Applying contingent valuation method for estimating willingness to pay to control urban flooding in ho chi minh city

UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY THE HAGUE VIETNAM THE NETHERLANDS VIETNAM – NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS APPLYING CONTINGENT VALUATION METHOD FOR ESTIMATING WILLINGNESS TO PAY TO CONTROL URBAN FLOODING IN HO CHI MINH CITY BY NGUYEN DUY CHINH MASTER OF ARTS IN DEVELOPMENT ECONOMICS HO CHI MINH CITY, DECEMBER 2014 UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY THE HAGUE VIETNAM THE NETHERLANDS VIETNAM – NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS APPLYING CONTINGENT VALUATION METHOD FOR ESTIMATING WILLINGNESS TO PAY TO CONTROL URBAN FLOODING IN HO CHI MINH CITY A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By NGUYEN DUY CHINH Academic Supervisor: DR TRUONG DANG THUY HO CHI MINH CITY, DECEMBER 2014 ABSTRACT Despite the implementation of many large scale projects involving in controlling urban flooding in Ho Chi Minh City, the issue of urban flooding has been a longlasting issue for inhabitants in HCMC There are many causes for the problem, both objective and man-made This study conducts a contingent valuation (CV) study to find out the willingness to pay (WTP) for the controlling of urban flooding issue The CV survey was done with the direct survey instrument on 180 households in HCMC Double-bounded dichotomous choice question was also used Nonparametric and parametric estimates for mean WTP are VND 464,654 and VND 380,000 per each household respectively Bootstrapping procedure further solidifies these results LIST OF FIGURES Figure Measure of change in human welfare Figure WTP and WTA in the case of perfect substitutability Figure WTP and WTA in the case of imperfect substitutability .6 Figure Total economic value Figure Monthly mean rainfall in HCMC and mean sea water level at Vung Tau 16 Figure Number of flooding locations in HCMC from 2008 to 2014 18 Figure Open-bid responses from the pilot survey 27 Figure Project 1547 overview 31 Figure Protection for zone I 32 Figure 10 Protection for zone II 33 Figure 11 Distribution of age in the sample 38 Figure 12 Monthly income distribution and House condition 39 Figure 13 Income and ownership of households with ‘no’ response at VND 50,000 .41 Figure 14 Survivor function of non-parametric estimation 42 Figure 15 Monthly income classified by education groups .49 Figure 16 Crystal Ball bootstrapping result for non-parametric WTP 67 LIST OF TABLES Table Total economic value of the project 1547 .10 Table Three types of commonly used elicitation questions 20 Table Details of 18 sets of bid used in the survey 27 Table Distribution of observations across areas 28 Table Main contents of the questionnaire .29 Table Interval distribution of WTP responses 34 Table First bid question response statistics .40 Table Second bid question response statistics 41 Table Variables specification 44 Table 10 Logistic regression result 46 Table 11 Unrestricted model and Restricted model comparison 48 TABLE OF CONTENTS CHAPTER 1: INTRODUCTION CHAPTER 2: LITERATURE REVIEW 2.1 Willingness to pay and willingness to accept 2.2 Total Economic Value 2.3 Utility Theory and the Utility Difference Approach 10 2.4 Contingent Valuation Method 12 2.5 Some relevant CVM case studies 13 CHAPTER 3: METHODOLOGY 15 3.1 Current flooding and flooding control state in HCMC 15 3.2 The procedure of conducting a CVM study 19 3.3 Survey procedure of the study 23 3.4 Main contents of the questionnaire 29 3.5 Description of the hypothetical project 30 3.6 Non-parametric estimation technique 33 3.7 Parametric estimation technique 35 3.8 Confidence intervals of mean WTP 36 CHAPTER 4: ANALYSIS RESULTS 38 4.1 Descriptive statistics 38 4.2 Non-parametric estimation result 42 4.3 Parametric estimation results 43 CHAPTER 5: CONCLUSION 51 5.1 Main findings and policy implication 51 5.2 Limitations of the study 52 REFERENCES 54 APPENDIX 59 CHAPTER 1: INTRODUCTION Ho Chi Minh City (HCMC) is one of the largest city and an important economic center in Viet Nam With the population of 7.8 million people and density of 3,721 people per kilometer square (Wikipedia, 2011), the development of infrastructure cannot catch up with the rapid urbanization rate of HCMC The inconsistency in the urban development in HCMC caused various problems, one of which is the exceedance in capacity of the urban sewer and drainage system, one of the causes for major urban flooding (Hoc, 2008) Moreover, the instability of the weather also further aggravates the flooding issue through the heavy rainfall and high rainfall level In the effort to address the flooding circumstance, the Government has approved two major plans The first one is the plan of urban drainage improvement and sewer development of HCMC, which was approved by Decision 752 in 2002 The second is the MARD1 construction plan, which was approved by Decision 1547 in 2008 Both projects have the same goal to help alleviate the flooding in HCMC, but they solve in different approaches While the former focuses on rehabilitating and developing the inner urban drainage system, the latter seeks to build large scale hydraulic constructs around the city However, due to the diversity in causes of flooding in HCMC, in order to completely solve the problem, the combination of both projects is required Research problem and objectives In summary, to deal with the urban flooding issue, not to mention other smaller projects, there are two main projects have been approved and in the process of implementing, which are urban drainage improvement and sewer development of HCMC (project 752) and hydraulic construction plan (project 1547) However, project 1547 is facing difficulties in appealing investment and cost underestimating, which may delay the project until 2025 Although a specific number of expected MARD stands for Ministry of Agriculture and Rural Development outcomes of the two projects were not given, CBA of these projects expected to relieve all the tangible damage caused by flood in 50 years, which means no heavy flooding will be occurring in the next 50 years (Steering Centre for Urban Flood Control Program, 2013) Are all the aforementioned measures effective or not? Inspection of the all the projects upon completion will be required to answer the question in the future Although a CBA analysis for project 1547 has already been conducted However, the estimating of the benefit of the project in the CBA is based on the ‘flood risk approach’ which did not consider the demand and willingness to pay (WTP) of the inhabitants in HCMC In addition, the results of the study may serve as a guide for making decisions involving potential urban flooding project implementation and fee collection in HCMC as well as in other areas Furthermore, potential projects involving flood control in HCMC may also require WTP as an important indicator to estimate the benefit of the project Therefore, this study intents to:  Evaluate inhabitants’ level of awareness about flood risks in HCMC  Determine the aggregate WTP for a hypothetical anti-flooding project  Find out the factors governing the WTP of HCMC inhabitants In order to answer these questions, Contingent Valuation Method (CVM) will be applied in conjunction with the direct interview survey instrument to measure the willingness to pay for the elimination of urban flooding in HCMC The result in the study may be used in policy making for deciding whether or not a project would be feasible to be conducted in the future It may be also helpful for the process of inspecting the effectiveness of the project 1547 and 752 CHAPTER 2: LITERATURE REVIEW Benefits and costs of a public good or an environmental good are difficult to determine since public goods or environmental goods usually not have a price Moreover, the costs and benefits are mostly dependent on individuals’ preferences Given a public good is provided, the individual’s benefit can be measured by measuring how much that individual is willing to give up to obtain that public good On the contrary, the individual’s cost when a public good is lost is measured by the value of something else that individual would accept to compensate for the loss In terms of welfare, money is usually used as a standard measure In that case, the measure of benefit is the willingness to pay (WTP) to obtain the benefit or willingness to accept (WTA) to compensate for the lost The measure of cost is WTP to avoid the cost and WTA to tolerate the same Many techniques have been invented with the purpose of estimating the WTP and WTA for the provision or decrease in public goods Bateman et al (2002) classified techniques into revealed preference techniques (RP) and stated preference techniques (SP) Contingent Valuation Method (CVM) and Choice Modelling (CM) are commonly used SP techniques by researchers to estimate WTP and WTA While CM involves in inferring a value from the change in attribute levels of a public good, CVM directly collects individuals’ stated WTP by eliciting a scenario for respondents This chapter will present some basic concepts of economic valuation, which are WTP, WTA, Total Economic Value (TEV), Utility Theory, the Contingent Valuation Method and several CVM empirical applications 2.1 Willingness to pay and willingness to accept Pearce (1997) defined willingness to pay is the monetary valuation that was placed by an individual for a good or service WTP is constrained by ability to pay so that any people with higher income will value goods or services more highly than that with lower income Field (1997) and Pearce (1992) also pointed out that willingness to accept is not constrained by the individual’s income, as is willingness to pay Thus, when people are asked willingness to accept questions, their answers are usually higher than their willingness to pay for the same item According to Bateman et al (2002), the concept of WTP and WTA can be graphically illustrated using indifferent curve The vertical axis represents the expenditure in money unit of an individual on the private good (y) The horizontal axis represents the quantity of a public good (x) The indifferent curve I and I’ represent two linked combinations which have the same level of utility of two distinct individuals, with I has a lower level of utility than I’ Figure Measure of change in human welfare Source: Bateman et al., 2002 If there is a change in the quantity of a public good, the value of the change can be measured using four measures, two of which are WTP and WTA First, suppose there is an increase in the quantity of public goods from x0 to x1 and the individual’s initial consumption point is at A With the increase in the quantity of public good, the individual can enjoy x1 of the public good, but the private consumption is reduced by BC, in other words, the new consumption point is now C The amount BC is defined as the WTP for the increase in the public good (or the compensating variation for the increase in the public good) Second, if the initial consumption of the individual is at B and there is a decrease in quantity of public goods from x1 to x0, but the individual enjoys a higher level of private consumption, y1, moving the consumption point from B to D The amount DA is the amount of private consumption needs to be compensated for the loss in the public good, specifically in this case, the WTA for the reduction in the public good is DA (or the compensating variation for the reduction in the public good) The third and the fourth measure are equivalent variation for the increase in the public good (equivalent gain) and equivalent variation for the decrease in public good (equivalent loss) Theoretically, they are equal to WTA and WTP, respectively, in value, but are distinct concepts elicited by different types of questions Equivalent variations are obtained by asking ‘how much would be good/bad as gaining/losing public good X’ instead of ‘how much would pay/compensate for the gain/loss public good X’ In theory, WTP and WTA have to be equal or only some insignificant divergence is allowed, but major differences between WTP and WTA have been illustrated by many studies Brown (2000) pointed out the difference between WTP and WTA still exists if the following conditions are not met: (i) no income effect; (ii) no transaction cost; (iii) perfect information about goods and prices; (iv) a market bring back a set of truthful references In practice, the gap between WTP and WTA always exists because it is unrealistic to meet all the conditions in a single study case Nas (1996), Hanley (1997), and Hanemann (1984) stated that the divergence between WTP and WTA originates from the substitution between private and public good, so the equality between WTP and WTA is only achieved in the case of perfect substituted Landry, C., & List, J (2007) Using ex ante approaches to obtain credible signals for value in contingent markets: Evidence from the field American Journal of Agricultural Economics, 89(2), 420-429 Loomis, J (2011) What's to know about hypothetical bias in stated preference valuation studies? Journal of Economic Surveys(25), 363-370 Malhotra, N K (1995) Marketing Research: An Applied Orientation New Jersey: Prentice-Hall International Mitani, Y., & Flores, N (2010) Hypothetical bias reconsidered: payment and provision uncertainties in a threshold provision mechanism Mitchell, R C., & Carson, R T (1989) Using Surveys to Value Public Goods The Contingent Valuation Method Morrison, M D., Blamey, R K., & Bennett, J W (2000) Minimising Payment Vehicle Bias in Contingent Valuation Studies Environmental and Resource Economics, 407-422 Nas, T F (1996) Cost-Benefit Analysis: Theory and Application California: Sage Publication, Inc National Oceanic and Atmospheric Administration (1994) Natural resource damage assessment: Proposed rules Federal Register Pearce, D (1997) Benefit-cost analysis, environment, and health in the developed and developing world Environment and Development Economics, 195-221 Phi, H L (2009) Local Climate Change and Urban Inundation in Ho Chi Minh City 11th Conference on Science and Technology - Department of Civil Engineering - HCMCU of Science and Technology, (pp 155-165) Shabman, L., & Stephenson, K (1996) Searching for the Correct Benefit Estimate: Empirical Evidence for an Alternative Perspective Land Economics, 433449 Steering Centre for Urban Flood Control Program (2013) Ho Chi Minh Flood and Inudation Project Final Report Thunberg, E M (1988) Willingness to Pay for Property and Nonproperty Flood Hazard Reduction Benefits: An Experiment Truong, T V (2010, November 7) A look back at inundation solutions in HCMC Retrieved November 14, 2014, from Vietnam National Commitee on Large Dam & Water Resources Development: http://www.vncold.vn/Web/Content.aspx?distid=2477 Wikipedia (2011) Ho Chi Minh City entry Retrieved November 14, 2014, from Wikipedia: http://vi.wikipedia.org/wiki/Th%C3%A0nh_ph%E1%BB%91_H %E1%BB %93_Ch%C3%AD_Minh Zhai, G., Sato, T., Fukuzono, T., Ikeda, S., & Yoshida, K (2007) Willingness to Pay for Flood Risk Reduction and Its Determinants in Japan Journal of the American Water Resources Association, 927-940 APPENDIX APPENDIX 1: The questionnaire Question Year of birth Question Gender Question Number of family members Question Monthly income of household Question How many members in your household is currently working? Question How long have your family been in HCMC? Question Education level ⃝ High School ⃝ Vocational training ⃝ College ⃝ University ⃝ Post-graduate Question House condition: ⃝ Semi-concrete house ⃝ Concrete house ⃝ Temporary house ⃝ Villa Total square (m2) No of floors Question House ownership ⃝ Rent house ⃝ Private house ⃝ State-provided house Question 10 Distance to the nearest canal or river: ⃝ Under 100m ⃝ From 100m to 500m ⃝ From 500m to 1km ⃝ From 1km to 2km ⃝ Higher than km Question 11 House address Question 12 Have your family been affected by urban flooding before? Question 13 In your opinion, would the urban flooding controlling be improved in the future? Question 14 Do you concern about the current urban flooding consequences? Yes No ⃝ ⃝ ⃝ ⃝ ⃝ ⃝ Question 15 On average, how many times have your house been affected with urban flooding? Question 16 In your opinion, what natural disaster you concern the most? (From to scale) Hurricane ① ② ③ ④ Flooding ① ② ③ ④ Earthquake ① ② ③ ④ Thunderstorm ① ② ③ ④ Question 17 In your opinion, what environmental issue you concern the most? (From to scale) Air pollution ① ② ③ ④ Water pollution ① ② ③ ④ Noise pollution ① ② ③ ④ Soil erosion ① ② ③ ④ Greenhouse effect ① ② ③ ④ Extinction of rare species ① ② ③ ④ Illegal logging ① ② ③ ④ Natural resources exhaustion ① ② ③ ④ Question 18 In your opinion, what urban issue you concern the most? (From to scale) Robbery ① ② ③ ④ Traffic accident ① ② ③ ④ Fire hazard ① ② ③ ④ Urban flooding ① ② ③ ④ Traffic congestion ① ② ③ ④ Question 19 Is your family currently using any private anti-flooding measures? ⃝ No measures taken ⃝ Floor elevation HCMC has been facing with the urban flooding for a long time, especially in the rain season from May to November Moreover, in October, the situation will be further aggravated by the river tide There are three main causes for urban flooding in HCMC: flooding from upstream, high tide and heavy rain Moreover, the rapid urbanization also pushes canals, rivers and urban drainage system into overload condition According to Steering Centre for Urban flooding, In 2013 and 2014, it is expected to have 11 impaired street routes in the city suburban areas remains and there will be no flooding in the city center Most significant flooding points are:  Around Phu Lam Circus (District 6) in the city western gateway Specifically, Hau Giang Street, Hung Vuong Street, Kinh Duong Vuong Street, Minh Phung Street  In Binh Tan District, which has the fast urbanization and unorganized city planning, leads to the exceedance in drainage system  Some other areas such as Thanh Da (Binh Thanh district), Kha Van Can Street (Thu Duc district) is also affected heavily with flooding With the flooding situation I would like to propose a project which will help alleviating the flooding problem in HCMC This project is actually approved and implemented in 2008 by the Government but it has not been completed yet First, the HCMC will be divided into zones (see figure)  Zone I (yellow zone): contains all of the left-hand side of Saigon river plus Nha Be District (central HCMC districts and a part of Long An Province)  Zone II (red zone): contains the area around Saigon river – Dong Nai river tri- fork (District 2, and Thu Duc District)  Zone III (blue zone): contains Can Gio district only The project focuses on protecting zone I and zone II, zone III is consider to be an important ecological and vulnerable area So no measures is implemented in zone III in the project Second, the protection for zone I consists of 172 km of dykes and 12 sluice gates, protecting urban and rural areas of HCMC, more specific, zone I, against tidal flooding and river discharges It also serves the role of controlling the water level in the protected area by enhancing the outer drainage system Furthermore, the inner urban drainage will also be enhanced by rehabilitate 186 urban drainage routes Third, the protection for zone II focuses on the raising of the embankment of lowlands and building smaller dykes to enclose ecological and tourism areas Canals and main rivers will also be dredged to improve drainage capacity Upon completion, this project is expected to eliminate completely the urban flooding in HCMC Do you have any questions regarding this project? Question 20 Assuming if there is a call for contribution to the implementation of this project Each household in HCMC will be requested to contribute an amount of as voluntary donation Will you accept to contribute? ⃝Accept ⃝Accept If the amount is now , will you accept? → ⃝Reject ⃝Accept ⃝Reject If the amount is now , will you accept? → ⃝ Reject Questions regarding belief Yes No Question 21 Do you think this project can effectively reduce the urban flooding problem? ⃝ ⃝ Question 22 Do you think this project may contaminate the surroundings in the implementation process? ⃝ ⃝ Question 23 Do you believe the Government will make a good use of your contribution? ⃝ ⃝ APPENDIX 2: Non-parametric WTP calculation and bootstrap At the beginning, the number of households will be distributed into intervals First, TSCA will calculate the probability of having WTP greater than boundary values by dividing the number of households who have WTP higher than boundary values by the total sample size (180) the results are presented in column in the table below Second, the probability for having WTP range from an interval’s lower bound and higher bound is then calculated for basic intervals by subtracting aforementioned figures For overlapping intervals, the probability is calculated by adding corresponding basic interval probability Detailed calculation is presented in column in the table below 20,000 Higher bound 20,000 50,000 Number of HHs in interval 13 Probability WTP > lower bound 180/180 = 1.00 122/180 = 0.67 Probability lower b < WTP < higher b 1.00 – 0.67 = 0.33 0.67 – 0.60 = 0.07 C 50,000 250,000 11 109/180 = 0.60 0.60 – 0.35 = 0.25 D 250,000 450,000 10 63/180 = 0.35 0.35 – 0.19 = 0.16 E 450,000 1,000,000 12 35/180 = 0.19 0.19 – 0.06 = 0.13 F 1,000,000 3,000,000 11/180 = 0.06 0.06 – 0.02 = 0.04 G 3,000,000 5,000,000 4/180 = 0.02 0.02 – 0.00 = 0.02 H 5,000,000 ∞ 0/180 = 0.00 0.00 – 0.00 = 0.00 CD CDE 50,000 50,000 450,000 1,000,000 11 0.25+0.16 0.25+0.16+0.13 CDEF 50,000 3,000,000 15 0.25+0.16+0.13+0.04 DE 250,000 1,000,000 0.16+0.13 DEF 250,000 3,000,000 10 0.16+0.13+0.04 DEFGH 250,000 ∞ 0.16+0.13+0.04+0.02+0.00 EF 450,000 3,000,000 0.13+0.04 EFGH 450,000 ∞ 0.13+0.04+0.02+0.00 FGH 1,000,000 ∞ 0.04+0.02+0.00 GH 3,000,000 ∞ 0.02+0.00 AB 50,000 17 0.33+0.07 ABC 250,000 14 0.33+0.07+0.25 ABCD 450,000 0.33+0.07+0.25+0.16 ABCDE 1,000,000 0.33+0.07+0.25+0.16+0.13 Interval code A B Lower bound Third, the number of households in each basic interval will be recalculated by adding the initial number of households with the probability-weighted number of households in overlapping intervals The result is shown in the column at the table below Probability WTP > lower bound (Recalculated) 1.00 0.80 A B 20,000 20,000 50,000 Number of HHs in interval (Recalculated) 36.52 19.17 C 50,000 250,000 39.14 0.69 D 250,000 450,000 32.74 0.47 E 450,000 1,000,000 36.02 0.29 F 1,000,000 3,000,000 10.81 0.09 G 3,000,000 5,000,000 5.61 0.03 H 5,000,000 ∞ 0.00 0.00 Mean WTP 484,654 Interval code Lower bound Higher bound Lastly, the probability of having WTP higher than boundary values will be recalculated with the new numbers of households The Mean WTP will now be the sum product of newly calculated probabilities with the boundary values (lower bounds) To calculate the confidence interval, the bootstrapping procedure is applied More specifically, using Crystal Ball software, multiple randomly generated data sets will be used to calculate different WTPs The result for the confidence interval of this mean WTP is shown below Figure 16 Crystal Ball bootstrapping result for non-parametric WTP (For even more detailed calculations and bootstrapping instruction, see the Excel file) APPENDIX 3: Unrestricted model STATA results and bootstrap Logistic regression Log likelihood = -120.92096 Number of obs LR chi2(47) Prob > chi2 Pseudo R2 = = = = 360 230.21 0.0000 0.4877 choice | Coef Std Err z P>|z| [95% Conf Interval] + -bid_inmil | -3.768358 5834935 -6.46 0.000 -4.911984 -2.624732 age | -.0245201 0173724 -1.41 0.158 -.0585694 0095292 no_of_dependent | 4663285 256356 1.82 0.069 -.03612 9687769 gender_male | 0989271 3949432 0.25 0.802 -.6751474 8730015 income_inmil | 4199499 0774706 5.42 0.000 2681103 5717894 no_floors | -.1430356 302022 -0.47 0.636 -.7349878 4489166 khavancan | 3610017 6463254 0.56 0.576 -.9057729 1.627776 anduongvuong | -3.505577 900945 -3.89 0.000 -5.271397 -1.739757 kinhduongvuong | -1.877452 7343452 -2.56 0.011 -3.316743 -.4381624 nguyenhuucanh | -.440789 7830333 -0.56 0.573 -1.975506 1.093928 flood_affect | 6096581 1.27954 0.48 0.634 -1.898194 3.11751 flood_improve | 1.058125 4463937 2.37 0.018 1832095 1.933041 flood_consequences | -1.314471 5455949 -2.41 0.016 -2.383817 -.2451247 belief_project | 3818523 4500268 0.85 0.396 -.500184 1.263889 belief_pollution | 2956996 6009737 0.49 0.623 -.8821871 1.473586 belief_government | -.9069781 5409955 -1.68 0.094 -1.96731 1533535 res_period_under5 | 1.033324 5738275 1.80 0.072 -.0913568 2.158006 res_period_under10 | -.1490424 5650193 -0.26 0.792 -1.25646 958375 edu_highschool | -.9260158 9460629 -0.98 0.328 -2.780265 9282334 edu_vocational | -.6251548 1.081205 -0.58 0.563 -2.744277 1.493968 edu_college | -2.394677 9987266 -2.40 0.016 -4.352146 -.4372093 edu_university | -1.17833 92941 -1.27 0.205 -2.99994 6432799 house_semi_concrete | -.0185201 7538696 -0.02 0.980 -1.496077 1.459037 ownership_rent | -.2690481 6011 -0.45 0.654 -1.447182 9090862 distance_100m_500m | -1.148421 8553328 -1.34 0.179 -2.824842 5280004 distance_1km_2km | 1.417869 1.153721 1.23 0.219 -.8433825 3.679121 distance_500m_1km | 8956122 8600779 1.04 0.298 -.7901096 2.581334 flood_times_under5 | -.060428 4941573 -0.12 0.903 -1.028958 9081024 flood_times_above10 | -.1774402 4657231 -0.38 0.703 -1.090241 7353603 self_anti_flooding | -.6536278 3.444295 -0.19 0.849 -7.404321 6.097066 concern_hurricane | 1111465 4235304 0.26 0.793 -.7189577 9412508 concern_flood | -.0124173 4803627 -0.03 0.979 -.953911 9290764 concern_earthquake | 622374 7676667 0.81 0.418 -.8822251 2.126973 concern_thunderstorm | 6659196 4434418 1.50 0.133 -.2032102 1.535049 concern_air_pollution | -.2308682 5029651 -0.46 0.646 -1.216662 7549253 concern_water_pollution | -.4482941 3846488 -1.17 0.244 -1.202192 3056037 concern_noise_pollution | 3958451 4837591 0.82 0.413 -.5523053 1.343996 concern_soil_erosion | 1.463726 5531172 2.65 0.008 3796367 2.547816 concern_greenhouse | 1436706 4564686 0.31 0.753 -.7509913 1.038333 concern_extinction | 901053 5287251 1.70 0.088 -.1352292 1.937335 concern_logging | 3970874 6177997 0.64 0.520 -.8137777 1.607952 concern_res_exhaustion | 7723069 5094352 1.52 0.130 -.2261678 1.770782 concern_robbery | 7268556 4757019 1.53 0.127 -.205503 1.659214 concern_traff_accident | 2819275 4858891 0.58 0.562 -.6703976 1.234253 concern_fire | -.35244 4204741 -0.84 0.402 -1.176554 4716741 concern_urflooding | 8218355 7144666 1.15 0.250 -.5784932 2.222164 concern_congest | 4266145 4105365 1.04 0.299 -.3780223 1.231251 _cons | -2.538461 2.228521 -1.14 0.255 -6.906281 1.829359 - Krinsky and Robb (95 %) Confidence Interval for WTP measures (Nb of reps: 5000) + -+ | MEASURE | WTP | LB | UB | ASL* | CI/MEAN | | + + + + + | | MEAN/MEDIAN | 0.40 | 0.29 | 0.50 | 0.0000 | 0.51 | + -+ *: Achieved Significance Level for testing H0: WTP0 LB: Lower bound; UB: Upper bound Measures of Fit for logit of choice Log-Lik Intercept Only: D(312): McFadden's R2: ML (Cox-Snell) R2: McKelvey & Zavoina's R2: Variance of y*: Count R2: AIC: BIC: BIC used by Stata: -236.024 241.842 0.488 0.472 0.844 21.103 0.861 0.938 -1594.623 524.375 Log-Lik Full Model: LR(47): Prob > LR: McFadden's Adj R2: Cragg-Uhler(Nagelkerke) R2: Efron's R2: Variance of error: Adj Count R2: AIC*n: BIC': AIC used by Stata: -120.921 230.207 0.000 0.284 0.647 0.549 3.290 0.618 337.842 46.440 337.842 VIF result Variable | VIF 1/VIF -+ -flood_affect | 27.33 0.036584 distance_5~m | 20.00 0.050003 concern_ur~g | 14.30 0.069915 no_floors | 11.35 0.088109 edu_highsc~l | 11.33 0.088288 age | 11.30 0.088490 flood_cons~s | 9.15 0.109271 edu_univer~y | 8.19 0.122100 income_inmil | 7.75 0.129002 edu_college | 7.19 0.139023 distanc~500m | 5.59 0.179011 concern_tr~t | 5.47 0.182869 no_of_depe~t | 4.72 0.211899 anduongvuong | 4.44 0.225211 res_perio~10 | 4.42 0.226474 concern_ro~y | 4.24 0.235582 res_period~5 | 4.15 0.241168 khavancan | 3.99 0.250370 edu_vocati~l | 3.95 0.253188 concern_co~t | 3.62 0.276038 distanc~_2km | 3.28 0.304685 gender_male | 3.26 0.306687 kinhduongv~g | 3.18 0.314825 concern_fl~d | 3.13 0.319346 concern_th~m | 2.84 0.352726 concern_fire | 2.75 0.363180 concern_re~n | 2.63 0.379520 house_semi~e | 2.63 0.379818 nguyenhuuc~h | 2.57 0.388595 flood_tim~10 | 2.57 0.389299 belief_pro~t | 2.56 0.391133 belief_gov~t | 2.50 0.400751 flood_impr~e | 2.48 0.402447 flood_tim~r5 | 2.48 0.402512 concern_wa~n | 2.42 0.412616 Variable | VIF 1/VIF -+ -bid_inmil | 2.41 0.414638 concern_ai~n | 2.39 0.417829 concern_hu~e | 2.33 0.429058 concern_no~n | 2.19 0.456214 ownership_~t | 2.05 0.487842 concern_gr~e | 1.92 0.519813 concern_so~n | 1.89 0.528191 concern_lo~g | 1.72 0.581078 belief_pol~n | 1.68 0.594123 self_anti_~g | 1.65 0.604361 concern_ex~n | 1.62 0.618032 concern_ea~e | 1.47 0.679724 -+ -Mean VIF | 5.05 APPENDIX 4: Restricted model STATA results, bootstrap and LR contrast test Logistic regression Number of obs LR chi2(8) Prob > chi2 Pseudo R2 Log likelihood = -142.38521 = = = = 360 187.28 0.0000 0.3967 -choice | Coef Std Err z P>|z| [95% Conf Interval] -+ -bid_inmil | -3.057467 4594746 -6.65 0.000 -3.958021 -2.156913 age | -.0292945 0135986 -2.15 0.031 -.0559473 -.0026417 distance_100m_500m | -.4002655 6649611 -0.60 0.547 -1.703565 9030343 distance_1km_2km | 6371651 8084036 0.79 0.431 -.9472769 2.221607 distance_500m_1km | 7792256 5998251 1.30 0.194 -.3964099 1.954861 kinhduongvuong | -1.273006 4446715 -2.86 0.004 -2.144546 -.4014654 income_inmil | 3471769 0441237 7.87 0.000 260696 4336579 anduongvuong | -2.124322 5150738 -4.12 0.000 -3.133848 -1.114796 _cons | -.7082265 7698011 -0.92 0.358 -2.217009 800556 -Krinsky and Robb (95 %) Confidence Interval for WTP measures (Nb of reps: 5000) + -+ | MEASURE | WTP | LB | UB | ASL* | CI/MEAN | | + + + + + | | MEAN/MEDIAN | 0.38 | 0.27 | 0.48 | 0.0000 | 0.56 | + -+ *: Achieved Significance Level for testing H0: WTP0 LB: Lower bound; UB: Upper bound Measures of Fit for logit of choice Log-Lik Intercept Only: -236.024 D(351): 284.770 McFadden's R2: ML (Cox-Snell) R2: McKelvey & Zavoina's R2: Variance of y*: Count R2: AIC: BIC: BIC used by Stata: 0.397 0.406 0.782 15.115 0.833 0.841 -1781.252 337.745 Log-Lik Full Model: LR(8): Prob > LR: McFadden's Adj R2: Cragg-Uhler(Nagelkerke) R2: Efron's R2: Variance of error: Adj Count R2: AIC*n: BIC': AIC used by Stata: -142.385 187.278 0.000 0.359 0.555 0.471 3.290 0.542 302.770 -140.189 302.770 VIF results Variable | VIF 1/VIF -+ -age | 7.31 0.136749 distance_5~m | 5.44 0.183855 income_inmil | 3.25 0.308110 anduongvuong | 2.13 0.468563 distanc~500m | 2.06 0.484694 bid_inmil | 2.01 0.497408 kinhduongv~g | 1.67 0.598646 distanc~_2km | 1.29 0.777012 -+ -Mean VIF | 3.15 LR contrast test Likelihood-ratio test (Assumption: restricted nested in unrestricted) LR chi2(39) = Prob > chi2 = 42.93 0.3065 APPENDIX 5: Related materials, data and calculation files Data and related materials can be downloaded at one of the following links: http://goo.gl/4AwHtC http://goo.gl/ivQoJb http://goo.gl/2eY10M http://goo.gl/14AM4Q ... METHOD FOR ESTIMATING WILLINGNESS TO PAY TO CONTROL URBAN FLOODING IN HO CHI MINH CITY A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT... CHINH Academic Supervisor: DR TRUONG DANG THUY HO CHI MINH CITY, DECEMBER 2014 ABSTRACT Despite the implementation of many large scale projects involving in controlling urban flooding in Ho Chi. .. Theory, the Contingent Valuation Method and several CVM empirical applications 2.1 Willingness to pay and willingness to accept Pearce (1997) defined willingness to pay is the monetary valuation