1 Does Subway Proximity Discourage Automobility? Evidence from Beijing Yingjie Zhang a , Siqi Zheng b, c , Cong Sun d , Rui Wang e 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.
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Does Subway Proximity Discourage Automobility? Evidence from Beijing
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.
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.
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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.
Some scholars, especially spatial planners, highlight land use and built environment’s effects on
motorization, primarily due to the interest in better planning built environment to reduce car
dependence, traffic congestion, and related environmental and health impacts (e.g., climate change,
energy shortage, air pollution, and the lack of physical activity). There have been several reviews
of this literature, such as Crane (2000), Ewing and Cervero (2001), Stead and Marshall (2001),
Handy (2005), Guo and Chen (2007), Mokhtarian and Cao (2008), Ewing and Cervero (2010), and
Cao (2015). Most studies have shown that features of the built environment, such as the “three Ds,”
namely “density,” “diversity” or land use mix, and “design” features related to pedestrian
friendliness of streets and street networks (Cervero and Kolkelman, 1997), are often associated
with travel behaviors including trip frequency, trip distance, mode choice, etc.
However, more and better empirical evidence is needed in order to advance our understanding of
the effects of land use on travel behavior (and/or associated health outcomes) for at least two
reasons. First, while a good number of studies have been conducted on urban land use, passenger
travel and health/environmental effects, the vast majority of existing evidence is based on cross-
sectional data and only confirms the correlations between land use patterns and travel, leaving
causality unexplained or falsely claimed, as in most studies reviewed in the meta-analyses by
Brownson et al. (2009) and Ewing and Cevero (2010). A small number of studies utilize a range
of often sophisticated statistical strategies to address the bias caused by the self-selection of
residential location, including (quasi-)longitudinal design (e.g., Boarnet et al., 2005; Handy et al.,
2005), structural equations (e.g., Cao et al., 2007), joint choice modeling (e.g., Bhat and Guo,
2007), propensity score matching (e.g., Boer et al., 2007; Cao and Fan, 2012), the explicit control
of residential/travel attitudes (e.g., Schwanen and Mokhtarian, 2005; De Vos and Witlox, 2016),
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among others. However, most of the results of the studies attempting to correct the self-selection
bias remain suggestive (Guo and Chen, 2007; Mokhtarian and Cao, 2008) and do not seem to be
very consistent with each other (TRB, 2009; Guo, 2009), although they generally confirm that the
built environment affects travel behavior even after controlling for residential self-selection.
Second, almost all major empirical studies are from industrialized countries, where travel behavior,
health background and the speed of land use change are completely different from those of
developing countries, where air pollution and carbon emissions grow as rapidly as urbanization
and motorization. Data and analyses are very much needed to enrich our knowledge in the
developing country setting for at least two reasons. On one hand, walking, cycling and transit use
are often much more important in the developing countries compared to the highly motorized
countries. On the other hand, significant and rapid socio-economic changes of developing-world
cities provide researchers with significant local built environment variations. Existing studies in
the developing world confirm the associations between land use density and travel mode choice
(Zhang, 2004), between road facility design (street density, connectivity, and location) and physical
activity (Cervero et al., 2009), between built environment characteristics and car ownership and
vehicle-kilometers traveled (Zegras, 2010), and between transit access and car ownership (Huang
et al., 2016). Unfortunately, these studies do not prove causality between the built environment
and travel behavior due to the potential self-selection bias in residential location.
Overall, while the effects of transit access on motorization (e.g., household car ownership and use)
may be studied from either the infrastructure provision perspective or the built environment
perspective, careful causal identification of such effects remain rare in the literature.
3. Research Context and Data
3.1 Housing in Beijing
The plan-to-market transition and the rapid growth of the Chinese economy have left cities with a
complicated housing stock. Most of the owner-occupied housing units belong to one of three types:
commodity housing traded in the free market, reformed (fanggai) housing resulted from the
privatization of work-unit housing under the previous socialist welfare housing regime, and
resettlement (chaiqian) housing typically due to urban renewal and redevelopment. See Chen and
Han (2014) for a detailed survey of the urban housing market in post-reform China.
Under the socialist planning system, urban land was allocated to work units. A work unit typically
used part of its land to construct housing units and allocated them to its employees for a low rent
based on a worker’s office rank, occupational status, working experience, etc. (Fu et al., 2000). As
a result, most of urban workers did not choose their residential locations. Launched in the early
1990s, the housing reform established a housing market on one hand and privatized state-owned
housing units to sitting tenants at very low subsidized prices on the other hand. Such privatized
work-unit housing is the so called reformed (fanggai) housing, resale or lease of which to other
people outside the original work units have in general been prohibited.4 In other words, unlike the
4 The circulation ban of reformed housing has not been relaxed until recently, especially for the units owned by governmental
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commodity housing residents, households living in reformed housing do not have a free choice of
where to live.
Since the establishment of urban land and property markets, many Chinese cities have experienced
urban renewal and redevelopment in their old built-up area to maximize land value and utilization
rate. This process of demolition of properties and relocation of property users is called chaiqian,
which triggers the relocation of existing land users including enterprises and households. In many
cases the government will provide resettlement housing to the relocated households to enable the
process of urban redevelopment or renewal. While the size and condition of the resettlement
housing units are often better than the demolished properties, the location of the resettlement
properties is usually determined through a complicated and ad hoc planning process. Sometimes
resettlement housing is close to the original sites, while sometimes far away. In many cases,
resettlement housing units are mixed with free-market housing development because the
government requires or subsidizes commodity housing development to set aside some units
dedicated to resettlement households. Obviously, residents of the resettlement housing do not have
much control on their residential locations, i.e., they are unable to freely choose where to live.
In summary, due to the special institutional arrangement in Chinese cities’ housing stock, only
residents living in commodity housing had a free choice of residential location through housing
market, while those living in the reformed or resettlement housing did not. This unique context in
urban housing provides us a natural experiment in residential location to test the self-selection in
residential location and to estimate unbiased effects of neighborhood characteristics (e.g., subway
proximity) on household travel behavior.
3.2 Data
Beijing is a largely monocentric city (Zheng and Kahn, 2008), with a contiguous urban core
(central city) dominating most of the key public and private sector activities.5 Tian’anmen square
with its east and west extensions to the Second Ring Road are conventionally regarded as the city
center. Five ring roads (the Second to the Sixth Ring Roads) have been built from the center
outward, as shown in Figure 1. Beijing’ urbanized area is primarily within the Sixth Ring Road
(the largest ring in Figure 1).
*** Insert Figure 1 about here ***
This research uses data from the “Housing, Transportation and Energy Consumption Survey of
Beijing Households” conducted by the Institute of Real Estate Studies at Tsinghua University in
September 2009. This dataset covers all key demographic, income, residential location and travel
information of 826 urban households (in 38 residential complexes) and their household heads.6
employees, which account for more than 30% of the total stock of reformed housing in Beijing. According to the National Bureau
of Statistics of China, reformed housing remains the largest category in owner-occupied housing, indicating a slow conversion from
reformed housing to commodity housing. The cumulative transaction volume of reformed housing is less than 1.5% of its total
stock by the end of 2002 (http://bj.house.sina.com.cn/n/s/2003-09-27/29188.html). 5 Thanks to an anonymous referee’s comments that multiple sub-centers exist within the urban core, we also use multiple job
sub-centers in a robustness check in Section 5.2. 6 Quota sampling method is employed in this survey. The feature matrix of this quota sampling scheme is based on both housing
property type and zone-level population size. There are three types of housing in Beijing, namely reformed housing, commodity
housing and affordable housing, while the resettlement housing units are scattered among them. The respective shares of these
Fig.1 marks the spatial distribution of the 38 complexes and the subway network in Beijing in
2009.
Overall, our sample contains 481 households living in commodity housing units, 204 households
living in reformed housing, and 107 households in resettlement housing, accounting for 58%, 25%
and 13% of the sample,7 respectively. As discussed previously, the resettlement housing units in
our sample are mixed with commodity housing units. Therefore, there are altogether three types
of housing units in two types neighborhoods (complexes) in our sample: commodity housing
complexes (with some resettlement housing units in them) and reformed housing complexes. Our
statistics show that, the average housing age (as of 2009), green space coverage ratio and floor-
area ratio of commodity (reformed) housing complexes are 9 (19) years, 33% (24%) and 3.03
(2.00), respectively. That is, commodity housing complexes are generally newer, taller and have
more green landscape than reformed housing complexes.
The car ownership rate in our sample is 47% by household, with resettlement households having
the lowest car ownership rate (30%), compared to those of households living in reformed housing
(50%) and in commodity housing (52%). 32% of the correspondents drive to work, with an average
one-way commuting time of 33 minutes. 12.6% of the sample residents commute by foot, with an
average one-way commuting time of 11 minutes. 14% commute by bicycle, on average spending
20 minutes one way. 20.6% of the correspondents commute by bus and their average one-way
commuting time is 49 minutes. Only 11.8% take subway to their jobs, and they have the longest
one-way door-to-door commuting time of 57 minutes.
We geo-code the household data and subway lines and stops using the map of Beijing. The straight-
line distance from a household’s residence to its closest subway stop is defined as this household’s
distance to subway. Overall, households living closer to a subway station are less likely to own
private cars. Car ownership rates among households living between 1-1.5 km, 0.5-1 km, and within
0.5 km from the nearest subway station are 56%, 46%, and 38%, respectively.
To explore the heterogeneity in the potential effects of different subway stations, we differentiate
subway stations by measuring the travel time to city center via the subway network. We obtain
typical travel time between each subway station and Tian’anmen Square during morning peak
hours (SUBTIME) using historical travel time data from GAODE Map, China’s version of Google
Map. We then use this SUBTIME variable to proxy the attractiveness of different subway stations.8
All else being equal (e.g., straight-line distance to city center), a larger value of SUBTIME of a
subway station indicates that nearby residents overall benefit less from using subway.
4. Method
three types of housing in Beijing’s then housing stock are 4:5:1. This survey divides Beijing’s urban area into three circles from
inside out (the first circle includes Xuanwu District, Dongcheng District, Xicheng District, and Chongwen District; the second circle
consists of Chaoyang District, Haidian District, Fengtai District, and Shijingshan District; districts in the third circle are Tongzhou
District, Daxing District, Shunyi District, and Changping District). The proportion of surveyed communities in each circle is based
on the total number of residents in the circle. Details of the sampling method are discussed in Zheng and Huo (2010). 7 The remaining 4% respondents reported their housing type as “other” in the survey. 8 Following an anonymous referee comment, we will also check the robustness of results using an alternative SUBTIME measure
defined as the subway travel time between a station and its nearest job sub-center (one of Beijing’s three sub-centers Jinrongjie,
Zhongguancun, and Yayuncun).
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Table 1 presents the variable definitions and summary statistics. CAROWN indicates whether a
household owns a private car. FUEL measures a household’s car usage by the monthly fuel
expenditure reported by car owners.
*** Insert Table 1 about here ***
To show how the car ownership (CAROWN) and use (FUEL) of households relate to their subway
proximity (NEAR_SUB, a dummy variable indicating whether a household is less than 1,000 m
from the nearest subway station), we start with a probit regression of car ownership (Equation 1)
and an ordinary linear squares (OLS) regression of car-owning households’ fuel consumption