Motorization in Asia: 14 countries and three metropolitan areas Metin Senbil COE Researcher COE Seminar - 2006.10.20 1 Outline Background Motorization in Asia: 14 countries Kuala Lumpur, Manila, Jabotabek metropolitan areas Car and motorcycle ownerships Econometric model Basic model (all) Extended model (only Jabotabek) Conclusions 2
Background [f]or many, vehicles are desirable as a secure and private means of travel, and as status symbols [b]ut personal motorization also imposes enormous costs, especially in cities. The well known litany includes air and noise pollution, neighborhood fragmentation, and high energy use. Sperling & Claussen (2002) 3 Background Why is motorization in Asia important? Economic development Population pressure Land use and urban development Popular culture & aspirations () 4
Motorization in Asia (1995) Passenger cars per 1000 people Motor cycles per 1000 people Passenger cars per road kilometer Motor cycles per road kilometer OTHER ASIAN CITIES 88.30 117.21 135.71 246.66 AFRICA 102.12 6.92 117.44 12.99 MIDDLE EAST 185.26 23.38 151.06 47.53 LATIN AMERICA 188.53 11.81 176.96 10.14 ASIAN AFFLUENT CITIES 217.33 65.79 110.15 26.52 EASTERN EUROPE 279.23 13.49 230.59 9.39 WESTERN EUROPE 411.86 33.30 175.66 14.81 NORTH AMERICA 567.95 11.90 100.46 2.12 OCEANIA 575.36 13.42 72.57 1.65 5 1980-2000 Asia economics-motorization Passenger cars per thousand population Year 2000 Average annual % change % change from 1980 to 2000 GDP per capitaa Passenger cars per thousand population GDP per capita Passenger cars per thousand population GDP per capita 11 Republic of Korea 172.81 $10,938 18.27 6.37 2546.40 239.58 3 China 6.94 $1,065 13.91 7.79 1209.43 345.61 4 India 6.02 $448 7.08 3.59 290.91 101.80 8 Nepal 1.96 $225 6.87 2.43 269.81 60.71 1 Bangladesh 0.52 $353 8.911 1.93 246.67 46.47 5 Indonesia 14.48 $788 6.43 3.70 240.71 102.05 9 Pakistan 7.47 $514 5.73 2.07 198.80 50.29 14 Thailand 43.38 $1,998 7.674 6.50 197.12 148.51 13 Sri Lanka 16.88 $823 3.89 3.33 112.59 92.29 6 Japan 415.15 $37,361 3.81 2.25 110.83 55.52 10 Philippines 28.46 $1,002 3.933 0.10 104.60 1.31 7 Malaysia 15.22 $3,927 7.43 3.93 63.66 112.50 12 Singapore 103.05 $22,770 2.13 4.86 50.79 155.15 2 Cambodia 0.63 $287 6.322 4.97 36.96 40.00 a. Constant 2000 US$. 1. 1980-1998. 2. 1990-2000. 3. 1981-2000. 4. 1980-1995. 5. 1993-2000. 6
Asia 14000 12000 10000 8000 6000 4000 2000 Number passenger cars (millions) China India Indonesia Malaysia Philippines N. America and Europe 300000 250000 200000 150000 100000 50000 North America and Europe 0 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 0 7 Number of passenger cars per thousand population 35 600 Asia 30 25 20 15 10 5 China India Malaysia Philippines Indonesia N.America and Europe 500 400 300 200 100 North America and Europe 0 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 0 8
Model PC ct = α c GDP θ Fixed elasticity of income: θ Log-transformation is estimated Log(PC ct ) = log(α c ) + ε ct + θ log(gdp) + ε c α c gives the pace of motorization Factors other than income 9 Estimation Coefficient t-score θ 1.75 29.0842 lnα 1 Bangladesh -10.94-31.87 2 Cambodia -10.04-29.37 3 China -10.35-27.29 4 India -8.95-25.55 5 Indonesia -9.14-23.50 6 Japan -12.49-19.94 7 Malaysia -11.76-24.58 8 Nepal -8.99-28.35 9 Pakistan -9.13-24.63 10 Philippines -9.03-21.75 11 Republic of Korea -11.62-21.92 12 Singapore -12.28-21.17 13 Sri Lanka -8.80-22.73 14 Thailand -9.17-21.21 Constant term only -558.49 Log-likelihood (restricted models) Fixed effects only -201.10 Random effects only -340.28 Log-likelihood (the model) Fixed and random effects -2.68 R 2 0.98 10
16 14 12 10 8 6 4 2 0 Japan Singapore Malaysia Republic of Korea α (x10 5 ) Bangladesh China Cambodia Thailand Indonesia Pakistan Philippines Nepal India Sri Lanka 11 Kuala Lumpur, Manila, Jabotabek metropolitan areas 12
Manila MA PHILIPPINES Kuala Lumpur MA MALAYSIA INDONESIA Jabotabek MA 13 Kuala Lumpur 55.53 69.18 Jabotabek 19.15 35.36 Private car Motorcycle Manila 1.45 11.22 0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 14
Metropolitan area Variable Min. Max. Mean Std. Dev. Cars owned 0 3 0.25 0.56 Motorcycles owned 0 3 0.42 0.61 Household income 1 1 9 3.39 1.96 Household size 1 9 3.65 1.54 Jabotabek Average age of household members 5 80 31.81 11.42 Number of male household members 0 7 1.48 0.93 Number of employed household members 0 6 1.24 0.82 # of cases 14,545 Cars owned 0 3 0.87 0.75 Motorcycles owned 0 3 0.62 0.67 Household income 2 1 10 4.52 1.94 Household size 1 20 4.61 1.63 Kuala Lumpur Average age of household members 7 89 28.76 8.86 Number of male household members 0 10 2.32 1.15 Number of employed household members 0 16 2.86 1.54 # of cases 15,654 Cars owned 0 3 0.13 0.42 Motorcycles owned 0 1 0.01 0.12 Household income 3 0 15 3.15 1.57 Household size 1 15 4.16 1.57 Manila Average age of household members 6 85 29.36 9.55 Number of male household members 0 9 1.81 1.07 Number of employed household members 0 7 1.52 0.91 # of cases 15,024 Variables in the basic model 15 cation, system Min. Max. Mean Std. Dev. Center city indicator 1 0.00 1.00 0.63 0.48 DKI Jakarta indicator 0.00 1.00 0.39 0.49 Average land use diversity 2 1.00 2.90 1.54 0.42 Ratio of commercial land use 0.00 0.16 0.03 0.08 Ratio of residential land use 0.00 0.92 0.56 0.28 Ratio of undeveloped land 0.00 0.87 0.32 0.29 Length of major roads passing through the neighborhood 0.00 8.31 1.21 1.49 Length of all roads in the neighborhood 0.06 110.20 25.54 19.90 Distance to DKI Jakarta city center 0.36 70.32 23.83 15.34 Median of total bus lines on street segments 0.00 915.00 67.96 100.58 Ratio of lands within one-kilometers of rail station 0.00 47.18 0.02 0.71 Residential density 3 9.49 571.76 137.63 82.24 Job density 4 2.49 385.84 37.24 40.63 Additional Variables in the extended model Jabotabek - neighborhood variables 16
Let c and m represents the number of cars and the number of motorcycles respectively owned by a household, and let the equation system as follows: y 1h = β x h + ε 1h, y 1h = c if μ 1,c < y 1h μ 1,c+1 (1) y 2h = γ z h + ε 2h, y 2h = m if μ 1,m < y 1h μ 1,m+1 where y and 1h y 2h represent the underlying unobserved responses for household h ownership of c cars and m motorcycles which are observed by variables y 1h and y 2h respectively, β, γ are vectors of parameters, x and z are vectors of independent variables associated with the household, μ is the threshold value that divides a continuous distribution into intervals associated with different levels of ownership. In this equation system, the two error terms are distributed as the bivariate standard normal distribution: φ 2 ( ) = φ 2 (ε 1h, ε 2h, ρ 12 ) (2) where ρ represents correlation between the random error terms. The corresponding cumulative distribution is denoted by Φ 2 ( ) = Φ 2 (ε 1h, ε 2h, ρ 12 ) (3) Using equations (1) and (3), the joint probability of household ownership of c cars and m motorcycles is as follows: P hcm = Φ 2 [(μ 1,c+1 β x h ), (μ 1,m+1 γ z h ), ρ 12 ] Φ 2 [(μ 1,c β x h ), (μ 1,m+1 γ z h ), ρ 12 ] Φ 2 [(μ 1,c+1 β x h ), (μ 1,m γ z h ), ρ 12 ] +Φ 2 [(μ 1,c β x h ), (μ 1,m γ z h ), ρ 12 ] The parameters of the equation system above is estimated by the log-likelihood function, which can be given as: H C M log L = 0Z P hcm hcm (4) 1 0 h= c= m= The econometric model-bivariate probit 17 Jabotabek Kuala Lumpur Manila Variable Motorcycle Car Motorcycle Car Motorcycle Car Coefficient t-score Coefficient t-score Coefficient t-score Coefficient t-score Coefficient t-score Coefficient t-score Constant term -0.65-14.70-2.92-43.70-0.23-5.17-0.43-11.41-2.52-17.51-2.94-37.42 Household income 0.04 6.50 0.56 69.31 0.00 7.75 0.00-5.54 0.08 4.99 0.33 38.08 Household size 0.12 17.22-0.13-12.10-0.02-2.73 0.04 4.62 0.01 0.64-0.02-1.45 Average age of household -0.01-8.31 0.01 4.50-0.01-6.12 0.01 18.44 0.00-0.84 0.01 8.11 members Number of male household 0.00 0.13 0.06 3.74 0.19 17.25 0.00 0.31 0.01 0.24 0.11 6.30 members Number of employed household 0.00-0.19-0.08-4.58 0.04 6.20 0.11 15.69 0.03 0.98-0.01-0.54 members Threshold values μ 1 : one and two 1.24 73.89 1.25 51.44 1.53 100.88 1.41 113.36?? 0.99 38.42 μ 2 : two and three 2.45 48.70 2.69 48.74 2.26 90.15 2.44 113.12?? 1.71 35.24 Correlation ρ -0.07-39.36. -0.22-82.45 0.04 25.64 SAMPLE SIZE 14,545 15,654 15,024 LOG-L(0) -10,469.36-16,006.74-3,630.27 LOG-L(β;γ) -8,707.67-15,623.66-3,144.62 the basic model results 18
Variable Motorcycle Car Coefficient t-score Coefficient t-score Constant term -0.52-3.96-2.99-16.29 Household income 0.04 6.57 0.57 69.26 Household size 0.12 17.30-0.13-11.81 Average household age -0.01-8.26 0.01 4.44 Number of males 0.00-0.07 0.06 3.64 Number of workers 0.00-0.26-0.08-4.68 Neighborhood characteristics Center city indicator 0.01 0.41 0.08 1.78 DKI Jakarta indicator -0.09-2.34-0.01-0.26 Distance to DKI Jakarta city center (meters) -3.93-2.93-3.90-2.09 Average land use diversity 0.00-0.02 0.02 0.37 Ratio of commercial land use 0.04 0.23-0.64-2.32 Ratio of residential land use -0.07-0.78-0.14-1.21 Ratio of undeveloped land -0.05-0.48 0.10 0.69 Residential density 0.00 0.24 0.00 1.47 Job density 0.00-0.90 0.00 0.47 Length of major roads passing through the neighborhood (kilometers) 0.01 1.67-0.01-0.93 Length of all roads in the neighborhood (meters) 0.80 1.31 3.12 3.77 Median of total bus lines on street segments 0.00 0.90 0.00-0.74 Ratio of lands within one-kilometers of rail station 0.03 2.38-9.64 0.00 Threshold values μ 1 : one and two 1.24 73.92 1.26 51.44 μ 2 : two and three 2.45 48.68 2.72 48.59 Correlation ρ -0.08-43.57 SAMPLE SIZE 14,545 LOG-L(0) -10,469.36 LOG-L(β;γ) -8,693.04 Extended model results 19 Conclusions The motorization is in a serious increase in all over the region the introduction of competition by Chinese and Indian car producers in the market it will soon cover the whole Asian region adding to the already galloping motorization trends The linear-regression model estimated for 20 years-14 countries panel data a fixed income elasticity: 1.75 two different sets of countries regarding the effects of factors other than income Sri Lanka, India, Nepal, Philippines, Pakistan, Indonesia, and Thailand with slopes characterizing motorization pace are detected as diverging (significantly higher) from the rest of the countries analyzed Three metropolitan areas (Kuala Lumpur, Jabotabek, Manila) analyzed Motorization has attained highest level in Kuala Lumpur metropolitan area. Manila metropolitan area experiences the lowest motorization. High household car ownership levels in both Kuala Lumpur and Jabotabek though not as much as Kuala Lumpur indicate primacies in their respective countries. The analysis conducted on three metropolitan areas indicate that simultaneous ownership of motorcycle and car is either independent from each other (Jabotabek and Manila) or negatively related (Kuala Lumpur) 20