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Does Subway Proximity Discourage Automobility? Evidence from Beijing

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Does Subway Proximity Discourage Automobility? Evidence from Beijing Does Subway Proximity Discourage Automobility? Evidence from Beijing Yingjie Zhanga, Siqi Zhengb, c, Cong Sund, Rui Wange a. School of Economics and Management, Beijing Forestry University, Beijing, 100083, China b. Hang Lung Center for Real Estate, Tsinghua University, Beijing, 100084, China c. Department of Urban Studies and Planning, Center for Real Estate, and the STL Real Estate Entrepreneurship Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, United States d. School of Urban and Regional Science, Shanghai University of Finance and Economics, Shanghai, 200433, China e. Johns Hopkins University, School of Advanced International Studies & UCLA Luskin School of Public Affairs, 1619 Massachusetts Avenue, NW, Washington, DC 20036, United States Abstract Many cities around the world are investing in rail transit, but whether it can effectively reduce road congestion and air pollution from automobiles remains an open question. A major challenge to empirically answering this question is the fact that the choices of residential location and travel mode are jointly made by households. The unique context of urban housing in Beijing provides us a natural experiment to separate residential location and travel choices of households living in the resettlement and reformed housing units. We take advantage of the largely exogenous residential locations of those living in the resettlement and reformed housing in Beijing and use the Heckman two-step method to correct a potential bias in estimating vehicle fuel consumption. To identify the heterogeneous effects of different subway stations, we use the travel time to city center by subway to proxy a subway station’s value to users. We find robust evidence supporting that subway proximity reduces a household’s probability of owning a car and subsequent fuel consumption. More valuable subway stations discourage nearby households’ car ownership rate by a greater extent. Evidence does suggest the existence of residential self-selection. Keywords Subway proximity; car ownership; fuel consumption; resettlement housing; reformed housing; Beijing. 1 1. Introduction Many cities around the world are investing in urban rail, but whether it can effectively reduce road congestion and mobile air pollution remains an open question. A major challenge to empirically answering this question is the fact that the choices of residential location and travel mode are often jointly made by households, known as the self-selection of residential location by travelers – household residential location choice is affected by travel needs and preferences (see, e.g., Guo and Chen, 2007; Mokhtarian and Cao, 2008; Brownson et al., 2009; TRB, 2009; Ewing and Cevero, 2010). Without exogenous variations in the residential locations of households, one could not cleanly identify the effects of rail transit on nearby households’ car ownership and travel behavior. Taking advantage of the unique urban housing policies in China and the rapid expansion of urban rail transit in Beijing, this study uses an empirical strategy (i.e., identifying a subsample of urban households with exogenous residential locations) different from earlier studies to provide a robust estimation of urban rail transit’s effects on automobility. Decades of rapid economic growth and urbanization have dramatically changed China’s urban transportation, making urban residents travel longer distances and more frequently and rely more on modes using fossil fuels (Wang, 2010). Rapid motorization has led to a series of problems including serious road congestion, severe air pollution, and rapidly rising demand for oil and emissions of greenhouse gases. Beijing, China’s capital and one of its most motorized cities, experienced an average annual increase rate of 11.8% in the number of motor vehicles from 2000 to 2010.1 Despite the government’s new policy of setting an annual quota on new car license plates since 2011, the total number of motor vehicles in Beijing exceeded five million by 2012.2 To combat road congestion and air pollution brought by rapid motorization, Beijing has been heavily investing in public transit systems. By 2020, Beijing will have 30 urban rail lines in operation, with 1,050 km in total route length and 450 subway stations.3 It is not a surprise that the new urban rail lines will be filled with passengers, especially as the majority of Beijing’s residents do not own cars currently. However, it is unclear how the development of urban rail will affect automobility (i.e., car ownership and usage) of residents. Will the car owners reduce driving? Will additional rail service slow down the rise in car ownership? The reason these questions are difficult to answer is that, on one hand, rail transit provides a competing alternative to driving, but on the other hand, the fact that rail transit may reduce surface road congestion (e.g., fewer buses are needed on the same route) can induce more driving from those who can afford to drive. Thus we need empirically test urban rail transit development’s effect on automobility. But this is not a straightforward task. One may observe that residents who live near a subway station have a lower car-ownership rate, but we can’t confirm whether it is due to that those who prefer subway to driving self-select to live nearby a subway station, or that 1 Data obtained from the Beijing Traffic Management Bureau. See http://www.bjjtgl.gov.cn/jgj/ywsj/index.html. 2 Ibid. 3 Data obtained from the Beijing Municipal Government. See http://zhengwu.beijing.gov.cn/gzdt/zyhy/t1114930.htm. 2 improved access to subway does change residents’ car ownership and use behavior. This study uses a 2009 household survey in Beijing to examine the impact of subway station proximity on urban residents’ car ownership and fuel consumption. To address the potential bias due to residence self-selection, we take advantage of the unique urban housing situation in China as an opportunity of natural experiment and focus on households living in the resettlement (chaiqian) housing and the reformed (fanggai) housing with pre-determined locations for causal inference. We also explore the heterogeneous effects of subway stations due to their different travel times to the city center via the subway network. Moreover, we employ the Heckman two-step model to test and correct the potential sample selection bias when estimating rail transit’s effect on fuel consumption using data from the car owners. Our findings show that subway proximity does reduce an urban household’s probability of owning a car as well as the mileage driven, even after controlling for the residential self-selection bias. The effect of subway on car ownership is stronger where subway provides a shorter time of travel to the city center. Overall, the development of urban rail in Beijing likely reduces overall car use as some would-be car owners choose not to own a car and car owners drive less, producing positive traffic and environmental impacts. Section 2 briefly reviews the literature. Section 3 describes the background of resettlement and reformed housing in Beijing and our household survey data, as well as the measurement of the heterogeneous locations of subway stations. Sections 4 discusses the analytical method and hypotheses. Sections 5 and 6 present empirical findings and related robustness check results, followed by conclusions in Section 7. 2. Literature Multiple factors, especially income, fuel price, and road infrastructure, influence private passenger motor vehicle ownership and travel behavior in cities. Income has been considered as a major determinant of motorization. Many studies, mostly from the industrialized world, have estimated the income elasticities of motor vehicle ownership and use. They indicate that motorization increases rapidly with income, although the elasticities vary. Ingram and Liu (1999) summarize studies since the mid-1960s and find that long-run income elasticities of car ownership (typically based on cross-sectional data, e.g., Silberston, 1970; Wheaton, 1982; Kain, 1983) are greater than 1.0, while short-run elasticities (typically based on time series or panel data, e.g., Pindyck, 1979; Button et al., 1993; Johansson and Schipper, 1997) are less than 1.0; income elasticities from urban-level data (e.g., Beesley and Kain, 1964; Chin and Smith, 1997) are similar to or smaller than those from country-level data largely due to the existence of competing modes of transportation; income elasticities of motor vehicle use (e.g., Pindyck, 1979; Wheaton, 1982; Mannering and Winston, 1985; Train, 1986; Hensher et al., 1990; Button et al., 1993; Johansson and Schipper, 1997) are less than unity, indicating that motor vehicle use increases less rapidly than ownership. 3 Many have also studied the effects of fuel price on motorization (e.g., Pindyck, 1979; Wheaton, 1982; Train, 1986; Hensher et al., 1990; Johansson and Schipper, 1997). Compared to the somewhat weak evidence on fuel price’s effect on vehicle ownership, studies generally confirm that increase in fuel price negatively affect vehicle usage and positively affect the average fuel efficiency of the vehicle stock, although evidence suggests that income elasticities are greater than price elasticities in magnitude for both motor vehicle ownership and use (Ingram and Liu, 1999). Road infrastructure at the national and city levels, usually provided publicly, is widely recognized as closely related to motorization. However, due to the endogenous relationship between infrastructure investment decisions made by governments and the growth in regional travel demand, there has been limited robust evidence on how motorization is influenced by road provision. Using a source of more plausible exogenous variations (the 1947 interstate highway plan, 1898 rail routes and the major exploration routes during 1835-1850), Duranton and Turner (2011) analyze the impact of interstate highway provision on city-level traffic in the continental US between 1983 and 2003. They suggest the elasticity of metropolitan area interstate highway vehicle-kilometer travelled with respect to lane kilometers to be 1.03 – a near proportional increase in metropolitan traffic to the extension in interstate highways.
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