeneity exists within the model. CL models cannotcapture dispersion and hence may lead to an imprecise estimation of populationpreference (Hynes et al. 2008; Wang et al. 2006; Lusk et al. 2007). Recognition oftaste heterogeneity is therefore important to avoid bias that otherwise may result inpoor policy selection (Hynes et al. 2008; Birol et al. 2006).

In the CL model, the parameters, ˇk in Eq. (1), are fixed and take the same valuefor all respondents. The RPL, on the other hand, introduces a random component inthe parameters (Eq. 6), where � is a constant,�zk produces individual heterogeneityand ��kvk is a triangular matrix with selected distributional function in its diagonal.Then, Eq. (3) can be rewritten as Eq. (7) where ˇk is a vector of fixed coefficientand � k is a coefficient vector that is randomly distributed across individual randomparameters (Greene 2007):

�k D � C�zk C ��kvk (6)

Vij D ASC CX

ˇkXk CX

�kXk (7)

To explain the sources of heterogeneity, interaction between socio-demographicvariables with ASCs is included in the model, so that RPL can pick up preferencevariation to improve the model fit (Birol et al. 2006).9

5 Results and Discussion

5.1 Sample Representation

To check the sample representativeness, chi-squared tests were used. Respondents’socioeconomic status (SES) was not significantly different from the populationwith the chi-squared result equal to 2.10, which is lower than the critical value(12.59 with 6 degrees of freedom at the 0.05 level). Across the sample, the gender

9Some studies report that there is no improvement in model fit by implementing RPL (Provencherand Moore 2006), but others find that significant improvements are achieved (Birol et al. 2006).


compositions were the same as the JMA population, namely, 51 % male and49 % female. The age structure was also not significantly different from the 2008projection by the Statistics Indonesia with the chi-squared statistics equal to 3.64,which is lower than the critical value (12.59 with 6 degrees of freedom at the 0.05level).

5.2 Estimation of Utility Function Using Conditional Logitand Random Parameter Logit Models

Four utility functions (Eqs. 8, 9, 10, and 11) were estimated simultaneously. Alter-native specific constant (ASC) is included in each of the policy utility equations: ATSfor public transportation improvement, ARD for reducing vehicle density in busyareas and ARO for reducing the number of old vehicle ownerships in the JMA.Respondents’ preferences for new policies (as opposed to the status quo) werecaptured by the three ASCs. Illness, Visibility and Cost are continuous variables,while Odour is a categorical variable. Therefore, the Odour variable was separatedinto Odour 1 (from disturbing to slightly disturbing condition) and Odour 2 (fromslightly disturbing to not disturbing condition):

VSQ D IˇI C VˇV C O1ˇO1 C O2ˇO2 C CˇC (8)

VTS D ATS C IˇI C VˇV C O1ˇO1 C O2ˇO2 C CˇC (9)

VRD D ARD C IˇI C VˇV C O1ˇO1 C O2ˇO2 C CˇC (10)

VRO D ARO C IˇI C VˇV C O1ˇO1 C O1ˇO2 C CˇC (11)

Two CL models were estimated to obtain coefficients for the above four equations(Table 2). The first model is an ‘attributes-only’ model and the second modelincludes other explanatory variables. The probability of a ‘change’ option beingchosen was hypothesised to increase with (1) lower RADs, (2) higher visibility, (3)lower degree of odour disturbance and (4) lower cost. All choice attributes werefound to be significant and have the expected signs except for Visibility which wasinsignificant in both models. All ASCs were significant and negative. The results ofthe first model (Model 1) demonstrate that the respondents support reductions in airpollution. However, all the ASCs were negative suggesting that respondents opposethe implementation of new policies. Negative ASCs show a ‘status quo bias choice’(Mazzanti 2001) or reluctance to move from the current condition (Kerr and Sharp2008; Concu 2006; Brey et al. 2007). In the survey, 173 respondents consistentlychose the status quo condition. The debriefing question showed that respondents’beliefs that their contribution will not be used to create better ambient air conditions


Table 2 Estimation results for CL and RPL models

Model 3 (RPL)Variables Model 1 (CL) Model 2 (CL) Coefficient Standard deviation

RAD �0.240 *** �0.248 *** �0.247 *** 0.324 ***Visibility 0.002 0.002 0.002Odour 1 �0.377 *** �0.407 *** �0.436 *** 0.651 ***Odour 2 �0.097 ** �0.091 ** �0.037Cost �0.002 *** �0.003 *** �0.003 ***ATS �0.220 * �1.003 ** �0.868 *ATS*Age �0.015 ** �0.018 ***ATS*Gender �0.351 ***ATS*Education 0.415 *** 0.472 ***ATS*Income �0.231 *** 0.268 ***ATS*Cough �0.323 ** �0.393 **ATS*Distance �0.035 *** �0.042 ***ATS*PM-10 �20.824 ** �25.880 ***ARD �0.382 *** �1.002 ** �0.894 *ARD *Age �0.017 *** �0.020 ***ARD *Gender �0.521 *** �0.612 ***ARD *Education 0.443 *** 0.513 ***ARD *Income 0.231 *** 0.271 ***ARD *Cough �0.441 *** �0.517 ***ARD *Distance �0.036 *** �0.043 ***ARD *PM-10 �38.490 *** �44.344 ***ARO �0.444 *** �1.078 ** �0.940 *ARO *Age �0.005 �0.007ARO *Gender �0.390 *** �0.430 ***ARO *Education 0.397 *** 0.447 ***ARO *Income 0.188 *** 0.229 ***ARO *Cough �0.290 * �0.327 *ARO *Distance �0.042 *** �0.050 ***ARO *PM-10 �27.128 *** �32.428 ***LL function �3090.273 �2799.018 �2791.552Restricted LL �3587.730AIC 2.394 2.186 2.181BIC 2.412 2.251 2.251Chi-squared 839.764 1422.274 1592.356Probabilitychi-squared

0.000 0.000 0.000

Rho-squared 0.120 0.203 0.222

Notes: *, **, *** refer to significance at 10 %, 5 %, and 1 %, respectively


might be the main cause of the negative ASCs. Another possible cause was that thelowest cost level (Rp100,000) was set too high, shifting too many respondents to thestatus quo condition.

In the second model, a combination of explanatory variables was added toobserve other possible explanations for variations in choices. These are socio-demographic variables (age, gender, number of kids, education and income), airpollution-related illnesses (fever, cold, cough, asthma), habits (smoking, averagenumber of hours spent outdoors) and location-specific condition (pollutant con-centration: PM-10 total, dispersion and emission as well as respondents’ homedistance from JMA centre). All those variables were interacted with all ASCs sothat respondents’ preferences can be linked to proposed policies.

The socio-demographic variables, Age, Gender, Education and Income, areconsistently significant. Young women with relatively high education and incomewere more likely to choose improvement over the current condition.

None of the PM-10 related illnesses consistently appeared as significant deter-minants of choice. Asthma was significant only for RD policy, while Coughwas significant for TS and RO policies. Respondents who suffered Asthma inthe past month of the survey appeared to choose improvement over the currentcondition. Unexpectedly, respondents who suffered Cough did not appear to chooseimprovement.

The third group of explanatory variables were respondents’ habits. Smoke wasnot significant for the RD policy but was significant for both the TS and ROpolicies (at 1 % and 5 %, respectively) indicating that people who smoke tend tochoose improvement. Outdoor, the average number of hours spent outdoors, was notsignificant for RO policy but significant for TS and RD policies. The negative signswere unexpected since they indicated that the more average hours spent outdoorsthe less respondents wanted the improvement.

The fourth group of explanatory variables was location-specific conditions: PM-10 concentration in the respondents’ living area and distance from JMA city centre.PM-10 was a significant variable across different policies. The Distance variableturned out to be significant, indicating that respondents who lived relatively closeto the centre of JMA were more likely to choose improvement over the currentcondition.

Different combinations of CL models were estimated to find the best fit accordingto the rho-squared statistic and the significance of variables. Model 2 was the bestmodel formulated with 0.203 rho-squared and significant explanatory variables. Inthis final model, the explanatory variables were Age, Gender, Education, Income,Cough, Distance and PM-10.

To check for IIA compliance in the CL model, the Hausman test was conductedby restricting one utility function at a time. The results are presented in Table 3.Model 1 was an ‘attributes-only’ model, while Model 2 was found to fit the databest with significant explanatory variables. The Hausman test for Model 1 showedthat the IIA assumption could be rejected for all ‘new policies’ options, while forModel 2 the test showed that the IIA assumption cannot be rejected for RD policy


Table 3 Hausman testresults for Model 1 andModel 2

Excluded choicesCL Model TS RD RO

Model 1 p-value 0.009 0.004 0.010Model 3 p-value 0.000 0.984 0.071

Table 4 Comparison of implicit prices (in thousand Rupiah)

CL (Model 2) RPL (Model 3)Variables IP CI (95 %) IP CI (95 %) Proportion of difference

RAD �97.716 ˙0.946 �88.857 ˙1.069 0.491Visibility 0.929 ˙0.051 0.602 ˙0.050 0.000Odour 1 �162.431 ˙1.125 �155.886 ˙1.142 0.471Odour 2 �35.741 ˙1.029 �13.014 ˙1.120 0.982Rho-squared 0.203 0.222

(Table 3). Since the overall results failed to reject the IIA assumption, the randomparameter logit (RPL) model which relaxes the IIA assumption was estimated.

An RPL model was constructed by including the explanatory variables found tobe significant in Model 2. After testing for different combinations of random param-eters, as suggested by Hensher et al. (2005), only the parameters that consistentlyappeared to have significant standard deviation were set as random. Initially, allattributes, except Cost, were set as random parameters. Then, Visibility and Odour2 were set as fixed parameters since both consistently showed insignificant standarddeviations. All ASCs (ATS, ARD and ARO) were also set as fixed parameterssince in the ten replication tests none of them had a significant standard deviation.Correlation among random parameters was tested and found to be low. The finalmodels were estimated without allowing for correlation among parameters.

For the RPL model (Model 3), all ASCs were again negative and significant.Significant attributes were Illness, Odour 1 and Cost. Visibility and Odour 2were insignificantly different from zero. All standard deviations for attributes setas random parameters were significant, indicating that unconditional unobservedheterogeneity existed for these attributes. Individual characteristics, interacted withASCs, were added into the model to improve the model fit.

5.3 Model Comparison

The implicit prices were calculated using Eq. (4). Coefficients used in the calcula-tion were generated using Model 3. The implicit prices revealed that respondents inthe JMA were willing to pay for changes in health and environmental conditions(Table 4). Implicit prices estimated using CL and RPL were not significantlydifferent except for Odour 2 since it was significant in the CL model but not inthe RPL model (Table 4, last column).


5.4 Compensating Surplus

To calculate the compensating surplus, each ‘change’ scenario and the status quoneed to be specified. The ‘change’ scenarios were based on the attribute levelsdefined in Table 1: low impact, medium impact and high impact, using first, secondand third levels for all attributes, respectively. The three ‘change’ scenarios for eachof the three new transportation policies, improvement of the transportation facilities(TS), reduction of vehicle numbers in the city centre (RD) and reduction of oldvehicles (RO), were investigated:

1. Low impact: an average of three RADs caused by respiratory-related illnesses,30 km of visibility range and a disturbing odour from the transportation sector

2. Medium impact: an average of two RADs caused by respiratory-related illnesses,50 km of visibility range and a slightly disturbing odour from the transportationsector

3. High impact: an average of one RAD caused by respiratory-related illnesses,70 km of visibility range and a not disturbing odour from the transportation sector

The average compensating surplus for households was calculated using Eq.(5) where ˇc is the marginal utility of income represented by the coefficientof the cost attribute, V0 is the utility of the current condition and V1 is theutility of the new proposed condition, with the ASC for each policy included inthe calculation. Compensating surplus represents respondents’ willingness to payfor the proposed transportation policies. Using the above four scenarios, threecompensating surpluses were estimated for each new transportation policy (Table 5).

Using estimation results from the RPL model, on average (using medium-impactscenario), respondents in the JMA were willing to pay Rp447,940 (USD54.40),Rp489,258 (USD55.46) and Rp503,555 (USD57.32)10 per household per annumover a 3-year period for the implementation of TS, RD or RO policies, respectively(Table 5, RPL medium in bold). Even though respondents tended to choose thestatus quo, their average willingness to pay for the new transportation policies wasrevealed as positive.

Table 5 Comparison of compensating surplus for each scenario across three new transportationpolicies (in thousand Rupiah per household per annum over a 3-year period), RPL model (Model 3)estimation results

Policies High CI Medium CI Low CI

TS 634.19 ˙16.40 447.94 ˙18.76 321.70 ˙19.08RD 643.50 ˙17.22 487.26 ˙19.58 331.02 ˙38.98RO 659.80 ˙17.47 503.56 ˙19.83 347.31 ˙59.13Rho-squared 0.222

10Indonesia’s GDP per capita in 2010 was USD2946 (The World Bank 2012).


Table 6 Present value of total benefit for three new transportation policies for 3-year period

NPV in million USD NPV in trillion RpDiscount rate (%) 6.75 9.51 12.75 6.75 9.51 12.75

PoliciesTS 498 474 448 4373 4161 3934RD 507 483 456 4459 4242 4010RO 524 499 472 4608 4384 4144

The welfare estimates reported in Table 6 for the RPL model (medium-impactscenario) were aggregated over the whole sampling frame to determine the totalbenefit for the three new policies using Eq. (12), where CSA is the aggregate welfareestimates, RR is the response rate, P is population, r is the discount rate and n is thenumber of years:

CSA D CS � RR � P � n � 1.1C r/n (12)

In the survey, the number of households contacted was 1170, and the numberof households that agreed to be interviewed was 647; therefore, the fraction of thesample who agreed to take part in the survey was 55 %. The number of householdsfor the whole of JMA in 2008 was approximately 6,310,790. Using these figuresto extrapolate the total benefit for each policy per annum results in USD230m,USD219m and USD228m for TS, RD and RO, respectively. The present value oftotal benefit for each policy over a 3-year period is presented in Table 6 (using threediscount rates11). These benefit estimates can then be compared with the presentvalues of the respective policy costs.

6 Conclusion

The results presented here provide an understanding of JMA citizens’ values for lessRAD, better visibility and reducing odour by decreasing air pollution represented byPM-10. Since air is a non-market good, respondents’ preferences were elicited via ahousehold survey using the choice modelling method. This method was used since itcan separate respondents’ values regarding air quality improvement into air qualityattributes derived from different policy instruments.

Most choice modelling studies have been conducted in developed countries withlimited examples available from developing countries. The main challenges foundin their application of CM in Indonesia were the low education level of respondents

11Discount rates used were 6.75, 9.5 and 12.75 % as minimum, average and maximum discountrates for Indonesia from July 2005 to July 2009 (www.bi.go.id, 30 July 2009).

http://www.bi.go.id/


and their high dependency on oral presentation. Consequently, respondents’ relianceon the interviewers was very high. Three strategies were used to overcome theproblem: (1) using show cards to describe the issue and the proposed solution to theissue and to present choice sets, (2) creating a ‘story-like’ questionnaire so that therespondents could be guided through the whole questionnaire by the interviewers(this was done by using conversational language in the written questionnaire thatwas read by the interviewers) and (3) training of interviewers so that they werecapable of delivering the questionnaire using show cards and at the same time tellingthe ‘story’ so that the respondents could understand the linkage between problems,alternative solutions and choice exercises.

Survey results were analysed using two models: the conditional logit model andthe random parameter logit model. Initially, the CL model was used, but since theIIA assumption was violated, the RPL model which relaxes the assumption wasimplemented.

Using results from the modelling stage, it can be concluded that the respondentsin JMA have significant non-market values for air quality attributes, especially forIllnesses and Odour caused by air pollution from the transportation sector, andthey were willing to pay for the changes. However, respondents were reluctant tochoose one of the three proposed transportation policies. The main reasons mightbe because they did not believe that their contribution would be used to reduce airpollution from the transportation sector since most of policies implemented in theJMA have failed to remedy the problem. Another possible cause was that the lowestcost level (Rp.100,000/year) was set too high, shifting respondents’ preference tothe status quo condition. Nonetheless, on average, respondents in the JMA werewilling to pay Rp447,940 (USD54.40), Rp489,258 (USD55.46) and Rp503,555(USD57.32) per household per annum over a 3-year period for the implementationof TS, RD or RO policies, respectively.

Estimated net present values for total benefits can be used in cost-benefit analysesto estimate the total net benefits of implementing new transportation policies. Theresults can assist the local government in JMA in selecting the best transportationpolicy in JMA, the one that maximises the net benefit to the society.

Acknowledgements The authors acknowledge the financial support of the Economy and Environ-ment Program for Southeast Asia, the Australian National University’s Vice Chancellor’s Officeand Australia’s DFAT-Aid. All mistakes however are the authors’ own responsibility.

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