Understanding Urban Congestion in Jakarta: Causes, Impacts, and Solutions, Lecture notes of Business

Download Understanding Urban Congestion in Jakarta: Causes, Impacts, and Solutions and more Lecture notes Business in PDF only on Docsity! 1 How Does Congestion Matter for Jakarta Citizens? 1 Sonny Harry B Harmadi (FEB, Universitas Indonesia) Muhammad Halley Yudhistira (FEB, Universitas Indonesia) Decky Priambodo Koesrindartono (FEB, Universitas Indonesia) Abstract Jakarta, as the biggest city in Indonesia, faces many problems, one of which is congestion, that produces a high cost economy. It is predicted that if the government does not take immediate action to solve this problem, there will be a potential loss of IDR65 trillion by 2020 (Bappenas, 2007). This consists of IDR28.1 trillion in operational costs and IDR36.9 trillion in opportunity costs from time lost. This study is aimed at estimating how much Jakarta citizens’ are willing to pay to overcome the congestion problem. By using the stated preference method, the estimation result shows that the annual cost of congestion in Jakarta is estimated at IDR50.2 trillion a year. Furthermore, this result can be used as a baseline for a cost-benefit analysis by the government to generate a better public transportation policy in Jakarta. Keywords: willingness to pay, congestion, stated preference method, conditional logit 1 This manuscript has been published in the Journal of Indonesian Economy and Business, Volume 30, Number 3, September 2015, p. 220-238. 2 1. INTRODUCTION Congestion is common phenomenon in every big city in the world, including in Jakarta. The increasing flows of vehicles into and out of Jakarta causes roads in Jakarta become increasingly congested. This is not surprising given the fact that Jakarta is the economic center on western Java area. In fact, one can also say that all of the Indonesian economic activity is centered in Jakarta. Although congestion has become inevitable, the condition of congestion in Jakarta has reached worrying level. Congestion is a form of negative externalities that could lead to economic inefficiency. Time to travel from one place to another place becomes longer which implies greater opportunity cost. As a result, the cost to make one trip would also become larger. These conditions become one of the problems in the economic growth and development in Jakarta, which certainly also affect the development of area around Jakarta. The travel time within Jakarta city has increased nearly twice since 1985 until 2000 (Harmadi, 2006). The estimated cost arose from congestion, car accidents, and pollution has reached about trillion of rupiah per year. Moreover, it is predicted that if congestion problem cannot be immediately resolved, then the potential loss will reach 65 trillion rupiah by the year 2020 (Bappenas, 2007). This predicted loss was calculated based on two parameters only, i.e. losses due to the vehicle operating costs amounting to 28.1 trillion rupiah and the time loss which is estimated around 36.9 trillion rupiah. These calculations have not included the costs of environmental deterioration (i.e. the costs arose from various kinds of pollution such as air pollution and noise pollution), failed transaction, declining productivity and competitiveness compared to other major cities in Southeast Asia region (Bappenas, 2007). Certainly, congestion in Jakarta has fundamental problems that must be quickly resolved, one of which is the disproportional transportation structure and systems. The amount of vehicles on Jakarta‟s roads grows faster than the existing roads. As an illustration, 5 thus they would have their own preferences to avoid congestion. These preferences are reflected through the amount of cost they are willing to pay to avoid congestion. 2. LITERATURE REVIEW Congestion is one of the urban problems that always raise interesting discussion. Congestion is an example of negative externalities resulted from the traffic flow passing through roads. Externalities can be formed as an increase in travel time, noise pollution, air pollution, excessive fuel consumption, and car accidents (Button, 1995). Of all the externalities arose from congestion, the problem of travel time has been the most discussed topic in researches about urban congestion. Each road user, particularly the vehicle users or drivers, will compete with other vehicle users up to certain degree. In other words, roads will have rivalry characteristic starting from certain stage. Therefore, any additional vehicles passing through the road must cause longer travel time faced by vehicle users. Hence, it would cause externalities in form of greater commuting costs for each vehicle users along with larger opportunity cost (Sullivan, 2006). ------------------------------------------------------------------------ Figure 1. Externalities and Congestion Tax ------------------------------------------------------------------------ Externality in form of greater commuting costs has caused the equilibrium not to reflect optimum number of vehicles on road. The nature of negative externality as uncalculated costs will eventually cause the number of vehicles on road exceeds its optimum level. The following result is the emergence of excessive traffic congestion or the so-called as congestion. The internalization of this external cost can be performed through imposing the instruments of congestion tax. This taxation shifts marginal private cost faced by each driver to be equal to marginal social cost of the economy, so the equilibrium will be finally an 6 optimum point. Within the larger framework, the existence of marginal congestion costs should be included in the valuation scheme of the road pricing (Anderson and Bonsor, 1974) as shown in Figure 1. Figure 1 shows how the congestion externalities occur and distort the economy. Equilibrium occurs at point i where marginal benefit equals to private trip cost faced by vehicle users on road. As there are externalities arose due to the increased travel time of each vehicle users resulted from the increased number of vehicle users, the cost that should be faced is marginal trip cost, which accommodates these externalities. Thus, the optimum level will occur at point e, where the number of vehicles on road is smaller (1400 vehicles) compared to the equilibrium level (1600 vehicles). The process of externality internalization is conducted through applying congestion tax that shifts the cost structure faced by vehicle users, i.e. shifting private trip cost to marginal social cost. In practice, congestion tax has various forms such as toll road taxes, fuel taxes and parking taxes (Sullivan, 2006). Other forms of taxation are including tax on tire and other spare parts, as well as direct road taxes that electronically applied via smart cards, optical systems, infrared, etc. (Johansson and Mattsson, 1995; Johansson-Stenman, 2005). Those forms of taxation could be implemented as long as it satisfies benefit and equity principles (Stiglitz, 2000). The optimal tax can be determined through the public road and vehicle users‟ preferences on the existing congestion. It is necessary to determine how much the willingness to pay of citizens for congestion since the main objective of congestion tax is to internalize the costs of congestion so that the existing congestion can be reduced. Congestion is also a loss to the economy, so what to be determined here is the willingness to pay of people to overcome this loss (Pearce and Turner, 1990). In this case, public road users must determine 7 how much cost that they are willing to incur in averting congestion based on current traffic congestion condition. One example of study concerned on willingness to pay for congestion is the study conducted by Tretvik (1995). In his study, Tretvik estimated the magnitude of willingness to pay for time savings of the users of Trondheim toll road. The simulations were carried out by using panel data and by dividing the toll road users into three groups based on the travel purposes, i.e. group of commuting, business, and others. According to the simulation results, it was found that the value of time saved by each group was different for each group. The group of business trip has the highest value of time saving, followed by the group of others trip and the group of commuting trip. Furthermore, the results of this study showed that the willingness to pay for time saving for each Trondheim toll road users were 73 NOK per hour for commuting group, 120 NOK per hour for others trip group, and 138 NOK per hour for business trip group. Apart from using the approach applied by Tretvik, the calculation for willingness to pay can also be estimated by other methods, e.g. Stated Preference Method (SPM). This method, which will be used within this research, is often used to observe cases relating to natural resources and environment issues. There are several related studies that also use this method. Patunru et al. (2007) used SPM to estimate benefits obtained from the cleaning up of pollutant substances in Waukegan Harbor, Illinois. By developing the determined attributes, it was concluded that the willingness to pay of the homeowner for overall clean up would cost a minimum cost of US$582 million. The method implemented by Tretvik can be classified as Contingent valuation method which is a common method to quantify, or, in some other occassions, to monetize, people‟s preferences. In the early 1990s, Contingent valuation (CV) method is critized as dubious, at best, or even hopeless. Hausman (2012) finds out three enduring problems: 1) hypothetical 10 then compensated by the extent to which people are willing to pay for that improvisation, which also represents the compensating variation of utility function. In order to estimate the amount of welfare (utility) change that occurs if congestion problems in Jakarta were reducible, this study uses random utility model which includes the error term into the consumer utility function. This model is then approximated by choice- modeling analysis and is solved by econometric method of conditional logit. In addition, the estimation of willingness to pay for averting congestion in Jakarta involves primary data which are obtained by using questionnaires. The questionnaire is arranged based on predetermined attributes and is distributed to respondents, the Jakarta road users. 3.1. Sampling Procedures In order to obtain accurate estimates and generalizations, probabilistic sampling is necessarily required. This technique requires a random sampling from the population list so it can provide equal opportunity for each individual in population to be selected as sample (Sugiyono, 2002). In this study, the unit of analysis of the population is the entire citizens, both of the citizens of Jakarta and outside Jakarta who use the transportation facilities within Jakarta. The best ideal technique to use is stratified random sampling, given the population structure. Hence, this study needs a list of entire population that uses transportation facilities in Jakarta. Problems arose when it was finally realized that acquiring a list of transportation facilities users is almost impossible because the related population database is not available. Consequently, the sampling process that was originally planned to use stratified random sampling method cannot be done at all. In fact, all types of probabilistic sampling approach cannot be used, therefore, the method used in this study is non-probabilistic sampling, i.e. purposive sampling method. 11 The scope of this research is mostly located in business and office center areas in Jakarta. These areas are the targeted areas in which road pricing is going to be applied. The main criterion in determining the selected areas depends on the area characteristics itself. These areas are characterized as congested areas and also areas containing many working citizens having relatively high education level. The selected areas include: National Monument (Monas), SudirmanThamrin street, HR Rasuna Said street, Gatot Subroto street (i.e. the end of Sudirman street and HR. Rasuna Said street), and Prof. Dr. Satrio street (which is bounded by Sudirman street and HR. Rasuna Said street). Based on the Study on Integrated Transportation Master Plan for Jabodetabek (Jakarta, Bogor, Depok, Tangerang and Bekasi) (SITRAMP, 2004), these areas have the best qualifications based on the simulation results in observing which area is producing the highest percentage reduction in the number of vehicles due to the implementation of „3 in 1‟ policy. 3.2. Questionnaire Design Estimation about how much cost the citizens are willing to pay in order to reduce congestion involves survey approach on primary data of roads users in Jakarta. The status quo is defined as the current condition faced by society in form of severe congestion problems. This ex ante condition becomes the basis for predicting how the people of Jakarta are willing to pay money compensation for reducing the current traffic congestion problems. Conditional logit approach is used in this study to accommodate the discrete and binary dependent variable. The dependent variable is conditional probability in choosing travel scheme (i.e. the status quo or a new type of trip). Deterministic part of utility is represented by the various travel attributes, i.e. travel time, connectivity, safety, and transportation costs. The determination of these four attributes is carried out based on expert judgments on factors 12 that people consider in travelling from and to Jakarta. In general, the operational definition of each attribute can be seen in table 1 at below. ------------------------------------------------------------------------ Table 1. Operational Definition of Each Attribute ------------------------------------------------------------------------ The use of multiple attributes in experiment design based on choice-modeling approach requires a code transformation as what has been done by Louviere (1988), which is also better known as effect codes. This effect transforms the ordinal variables into code system that can be used in econometric analysis. The code system is different from ordinary dummy variable approach. Its benchmark level is labeled with a value of −1. For example, the attributes of connectivity (K) has three levels, i.e. the status quo (KQ), moderate connectivity (KM), and excellent connectivity (TO) in which the status quo level becomes the benchmark level. These levels, KQ, KM, and KE, are given a value of -1, 0, and 1 respectively. Thus, the value of the status quo level is negative summation of utilities obtained from moderate and excellent level. There are also other variables used in this study, i.e. travel time and safety. These variables do not need to use effect codes because both of these variables are ratio variables. Of the four attributes, only the connectivity attribute that requires a transformation by using effect codes given this attribute is not continuous. On the other hand, other attributes are continuous so they do not require similar transformation. Survey method with primary data is the main point of experiment design in order to get the magnitude of welfare change. The core of survey is to ask questions to the Jakarta road users about their choices between the current travel conditions and the offered alternatives conditions. These options are explicitly asked by also including the four attributes that have been determined previously, i.e. travel time, connectivity, safety, and transportation costs. 15 The equation above illustrates how a consumer would decide his consumption decisions on various available choices. Rational consumer will maximize his utility of a number of N choices. Furthermore, if consumers have decided to choose to consume goods i, then it will formed the conditional utility function given the consumers have already selected travel ti. ̃ ( ( ) ) ̃ ( ) ̃ (7) Then, the probability of the choice of travel i is being selected by consumers is given by following equation. ( ) ( ̃ ̃ ) ( ̃ ̃ ) (8) Rational consumers always prefer to consume goods that can give them greater utility, so the difference between utility levels of two goods is an important factor in determining the consumer choice. Consumers would prefer the choice of good i relative to good j if the difference between systemic utilities of good i and j ( ) is greater than the errors difference. The probabilistic approach used in estimating random utility function within this study is conditional logit model, which will be detailed in subsection 3.4 Conditional Logit Model. Moreover, this study assumes the consumers want to increase their utility by reducing congestion, so they are willing to pay an amount of money as compensation. Hence, the Hicksian model of compensated demand function must be used to explain the consumers demand. Hanemann (1984) suggested that Marshallian demand function would be equal to Hicksian demand function when the utility function is monotonic transformation which is written as follows: ̃ ( ) ̃ (9) The symbol of γ denotes the marginal utility of income. By assuming that f(.) satisfies the condition of weak complementary, equation (9) can be transformed into a conditional utility function as shown in equation (10) below. 16 ̃ ( ) ( ) ̃ (10) The conditional utility function in equation (10) above is then transformed into the unconditional utility function, which is then used to estimate welfare change. If this conditional utility function is applied for unconditional utility function, then the new function can be written as following equation. ̃ ( ̃) , ( ) ̃ ( ) ̃ - (11) In order to calculate the amount of changes in welfare or the compensating variation (CV), one must determine the „before and after‟ conditions. Suppose the „before‟ and „after‟ conditions are denoted by 1 and 0, respectively. A good management of transportation system will lead to an increase in time savings, so consumers‟ utility will also increase as well. Meanwhile, CV measures the amount of compensation paid by consumers to attain the increased utility level. In general, this condition is formally written as follows: ( ) ( ) (12) In this case, CV has negative value (Jehle and Reny, 2000). If the compensating variation (CV) is implemented into the unconditional utility function as defined before, then we can obtain the following equation (13). ( ) , ( ) ̃ ( ) ̃ - , ( ) ̃ ( ) ̃ - (13) Thus, the amount of unconditional CV is defined through equation as follows: [ * ( ) ̃ ( ) ̃ + * ( ) ̃ ( ) ̃ + ] (14) By assuming the error value (ij) is following the extreme value distribution within the conditional logit model, the value of CV is determined by following equation. * ∑ ( ) ∑ ( )+ (15) 17 The value of CV obtained from equation (14) above is a value for one single individual only, so the process of aggregating the willingness to pay for all population can be calculated as follows: ∑ (16) denotes the weight used for each group of road users. The estimation for utility function used in this study involves four predetermined attributes, i.e. travel time, connectivity, safety, and transportation costs. The linear equation is formally written as follows: ( ) (17) WKT denotes the travel time, KONEX and KONMOD denote excellent and moderate connectivity, respectively (the status quo condition is used as the basis variable), SAFE denotes safety, and (Y–P) is a numeraire, where Y and P denote monthly income and transportation costs, respectively. Equation (17) represents the estimation of main effects of each attribute on utility function. It estimates only the effects of every single attribute on utility level without considering the interaction effects between these attributes. In fact, it is most likely that there is interaction between attributes and individual characteristics (e.g. income level). The resulted interaction effects reflect the preferences of individual characteristics on certain attribute. For instance, it is strongly predicted that high-income individuals would have higher parameter values than low-income individuals. In other words, individuals whose higher income would have larger decreased in utility level due to congestion (i.e. longer travel time) as they bear greater opportunity cost of congestion. Hence, the equation (17) is modified to include the interaction effects between attributes and individual characteristics. 20 4. FINDINGS AND DISCUSSION 4.1. Traffic Congestion in Jakarta SITRAMP (2004) has identified some of the causes and sources of traffic congestion in Jakarta. Generally, there are four main causes of traffic congestion in Jakarta, in which if these issues were not being resolved soon it will develop into more severe congestion problems. a. Road Network Problems Road network performance can be measured through direct indicators perceived by road users, one of which is the speed of vehicles on a certain road. The lower is the average speed of road users; the lower is the road network performance. The performance of road intersection and each road segment are very influential on the overall road network performance. Congestion on one particular road segment, e.g. traffic congestion due to the road constringency or the railway line intersection, can develop and lead to congestion on other road segments. The problem of missing link, inconsistent functional classification of roads, road constringency, and improper intersection conditions may cause the traffic management becomes less effective. Inadequate road network system and disorganized road hierarchy may also lead to conflicts between transportation modes and conflicts between community activities (i.e. business, school activity, etc.). Moreover, the number and length of roads in Jakarta is relatively small compared to the size of Jakarta. Table 2 shows that the ratio of road to land area in Jakarta is at 7.76 percent, which is under the ideal conditions for metropolitan cities (e.g. London and Tokyo), where it is supposed to be at 1215 percent. ------------------------------------------------------------------------ Table 2. Road Length and Road Area Ratio in Jakarta 21 ------------------------------------------------------------------------ The problems of infrastructure are also characterized by different road capacity causing the bottleneck effects, lack of traffic signs, signals or traffic control lights, mixed type of cargo and passenger transportation, and damaged road that are not immediately repaired. b. The abuse of road facilities and undisciplined road users The existence of illegal use and abuse of road facilities such as street vendors and illegal parking can decrease road capacity. Decreasing road capacity due to side friction can result in reduced performance of these roads. It is characterized by the slowing traffic flow which is thereby extending the travel time of road users. The behavior of road users such as pedestrians, passengers and drivers, either private or public transportation, can also affect the road network performance as a whole. Violation of traffic rules such as improperly crossing the road, passing through the red light, haphazardly stopping the vehicles, and loading the passenger at improper place are risky for the road users and likely to cause traffic congestion. c. Insufficient growth of road infrastructures Data of local transportation agencies of Jakarta state that there was road expansion in Jakarta by 159,293.99 meters or equivalently an increase of 2.09 percent during 2000-2003. This suggests that the road expansion was less than 1percent per year (Jakarta Macro Transportation Pattern, 2007). The slow road construction relative to the rapid increase of travel demand perhaps is one answer why traffic congestion became a daily routine in Jakarta. Road construction requires large amount of land, where for cities such as Jakarta, it will be very difficult to get as the price of land is expensive and also there are resistances from community to move away. The availability of substantial funds and the persuasion ability of local government become 22 very important. Therefore, it can be expected that the construction process of new roads in urban areas such as Jakarta will require longer time in the process of socialization, planning, and implementation phase. In addition, the rules and regulations restricting the land usage for construction of new roads, such as regulations regarding land usage and green area requirements can also hamper government efforts to increase the road network to offset the rapid growth of travel demand in Jakarta. d. High growth rate of private vehicles Growth rate of the number of vehicles far exceeds the growth rate of road network in Jakarta is a major cause of traffic congestion in Jakarta. In other words, huge demand for private motor vehicles is not accompanied by an adequate supply of road network. This condition is shown by table below which illustrates that during 2005-2009, growth in the number of motor vehicles in Jakarta in average reaches 9.7 percent per year. Although the growth rate has decreasing trend during 2005-2009, yet, the average growth rate of vehicles in Jakarta during period of 2005-2009 has increased compared to the average growth rate of vehicles during period of 1999-2004 which was about 6.3 percent per year. ------------------------------------------------------------------------ Table 3. Number of Registered Motor Vehicles in Jakarta, 2005-2009 ------------------------------------------------------------------------ Despite of its decreasing growth rates during 2005-2009, motorcycles remain ranked as transportation mode whose highest growth rate among other transportation modes used by road users in Jakarta. Table 3 above shows that the growth rate of motorcycles during 2005-2009 was always above 10 percent in each year. It also shows 25 actually only requires 37.7 minutes if there were no traffic congestion. There are 222 (55 percent) of 416 respondents chose travel time as primary consideration in selecting transportation modes. Of these 222 respondents, 110 respondents use private vehicles, i.e. motorcycles and private passenger cars. Most respondents chose private vehicles, especially motorcycles as primary transportation mode because motorcycles can shorten travel time. Furthermore, most respondents also consider transportation cost as an important factor in selecting transportation modes. The amount of transportation cost varies across respondents, ranging from IDR100 thousand per month to IDR4 million per month. Respondents must incur transportation cost of IDR654,506 per month on average. As much as 38 percent of private passenger car users are willing to pay an additional transportation cost for less than IDR100 thousand per month if only government can provide better alternative transportation modes. On average, respondents of private passenger car users are willing to pay 17 percent of additional transportation cost in order to avoid traffic congestion in Jakarta by using public transportation provided by government. In addition, there are 80 percent of respondents of motorcycle users that are willing to pay no more than IDR 100 thousand of additional transportation cost if only government can provide better alternative transportation modes. This additional cost represents their compensation to use better public transportation mode provided by government which may help them for reducing traffic congestion. In general, respondents of train and bus users are also willing to pay an additional transportation cost for less than IDR 100 thousand per month to avert traffic congestion in Jakarta as well. ------------------------------------------------------------------------ Figure 3. Important Attributes in Selecting Transportation Modes ------------------------------------------------------------------------ 26 Figure 4 shows that the benefit of main transportation modes over the alternative modes lies in the factors of connectivity and cost. The main transportation modes are including private motorcycles and private passenger cars as previously shown in Figure 2. In case of motorcycles (as a mostly used transportation mode), the respondents need not to switch transportation modes during their trip to their destination. ------------------------------------------------------------------------ Figure 4. Comparison between Main Transportation Mode and Alternative Transportation Mode ------------------------------------------------------------------------ 4.3. Regression Results 4.3.1. Main Effects The estimation results for main effects model as illustrated in the table below shows that the value of marginal willingness to pay (MWTP) for travel time is about IDR 40 thousand per minute. It is obtained from the ratio of the time-savings coefficient to marginal utility of income (MUI), the numeraire attribute (Y−P). This ratio shows the amount of additional utility measured in terms of money. ------------------------------------------------------------------------ Table 4. Summary of Estimation Results with Main Effects ------------------------------------------------------------------------ The estimation results show a significant negative value of constant of −1.626, meaning that in general, the respondents tend to choose the status quo condition rather than the offered alternative trips. There are two possible reasons behind this result. Firstly, respondents are already satisfied enough with current condition so they refuse to choose the offered alternative options. Secondly, the predetermined attributes level in the questionnaire are so 27 high, especially for attribute of transportation cost, that respondents might tend to choose the status quo option. Besides, all attributes except attribute of moderate connectivity show significant positive values in line with the hypothesis which suggest that greater positive attribute values are preferred. In other words, respondents prefer shorter travel time (i.e. greater time savings), excellent connectivity, and also lower transportation cost. The insignificant value for attribute of moderate connectivity also shows that respondents prefer greater improvement on connectivity by reducing two transportation modes at once. Moreover, the MWTP (marginal willingness to pay) of IDR40 thousand must be carefully interpreted. Such values cannot be directly interpreted as the money values of Jakarta road users put on the travel time. Instead, this estimation model is a model that estimates preferences, thus the value of MWTP is defined as the amount of Jakarta road users are willing to pay to reduce their travel time per minute for one trip per month. In other words, if an individual with current travel time of one hour, then the value of IDR40 thousand per month is the money amount that he is willing to pay to reduce his travel time from one hour to 59 minutes in each trip. Hence, IDR40 thousand represents his marginal willingness to pay in reducing travel time. It assumes that the reason for individual to make a trip to Jakarta is for working purposes, with an average of 25 working days each month. Hence, there are 50 times of trip per month per individual. Then, the value of IDR40 thousand is equivalent to 50 minutes of time savings per month or it is equal to IDR800 per minute of time savings 3 . 4.3.2. Interaction Effects 3 This assumption is quiet reasonable considering the survey results showing that more than 95 percent of respondents are going to Jakarta for working. 30 vehicles. Of these four factors, the high growth rate of private vehicles is considered as the main factor in causing the severe traffic congestion in Jakarta. Survey results show that most road users still rely on private vehicles as main transportation modes. The estimation results also justify that road users are willing to pay compensation in term of money for averting traffic congestion. It is estimated that the MWTP per minute of low-income group is IDR18.2 thousand. Also, the MWTP per minute for middle-income group and high-income group are IDR33.8 thousand and IDR49.5 thousand, respectively. Besides, the low quality of connectivity due to lack of public infrastructures also becomes a strong incentive for road users to prefer private transportation modes rather than public transportation. Therefore, improvement in connectivity of transportation modes becomes more important in encouraging people to shift to public transportation. In summary, the aggregation process of the estimated results in section 4 will result in the total benefits of traffic congestion management. Therefore, it needs a set of underlying assumptions. By relying on the results obtained from SITRAMP, it was found that there were about 11,678 million trips per day by road users both from Jakarta and commuter areas such as Bogor-Depok-Tangerang-Bekasi 4 . By taking the estimation results which have been obtained earlier, there is around IDR 9.34 billion of benefits derived from time savings for each minute of all trips in Jakarta. Furthermore, by assuming that the desired time savings is 15 minutes, then the benefits that can be obtained is estimated to be around IDR51.3 trillion. In addition, by also assuming that the number of trips increases by ten percent per year, the number of trips in 2008 would reach 20.87 million trips per day, nearly twice as much as in 2002. Further assumption that is also important to note is income structure. It certainly needs income structure adjustments in the estimation results with the income structure of Jakartans 4 This value is obtained from the estimated calculation of the amount of trips toward Jakarta minus the users of non-motorized transport. 31 to determine the value of total benefits. Based on data Susenas 2006, the average income of Jakartans is IDR2.44 million with average values in the lowest and highest quartiles are IDR917.4 thousand and IDR5.4 million, respectively. According to that interval, the amount of total benefits is equal to an average of IDR43.2 trillion with interval of IDR16.2--95.9 trillion 5 . 5 The minimum and maximum values of each interval are obtained through assuming that the Jakartans‟ willingness to pay is categorized in low-income and high-income only. 32 REFERRENCES Adamowicz, W., J. Louviere, and J. Swait. 1998. Introduction to Attribute-Based Stated Choice Methods. Canada: Advanis. Asri, D. U. and B. Hidayat. 2005. “Current Transportation Issues in Jakarta and Its Impacts on Environment”. Proceedings of the Eastern Asia Society for Transportation Studies, 5, 1792--1798. Best, J. W. and J. V. Khan. 1993. Research in Education, 7 th ed. Needham Heights, Simon & Schuster, Inc. Carson, R. T. 2012. Contingent valuation: A practical alternative when prices aren't available. The Journal of Economic Perspectives, 26(4), 27-42. Christiadi, B. C. 2007. “Conditional Logit, IIA, and Alternatives for Estimating of Interstate Migration”. Presented at The 46 th Annual Meeting of the Southern Regional Science Association, Charleston. Dinas Perhubungan, Pemda DKI Jakarta. 2007. Pola Transportasi Makro Jakarta. Evans, A. W. 1992. “Road Congestion: The Diagramatic Analysis”, in: Button, K. (Ed.), Recent Developments in Transport Economics. Northampton: Elgar Reference Collection. Hanemann, W. M. 1984. “Discrete/Continous Models of Consumer Demand”. Econometrica, 52 (3), 541--561. Harmadi, S. H. B. 2006. “Megapolitan in Economic Perspective”. Paper presented at The Seminar of Megapolitan in Multiperspective Discussion, FISIP University of Indonesia. Hausman, J. 2012. Contingent valuation: from dubious to hopeless. The Journal of Economic Perspectives, 43-56. 35 Figure 1. Externalities and Congestion Tax Source: Sullivan (2006) Figure 2. Share of Transportation Modes Usage Source:: BPS, 2010 Note: Mikrolets are small public passenger cars which is run by private sector to provide transportation services based on different routes 36 Figure 3. Important Attributes in Selecting Transportation Modes Source: based on authors‟ calculation Figure 4. Comparison between Main Transportation Mode and Alternative Transportation Mode Source: based on authors‟ calculation 37 Table 1. Operational Definition of Each Attribute Variables Description Level Travel Time The amount of time spent to travel from origin to destination point Status quo, moderate (decrease by15%), excellent (decrease by 30%) Connectivity Number of transits needed to reach destination point within one trip a) Status Quo (no changes in the number of transits) b) Moderate (transportation mode usage is decreased by 1 mode) c) Excellent (transportation mode usage is decreased by 2 modes) Safety Safety measures in doing trip in Jakarta that consist of traffic safety and property security a) Status Quo (no changes in transportation convenience and 100 deaths of traffic accident per year) b) Moderate (increasing convenience and 50% decrease in traffic accidents per year) c) Excellent (increasing convenience, 50% decrease in traffic accidents per year, 25% increase in the improved roads, and 25% increase in numbers of obedient road users) Transportation Cost The incurred transportation Status quo, increase by 205, and increase by 40% 40 Table 4. Summary of Estimation Results with Main Effects Variables Coefficients Marginal WTP (ten thousands) Constant -1.626 ** (0.113) Travel time 0.036 ** (0.003) 4.00 Moderate Connectivity -0.002 (0.046) Excellent Connectivity 0.167 ** (0.051) 19.3 Safety 0.009 ** (0.002) 1.00 Numeraire (Y-P) 0.009 ** (0.002) Pseudo R 2 Log Likelihood LR Test Number of Observations 0.068 -4139.59 605.39 3663 Source: authors‟ estimation. Note: Values in parantheses are standard deviations. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% respectively. 41 Table 5. Marginal Willingness to Pay for Travel Time Income Groups Joint Estimation Private Transportation Modes Public Transportation Modes Low-income Middle-income High-income 1.821 3.383 4.945 2.621 3.942 5.264 1.645 3.239 4.832 Source: authors‟ estimation 42 Table 6. Summary of Estimation Results with Interaction Effects Variables Joint Estimation Private Transportation Modes Public Transportation Modes Constant (1=new condition, 0=status quo) -1.558 (0.115) -1.689 (0.164) -1.520 (0.166) Travel time (minute) * Middle-income * High-income 0.049 (0.005) -0.007 (0.005) 0.023 ** (0.008) 0.051 (0.006) 0.000 (0.007) 0.017 * (0.010) 0.052 (0.008) 0.011 (0.008) 0.026 * (0.014) Moderate Connectivity (reducing 1 mode) * Middle-income * High-income -0.015 (0.061) 0.071 (0.071) -0.036 (0.105) -0.107 (0.076) 0.088 (0.093) 0.001 (0.126) 0.114 (0.109) 0.035 (0.122) 0.010 (0.201) Excellent Connectivity (reducing 2 modes) * Middle-income * High-income 0.224 *** (0.067) -0.121 (0.067) 0.153 (0.115) 0.397 *** (0.085) -0.198 ** (0.099) 0.135 (0.136) -0.013 (0.122) 0.027 (0.136) 0.009 (0.224)