Analysis of Air Pollution Exposure of Individuals in - Clean Air Initiative

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Table 5. Profile of Respondents Variables Description Mean St. Dev. CHOICE Dependent Variable; 1=Yes, 0=No 0.375 0.485 INITBID Initial bid; P0.5, P1, P1.50, P2, P2.50, P3, P3.50 1.610 0.663 GEN Gender; 1=Male, 0=Female 0.510 0.501 STAT Status; 1=Single, 0=Married 0.550 0.499 AGE Age; continuous data 28.425 10.696 CAROWN Car ownership; 1=with car, 0=without car 0.158 0.366 EDUC Educ. Attainment; 1=Elem., 2=Sec.,3=Vocational, 4=College, 5=Post College 2.900 0.962 HINC Household Income; 1=
xz = represents the different combinations yy, yn, ny, and nn, SP = stated preference/ answer of the respondents The median willingness-to-pay may be computed from changing V(1, Y-C)-V(0,Y) in the logit formula (2) to ∆V(C), in order to reflect the variations in cost. Equation 2 may be transformed to the formula below in deriving the median willingness-to-pay. C* = exp (α / β) (4) where C* = median cost of the responses α and β = parameter estimates The median was used as the measure of central tendency representing the data because the bids were already preset, meaning it is not affected by extreme values in the response distribution. The mean is the conventional measure in benefit-cost analysis and from a statistical point of view, more sensitive than the median, however, the median may be more realistic in a world where decisions are based on voting and there is a concern for the distribution of benefits and costs (Hanneman, 1999). The results of the maximum likelihood estimation are shown below. Table 6. Summary of Regression Survey 1 Survey 2 HHoldInc1 HHoldInc2 HHoldInc3 Parameter α β α β α β α β α β Estimates 0.36 1.66 0.19 1.45 -0.47 1.26 -0.02 1.89 0.26 1.21 Standard Error 0.24 0.26 0.16 0.16 0.33 0.32 0.22 0.27 0.21 0.18 t-statistic 1.45 6.45 1.21 9.28 -1.43 3.91 -0.10 7.05 1.25 6.64 No. of Samples Log of Likelihood = C* = Note: 61 139 40 77 80 -327.335 -860.839 -194.763 -433.145 -456.245 1.24 1.14 0.69 0.99 1.24 HHoldInc1 = household income below 10,000 pesos/month HHoldInc2 = household income 10,001-20,000 pesos/month HHoldInc3 = household income greater than 20,000 pesos/month Survey 1 was done simultaneously with the SPM and travel time survey. The respondents were inside the jeepney while in-transit. Survey 2, on the other hand, was conducted on passengers waiting for a jeepney along Philcoa. HHoldInc1, HHoldInc2 and HHoldInc3 stands for the aggregated respondents from survey 1 and 2, segmented according to the household income of the respondents. The log of likelihood of the model is indicative of its significance and a higher probability of the dependent variable to occur in the sample. The log of likelihood is also dependent on the robustness of the data; the higher the value means the larger the sample size. The low tstatistics of α indicates that it is not that relevant at the 95% significance level. Using the parameter estimates derived from the maximum likelihood estimation, the probability that the respondents will pay for the increase in fare was computed. Figure 13 shows a comparison of the two surveys, with the first survey having a slightly higher probability curve than the second survey. Although the disparity between the two is not big, it can be inferred that the respondents of the first survey, done inside the jeepney and in transit, obtained a higher median value for their willingness-to-pay and probability curve. Their direct exposure to higher SPM levels during the interview could affect the results.
Page 1 and 2: ANALYSIS OF AIR POLLUTION EXPOSURE
Page 3 and 4: these pollutants inside vehicles is
Page 5 and 6: Table 1. Start and End Time of the
Page 7 and 8: Speed (kph) Speed (kph) 35 30 25 20
Page 9 and 10: ug/Ncm 1000 900 800 700 600 500 400
Page 11: elevance of this regression analysi
Page 15 and 16: Given the current levels of SPM con

Analysis of Air Pollution Exposure of Individuals in - Clean Air Initiative

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