Department of Policy and Planning Sciences
Discussion Paper Series

No.1344The Impact of a High-speed Railway on Residential
Land Prices
by
Koichi USHIJIMA and HIROSHI KANASUGIMar 2016

UNIVERSITY OF TSUKUBA
Tsukuba, Ibaraki 305-8573
JAPANThe Impact of a High-speed Railway
on Residential Land Prices
Hiroshi KANASUGIa
Koichi USHIJIMAb,a

The University of Tokyo, 4-6-1 Ce-509 Komaba, Meguro-ku, Tokyo 153-8505, Japanb

The University of Tsukuba, 1-1-1 Ten-nodai, Tsukuba-shi, Ibaraki 305-8573, Japan

March 22, 2016

A bstract
By using the case of the high-speed railway scheduled to open in 2027 in Japan, namely
the Linear Chuo Shinkansen train, this study examines whether the value of transportinnovation is capitalized in land prices immediately after the construction plan isannounced. We adopt a hedonic approach to measure value, using balanced panel data on
residential land prices from 2008 to 2015 in Japan. This study aims to solve estimation
problems in the impact evaluation of large-scale transport infrastructure construction
projects. We find that residential land prices in the area where the time distance to theTokyo metropolitan area reduces rose, except in the area where the population isdecreasing. This result implies that the benefits are capitalized in land prices when there
is demand to shorten the time distance. The estimation results also suggest that thebenefits of transport innovation are capitalized in asset prices immediately after the
infrastructure construction decision. In addition, we confirm the importance of examining
whether ³a natural experiment´ is an experimental situation.

JEL classification: R4
Keywords: High-speed railway, Residential land price, Inverse probability weighting,Japan
Corresponding author. E-mail address: ushijima@sk.tsukuba.ac.jp1

I.

Introduction
High-speed railways benefit society by greatly reducing the time distance between

cities and by transporting more people than alternative public transportation modes suchas airplane and long-distance bus. The development of high-speed railways promotes
industry accumulation in urban areas, develops tourism in rural areas, and, in some cases,changes the residential distribution of people. However, high-speed railways are
expensive to build owing to the enormous sunk costs such as the cost of track andexpenses for land acquisition. Moreover, the basic plans of high-speed rail construction
are often canceled because of budget limitations. Thus, policymakers must be clear thatthe construction justifies the investment.
This study investigates whether the benefit of high-speed railways is capitalized inresidential land prices. We focus on the case of the Linear Chuo Shinkansen train (LCS
hereafter) in Japan, which construction of was announced in 2011 and which is scheduled
to open in 2027. The aims of this study are to solve estimation problems in the impact
evaluation of large-scale transport infrastructure construction projects. 1 While manystudies have evaluated the impact of transportation infrastructure development, many
suffer from three estimation problems, namely omitted variable bias, location selection
bias, and the timing of the treatment. Although there are estimation strategies to addresseach problem, it is necessary to use a high quality dataset or natural experiment setting to
identify the impact. Only a few studies have tackled all three problems when examining
the relationship between transport infrastructure and land prices.

1

Since it is difficult to measure all the benefits of the LCS that have not yet been realized, this study
does not estimate its cost-effectiveness.
2

The definition of the treatment groups is also a major problem when evaluating theimpact of large-scale transportation infrastructure. The purpose of high-speed railway
construction is to shorten the time distance. Since the effects of reducing the time distancespread throughout the transportation network, it is necessary to define the range oftreatment effects carefully.
This study attempts to resolve the above-mentioned estimation and definition
problems by using balanced panel data on residential land prices from 2008 to 2015 andinformation on the time distance to large cities in Japan. The estimation results2 showthat residential land prices in the area where the time distance to the Tokyo metropolitan
area reduces rose, except in the area where the population is decreasing. This result
implies that the benefits are capitalized in land prices when there is demand to shorten
the time distance. The estimation results also suggest that the benefits of transportinnovation are capitalized in asset prices immediately after the infrastructure construction
decision. In addition, we confirm the importance of examining whether ³a naturalexperiment´ is an experimental situation.
The remainder of the paper is organized as follows. Section 2 presents the
background of the impact evaluation of transport infrastructure and of the LCS. Section
3 provides an overview of the dataset. Section 4 describes the estimation strategy. Section
5 reports the estimation results. Section 6 concludes.

I I. BackgroundA . Problems of transport infrastructure evaluations

2

The estimation results report the value including the indirect effects of transport innovation such as
expectations of urban development (see Bowes and Ihlanfeldt, 2001).
3

Many researchers have evaluated the impact of constructing large-scale infrastructure
on economic growth and assessed the cost-effectiveness of projects given that mostconstruction costs are sunk cost. However, studies typically suffer from three estimation
problems, while the definition of the treatment group is also a challenge in the case oflarge-scale transportation infrastructure projects.
The first problem in empirical studies is omitted variable bias. Under hedonic
approaches, goods are regarded as being composed of a number of attributes and the priceof goods is considered to be a bundle of the potential economic value of each attribute. In
the case of land prices, land has myriads of unobservable attributes at any time that caneven be correlated with each other. Further, the estimators in the hedonic approach areoften biased because unobservable attributes are correlated with the treatment variables.
For example, it is assumed that land prices around a railway station are higher and thusthat the area around a railway station is more developed. However, land prices are affectedby both the benefits of the railway links and the benefits associated with the convenienceof the developed area. Identifying both benefits is often challenging because it is
impossible to observe all the elements of the convenience of the developed area.
Recent research that has examined the relationship between land prices and distancefrom the train station has considered omitted variable bias. Many studies adopt the fixed
effects model to control for the time-invariant and common factors in the neighborhood(e.g. Baum-Snow et al., 2005; Debrezion et al., 2007). The present study also controls fortime-invariant factors by adopting an individual fixed effects model and by usingbalanced individual panel data.
Location selection bias is the second major problem with the impact evaluations oftransport infrastructure. One purpose of transport infrastructure construction is to4

decrease transportation costs for local residents. Hence, transportation infrastructure isusually built in areas where it will be used by more people. Therefore, the observed impactof the infrastructure project includes the effects due to location selection (e.g., future
development). Then, changing the outcomes includes the impact of the infrastructure
construction and the effect of regional development.
There are two ways in which to overcome location selection bias. The first is by using
an experimental approach to exploit a situation that the location choice is treated as-if
random. Since the allocation of the treatment is as-if random, the attributes between thetreated and untreated groups can be regarded as the same. For example, Billings (2011)
measures the value of the light rail constructed in 2000 in Charlotte, North Carolina byadopting a natural experiment. The author defines the relevant control groups (i.e., twolines in the proposed construction plan). If the three areas where construction is planned
are similar and one is chosen by chance, we can assume that the attributes of these control
areas are similar to those of the construction area.
Several studies that have evaluated the impact of a large transportation infrastructure
project have also exploited the natural experimental setting. Banerjee et al. (2012)
examine the relationship between access to transportation infrastructure and economic
growth in China, exploiting the fact that transportation networks tend to connect historicalcities linearly. Although such historical cities that house a terminal could be affected bylocation selection bias, areas in the geographical middle of these historical cities do not
suffer from location selection bias because this is a geographically natural experiment.
Therefore, location selection bias can be avoided by evaluating middle areas. In a similar
fashion, Datta (2012) investigates the effects of highway improvements on firms in India.
As the examined highway was built to connect four metropolitan cities, it can be5considered that the intermediate areas through which the highway runs are not selected.3
The second approach to addressing location selection bias is by conducting apropensity score matching with difference-in-difference (DID) estimation (called amatching DID, or MDID herein). Propensity score matching aims to control forunobservable attributes to exploit the observable variables. DID measures the averagetreatment effect by defining a treatment group and a control group, which is similar to thetreatment group but is not treated. DID takes the difference of the difference between the
before and after treatment on the outcome of each group. If both groups are similar, thedifference in outcomes before and after the treatment in both groups is considered to be
due to the treatment. Propensity score matching can thus define a control group that
resembles the treatment group to alleviate the location selection problem.
In this vein, Gibbons and Machin (2005) investigate whether the reduction intransportation cost by transportation innovation raises land prices in London. They use
MDID to consider the location selection bias of transportation innovation as a robustness
check. Xu and Nakajima (2015) study the relationship between accessibility to highways
and industrial development in China. They use MDID to mitigate the location selection
bias of highway construction.
The treatment effect of the intermediate stations of the LCS can be regarded asleading to small location selection bias in the same way as in Banerjee et al. (2012) andDatta (2012). However, as discussed below, the attributes of the treatment and control
groups do not necessarily balance even if location selection bias is small. Therefore, inall estimations, this study alleviates selection bias by adopting MDID.

3

In addition, Michaels (2008) investigates the effect of highway construction by exploiting thenatural experimental setting of interstate highways in the United States.
6

The third problem is the timing of the treatment. When measuring rising property
values, it is necessary to consider the timing of the construction decision. Although thetransportation services as a flow are provided after opening, the asset price as a stock rises
when the service is reliably expected to be operating in the future. Obscuring this timing
will underestimate the treatment effect. Several studies have considered this problem(McDonald and Osuji, 1995; McMillen and McDonald, 2004; Billings, 2011). McDonald
and Osuji (1995) examine whether railway benefits are capitalized immediately after theconstruction information is published. The present study assumes that the time of thedecision of the construction plan is at the beginning of the treatment similar to McDonald
and Osuji (1995). The validity of this assumption is described in Section IIB.
In addition to these three estimation problems, the location of the treated group must
be solved. The LCS connects -DSDQ¶V major cities at the ZRUOG¶VKLJKHVW VSHHG, which
creates a large time-shortening effect. This effect is spread widely through the traffic
network. If the observational point is added to the control group candidate, although it
should be defined as a treatment group, the DID estimator is underestimated. If anexcessively broad area is defined as the treatment group, the treatment effect is also
underestimated. Hence, to find the observation points where the time distance tometropolitan areas is reduced by using the LCS, we compare the time distance to theseareas by using the LCS with that by not using this service.

B. H istory of the L CS
As noted above, the estimation includes two points; first, the direct benefit of the
LCS is a time-shortening effect to large cities such as Tokyo and Nagoya; second, thedecision to proceed with the construction plan is the start of the treatment. In this section,7

we explain the background to the construction of LCS including the opposing opinion on
the cost burden of construction, Shinkansen construction announcement, andgeographical factors.
The LCS, which is operated by JR Tokai in Japan, is expected start running services
between Tokyo and Nagoya in Aichi prefecture in 2027, with services expanded to Osakafrom Nagoya station by 2045.4 Currently, the Tokaido Shinkansen (TS) operated by JR
Tokai connects Tokyo station and Osaka station. The TS is the oldest, most famous, andmost densely scheduled high-speed railway in Japan. In 2013, 155 million people used
the TS. Although the main stations of both Shinkansens (Tokyo, Shinagawa and Nagoya,
Osaka) are located at the same station, the intermediate stations of the LCS are built
elsewhere (Figure 1).

Insert F igure 1

The construction of the LCS is designed to reduce the time distance between theTokyo metropolitan area and the Osaka metropolitan area from two and a half hours tojust one hour. The LCS is the highest speed railway powered by a linear motor at present
in the world.5 The capacity of each train is about 1000 people, which is approximately
twice the size of the largest domestic airplane. Therefore, the reduced time cost of the
LCS will create a huge consumer surplus. The other purpose of the construction of the
LCS is to provide an alternative route to the TS. The TS, which was opened in 1964, is

4The length of the line between Tokyo station and Nagoya station is 286 km and that between
Tokyo station and Osaka station is 438 km.
5 In 2015, the LCS achieved 603 km per hour in a manned test run, WKHZRUOG¶VKLJKHVWVSHHGforland transportation (see http://jr-central.co.jp/news/release/_pdf/000026466.pdf, published in April
21, 2015, in Japanese, last access February 26, 2016).
8

scheduled a large-scale renovation because and has a shut off risk due to a large
earthquake called a Tokai earthquake occurring.6 Since travelling between Tokyo andOsaka has huge demand, the economic loss of the TS not running is large. Hence, the
LCS is a socially desirable alternative mode.
In this regard, however, the construction cost of the LCS is high. The estimatedconstruction cost is about nine trillion yen (¥9×1012), including the cost of rolling stockand excluding interest. In addition, the total construction cost of the four intermediate
stations is about 330 billion yen (¥330×109). Usually in Japan, the government accepts
two-thirds of the construction cost of high-speed railways and the local government
accepts the rest. However, surprisingly, JR Tokai accepted all the construction costs of
the LCS, as the discussion on the cost allocation for each government was prolonged.7
Then, JR Tokai decided a phased construction plan, constructing the line between Tokyo
and Nagoya in the first stage and that between Nagoya and Osaka in the second stage.
To reduce the construction costs of the intermediate stations, the renovation ofexisting stations and adjustment of connection facilities were postponed until thecompletion of the line between Tokyo and Osaka in 2045. Furthermore, intermediate
stations will have no ticket office and no sales staff in order to reduce operating costs.8
That is, JR Tokai will build the intermediate stations without considering a transportation
network in neighboring regions. A park-and-ride system will be necessary to use the LCS
in intermediate stations because access to a nearby railway station will be inconvenient.

6

In 2011, the Ministry of Education, Culture, Sports, Science and Technology announced that theprobability that an eight-magnitude Tokai earthquake would occur in the next 30 years is 87%.
7 Although JR Tokai is a large company whose total assets were 5.2 trillion yen in 2014, accepting
the full amount of construction costs alone is still a challenge (see http://jrcentral.co.jp/news/release/_pdf/000013337.pdf, published in November 21, 2011, in Japanese, lastaccess February 26, 2016).
8 Trains will adopt a pre-reservation system for all seats.
9Next, we describe when the treatment effects of the LCS started. Although the LCS
was initially conceived in 1973, it was not until May 2011 when the Ministry of Land,
Infrastructure and Transport agreed the construction plan.9 In August 2011, JR Tokai
published the location of the intermediate stations and the rationale for selection in anenvironmental impact statement at the planning stage,10 only between Shinagawa stationin Tokyo and Nagoya station in Aichi prefecture. It is to be noted that, in Japan, theconstruction of Shinkansen is not determined. even though basic plan is announced.
Shinkansen listed in the basic plan published in 1973 have actually been built only sevenof the 17. Further, the date of completion is unknown at the time listed in the basic plan.
In other words, the construction of the LCS was determined after the information
disclosures in 2011.11
Focusing on geographical factors allows us to verify the validity of the naturalexperiment. The route of the LCS is almost a straight line between Tokyo and Nagoya
and is expected to take 40 minutes. The minimum radius of the curvature of this route is8000 m compared with that of the TS of 2500 m, which is a major restriction on the
maximum speed. Because of this problem, the minimum radius of the curvature of the
Shinkansens built after TS in Japan is 4000 m.

I I I. Data
This study uses official land prices, called Kojichika, from 2008 to 2015 to9

See ³7KHGHWHUPLQDWLRQRIWKHFRQVWUXFWLRQSODQRI&KXR6KLQDNDQVHQ´
http://www.mlit.go.jp/common/000145486.pdf (published in May 26, 2011, in Japanese, last accesslast access February 26, 2016).
10 See ³(QYLURQPHQWDOLPSDFWVWDWHPHQWDWWKHSODQQLQJVWDJHRI&KXR6KLQNDQVHQEHWZHHQ7RN\R
WR1DJR\D´http://company.jr-central.co.jp/company/others/assessment/_pdf/04.pdf (published inAugust, 2011., in Japanese, last access last access February 26, 2016)
11 Of course, the market may have reacted for some reason before 2011. For information on what todo, this problem is described instead of the identification strategy.
10

investigate whether the benefits of the LCS are capitalized in residential land prices after
2011. The Land Prices Public Announcement Act (公示地価法) investigates official land
prices in Japan. Although official land prices are not actual transaction prices, they are avalue evaluated by experts by using actual transaction information taken from the Land
Transactions Survey (土地取引状況調査). Therefore, official land prices report a survey
price that reflects changes in the market. Further, official land prices have a panel data
structure since they are reported on January 1 every year.
These panel data are superior to the panel datasets used by previous studies. First,
they are individual panel data. In an analysis of land or asset prices, a researcher typicallymakes a pooled cross-section data and estimates values by using area fixed effects.
However, our panel data can control for the unobservable time-invariant omitted variables
by using individual fixed effects. Second, the measurement error is smaller than that inother datasets since experts have evaluated official land prices in order to reduce theinformation asymmetry in land transactions. For such a reason, information that is nottraded is hard to contain.
Observations are selected as places that can build a house under the land use
regulations. However, observations are excluded for four prefectures, namely Fukushima,
Toyama, Ishikawa, and Okinawa (Figure 2). First, we exclude Fukushima prefecture as it
was affected by the Fukushima nuclear accident caused by the Great East Japan
Earthquake in 2011. However, because radioactive substances were scattered outside
Fukushima, the treatment group belonging to Shinagawa station will include affected
points. The distance between Shinagawa station and the Fukushima nuclear power plantis 230 km. As a robustness check, we estimate the average treatment effect on treated(ATT) of the redefined treatment group by distance from Shinagawa station. Second, we
11exclude Toyama prefecture and Ishikawa prefecture to remove the effect of the Hokuriku
Shinkansen that opened in 2015. The Hokuriku Shinkansen connects Tokyo station andNagano station (Figure 1). It also extends from Nagano station to Toyama prefecture andIshikawa prefecture. The construction plans of the Hokuriku Shinkansen were publishedin 2000.12 The opening of the Hokuriku Shinkansen in 2015 would have increased land
prices in Toyama and Ishikawa. Therefore, we exclude those areas from the analysis.
Finally, Okinawa is excluded from the analysis. Other small islands also are excluded.
The treatment group is defined as the shortening of the time distance to Tokyo station
and Nagoya station. However, five LCS trains an hour travel between Shinagawa station
and Nagoya station, four of which do not stop at the intermediate stations. Therefore, we
assume that the reduction in the time distance to the intermediate stations is not a benefit.
To find the observation point that shortened the time distance to the major city stations,this study uses spatial information about the railway, information about the travel time ofall public transportation infrastructures, and spatial information on all road infrastructures.
Evaluating the effects of local railway is straightforward by defining the treatmentgroups as the distance from the nearest station. However, defining the treatment groups
as the distance from the station is unsuitable for evaluating the effects of high-speed
railways. We must clarify how the benefits of high-speed railways spread. If we estimate
the range of treatment effects too narrowly, the control group candidates include treatedobservations. This study overcomes this problem of the definition of the treatment groupby using a detailed timetable. The Appendix provides details on how the treatment group
was defined.
The treatment effect of the LCS can be classified into two types: (i) those that reduce

12

The basic plan of the Hokuriku Shinkansen was published in 1973.
12the time distance to Tokyo and Nagoya (the intermediate stations correspond to this type)and (ii) those that reduce the time distance to Tokyo or Nagoya. The treatment group forwhich the nearest station is Shinagawa station in Tokyo aims to shorten the time distanceto Nagoya station. Conversely, the treatment group for which the nearest station is Nagoya
station aims to shorten the time distance to Tokyo station. Focusing on the second typeallows us to compare the benefits of the shortening of the time distance to Tokyo station
and Nagoya station.
This study adopts a propensity score estimation to control for selection bias.
Propensity scores are estimated by using information on the year immediately before thereceiving treatment, which is 2011. To estimate a propensity score, information on official
land prices in 2011 is combined with the Population Census 2010 and Economic Census
for Business Frame 2006. 13 Theses Censuses report an aggregate value for eachmunicipality. By using propensity score matching and the attributes of the neighboring
environment, we select a control group that has a land market condition similar to that of
the treatment group. In addition, estimating the propensity score uses land price history
from 2008 to 2011 in order to select a control group that considers the situation that amarket reacts before the treatment such as an Ashenfelter dip (Heckman and Smith, 1999)
and the effect of adventitious urban development before the treatment.
Table 1 reports the descriptive statistics. In columns (1) to (6) are the statistics of thetreatment group for the LCS stations. Column (7) shows the statistics of the control group
candidates. This table shows that the average land price from 2012 to 2015 is lower thanthat from 2008 to 2011 in Japan. The rate of change of the average land price ofShinagawa station in Tokyo is -10.0%. The land price of the treatment group by

13

Both Censuses are reported every five years.
13intermediate stations in Kanagawa prefecture, Yamanashi prefecture, Nagano prefecture,
and Gifu prefecture falls by -6.6, -11.6, -13.1, and -12.2 percentage points, respectively.
Moreover, the land price of the treatment group of Nagoya station in Aichi prefecture
falls by -8.5 percentage points and that of the control group falls by -9.1 percentage points.
According to a simple comparison of these statistics, the declines in the average land
prices of the treatment group of the station in Kanagawa prefecture and Nagoya station
are smaller than that of the control group.
Table 1 also shows that the opening of the LCS reduces the time distance to Tokyo
station of the treatment group except Shinagawa station, and vice versa. There is no clear
difference between the control group and treatment groups for distance to the nearest
station, acreage, building coverage, and floor area ratio, while the values of population,
population trend, and office number and number of employees in the treatment groupbelonging to the station in Tokyo, Kanagawa, and Nagoya are higher than those in thecontrol group. On the contrary, the population density of the treatment groups of thestations in Yamanashi, Nagano, and Gifu is lower than that of the other groups. Propensity
score matching thus mitigates the differences in the covariates between the treatmentgroup and control group.

Insert Talbe 1

I V . E mpirical Strategy
This study measures the change in residential land prices before and after the openingof the LCS by adopting a hedonic model. When the time distance to a large city reduces
because of transport innovation, the land attributes also change when the market is14

exposed to such information. For example, land prices will rise because of the discounted
present value of the benefit of the transport innovation.

A . Discount rate
In the analysis, we estimate the discounted present value of the LCS. This describes
the simplest case of the relationship between the estimation result and the discount rate.
We assume that the LCS will open j years after the construction information disclosure.
As a result, the change in land prices because of the transport innovation is

G LP

LPj  LP0 , where LPj is the land price in the j-th year which is immediately after

the opening and and LP0 is the land price in period 0 which is immediately before theannouncement. We also assume that discount rate d is constant over time. Thus, thechange in land prices in the first year immediately after the announcement is expressed
as LP1  LP0

G LP 1  dj 1. The interest of this study is the average change rate ofland prices from just before the announcement to four years after the announcement.
In our estimation, it is difficult to remove the influence of a specific urban
development that is determined after the announcement. The influence of such a specificdevelopment may be included in the change in land prices. For example, the decision tostage the Tokyo Olympic Games in 2020 was agreed in September 2013. Our analysis
cannot sufficiently remove the influence of infrastructure construction for the Olympic
Games on land prices. For this reason, the results must be interpreted carefully.

B. Baseline model
The baseline model measuring the reduction in the time distance to Tokyo and15

Nagoya because of the LCS isln LPitD  E Treat it  X cit J  YFt  F Ei  H it ,(1)

where LPit is the official land price at survey point i in year t,

Treat it is a dummy

indicating whether the survey point belongs to a treatment group in the postannouncement period, X cit represents the control variables of i in t, T Ft is the year
fixed effects, and F E i is the individual fixed effects. That is,estimator and thus the coefficient E of variable

Treat it is a DID

Treat it represents the averageincreasing land price. In other words, E is the discounted present value, which is basedon future benefit, immediately after the information disclosure to the land market. YFt
controls for the factors of each year such as the inflation rate. In this study, equation (1)is called a fixed effects DID. For previous impact evaluations of railways, Gibbons andMachin (2005) and Billings (2011) adopt fixed effects DID models by exploiting thequasi-experimental situation.

C . Identification
As described in Section 2, three estimation problems exist when evaluating
infrastructure construction in the hedonic model: omitted variable bias, location selection
bias, and the timing of the treatment. To control for the time-invariant omitted variables,some studies construct (quasi-) panel data and adopt a fixed effects model (e.g. Gibbons
and Machin, 2005). The present study also constructs a balanced panel dataset and adopts
individual fixed effects. That is, the first problem is solved substantially in the estimationof equation (1).
16

Location selection bias is the second problem from which all impact evaluation
studies of infrastructure construction suffer. On the one hand, since a railway is built tomaximize revenue, the location of the railway station is less likely to be considered to bea natural experiment.14 The effect of the construction of a terminal LCS station should
also be affected by location selection bias. This study mitigates the location selection biasof terminal stations by using propensity score matching.
On the other hand, since the purpose of the LCS is to shorten the time distance
between metropolitan areas, the route is straight. Hence, it can be regarded as a naturalexperiment with respect to the location of intermediate stations. However, the selection
bias problem for the treatment effects of such an intermediate station must be noted. In
general, if the control group covariates differ from the treatment group covariates, theyear trends of the outcome in each group would be different. As a result, the DID
estimator is biased. Therefore, balancing the covariates is essential in the DID estimation.
However, solving this balancing problem is not easy for the impact evaluation of largescale transportation infrastructure. The hedonic model obtains an estimate by regressing
the price on the characteristics of interest of each good. Hence, the model assumes thatdemand-side preferences are homogeneous. However, as shown in Table 1, the range
affected by the large-scale transportation infrastructure construction might extend beyond
a radius of 50 km. In this case, defining a control group for which demand-side
preferences can be regarded as equal to those of the treatment group is difficult caused by
the spatial sample selection. To address the spatial sample selection problem, control
groups for the intermediate stations are also selected by using propensity score matching.

14

In the case of local railways, the endogeneity of location selection is more serious sinceengineering constraints such curvature radius and social problems (e.g., the trade-off between train
speed and noise) are weaker than for high-speed railways.
17

The second problems are formulated on the basis of equation (1) as follows:
ln LPitD  Treat jt E  X cit J  YFt  F E i  K jt  H it ,(2)

where K jt is the unobservable time-variant factors of group j in t that correlate with

Treat jt . Since the DID estimation assumes that the unobservable time-variant factor in
the control and treatment groups is a common trend, the time-variant factors can becontrolled for by using year fixed effects, YFt . However, the K jt of the control andtreatment groups is unlikely to be a common trend because the treatment group in terminal
stations is biased by location selection and that in intermediate stations is biased by spatialsample selection.
Therefore, we use propensity score matching to define the control group with asimilar K jt to the treatment group. This procedure can compare the treated land market
to the untreated land market that is similar to the treated market. If both land markets were
not treated, land prices would show the same trend. That is, the assumption of a common
trend is met in the DID estimation by using propensity score matching.
This study adopts the inverse probability weighting (IPW) technique to eliminate theimbalance between the control group and treatment group covariates using propensity
scores as weights. IPW weights PÖi 1  PÖi , where PÖi is the propensity score, for thecontrol group to estimate the ATT. As apparent from the equation of weight, this is
unsuitable for the estimation when the propensity score is too small or too large. Therefore,
observations that have a propensity score of less than 0.1 or more than 0.9 are dropped
from the analytical sample.

18

V . Estimation Results and Discussion
A . AT T for the whole treatment group
Table 2 reports the DID estimations using the whole treatment group. Column (1)shows the result estimated by using OLS. Column (2) shows the result estimated by (1)with individual fixed effects. Column (3) shows the result estimated by (2) with regulated
samples that have a propensity score of more than 0.1 and less than 0.9. Column (4) shows
the result estimated by (3) with IPW. Since we are using balanced panel data over eight
years, the number of observations is eight times the number of individuals.
Although the land prices of the treatment groups belonging to each station tend to
decrease in Table 1, every estimator is significantly positive in Table 2. These results
imply that the benefits of the LCS are capitalized as the discounted present value
immediately after the construction plan is revealed. The difference between columns (1)and (2) is caused by controlling for the time-invariant omitted variables. Furthermore, thedifference between columns (2), (3), and (4) is caused by controlling for location selection
bias. It can be seen that the estimation result is overestimated by about 2.3 percentage
points because of location selection bias. That is, the ATT of the LCS on residential land
prices is about 0.5% in the whole treatment group (see Figure 2).

Insert Talbe 2Insert F igure 2

B. AT T by station
According to Table 1, most of the treatment group belongs to the Shinagawa station
area and the Nagoya station area. The ATT of the treatment group in each station could
19differ because of the difference in the socioeconomic backgrounds of these areas. Table
3 reports the estimation results for each station for the four estimation procedures reported
in Table 2. Similar to Table 2, a reliable estimation result is provided by the fixed effects
model with IPW in column (4).

Insert Talbe 3

The effect of transport innovation on the residential land prices of the treatment groupbelonging to Shinagawa station is 3.3 percentage points (see column (2)). However, therise in land prices is -0.5 percentage points after controlling for location selection bias
(column (4)). The benefit of the treatment group belonging to Shinagawa station is
reducing the time distance to the Nagoya metropolitan area and further west. This result
implies that the benefit does not capitalize in residential land prices, perhaps because
Tokyo is overcrowded. The average treatment effect of the treatment group belonging to
Nagoya station, the other terminal station, is 1.7 percentage points even after controlling
for location selection bias. In other words, the benefit of shortening the time distance tothe Tokyo metropolitan area is capitalized in the residential land prices in that area. These
results are reasonable because the Tokyo metropolitan area has a population scale aboutfour times that of the Nagoya metropolitan area. That is, the time distance shortening
from a smaller economic scale area to a larger economic scale area increases land value
in this case.
However, the average treatment effect on the treatment group belonging to eachintermediate station is not large. The area showing rising land prices in the estimation
results by IPW is only the treatment group belonging to the station in Kanagawa
20

prefecture. Residential land prices in that area have risen by about 1.7 percentage points.
On the contrary, residential land prices do not tend to increase around the other stations.
Rather, land price decreases are found in the treatment group belonging to the stations in
Nagano prefecture and Gifu prefecture. As shown in Table 1, the treatment groups
belonging to stations other than the station in Kanagawa have low population density, ahigher share of the elderly population, and decreasing population. Although the time
distance to a major city has been greatly reduced, those areas could not be selected forresidence because of the low utility related to the consumption of goods other than thetime distance shortening. This negative estimation result might also have been caused by
the treatment groups including areas far from intermediate stations that do not receive thebenefits of the LCS. This issue is examined in the following subsections. Nonetheless,the result in Table 3 is consistent with the Fogelian view (Fogel, 1962, 1964) as well as
the conclusions of Banerjee et al. (2012) that the construction of transportation
infrastructure is worthwhile when in demand.
The result of the intermediate stations allows us to consider the natural experiment
for location selection. Therefore, the location selection bias of the intermediate stations issmall because of the geographical structure of the LCS. The results for the intermediate
stations in column (2) exploit the experimental situation for location selection. However,
the result for the station in Kanagawa in column (2) is significantly different from thosein columns (3) and (4). This difference appears to be due to sample selection bias basedon the spatial concentration of the sample. Further, the estimation results in column (3)are those when using the sample that dropped significantly different residential landmarkets based on the propensity score. Spatial sample selection bias is mitigated by thisprocedure. Interestingly, the results in columns (3) and (4) do not differ since the location21

selection bias of intermediate stations is small because of the geographical experiments.
To confirm the robustness of the analysis using propensity scores in Table 3, weestimate using two types of samples that are limited to the control group candidates. The
first sample omits Hokkaido and the second sample omits Kyusyu (see Figure 2). The
results are reported in Tables A1 and A2, which confirm no large differences, although
the sample size in the control groups in Tables A1 and A2 is less than that in Table 3.

C . H eterogeneity of the AT T in each station
The magnitude of the time distance shortening of the LCS does not vary in thetreatment group belonging to terminal stations because, for travel to Tokyo station from
Nagoya station, the time distance difference occurs only by using the LCS or the TS.
However, as revealed in the estimation results in Table 3, land prices rise in the area thatshows demand for time distance shortening. Demand in the area close to the station would
be large since it is easy to acquire the benefit. In this section, we examine whether rises
in land prices depend on the distance from each station.
Table 4 reports the estimation results using samples redefined depending on the
distance from the station adopting IPW. First, we explain the results for the terminal
stations. The treatment group belonging to Shinagawa station is widely distributed andthe ATT is significantly negative, while the ATT within 20 km of Shinagawa station is not
significant. On the contrary, the ATT for over 20 km is significantly negative. The averagetime distance to Tokyo station for the treatment group within 20 km to 50 km of
Shinagawa station is 52.6 minutes (standard deviation is 15 minutes). This time distanceis longer than the shortest time distance to Shinagawa station from Nagoya station (40minutes). Land demand in this area may have moved to the Nagoya area. Moreover, this22

area includes the city, where land prices decreased because radioactive material wasscattered from the Fukushima nuclear power plant (Kawaguchi and Yukutake, 2014).
Since the treatment group is close to Fukushima with increasing distance from Shinagawa
station, land prices fall.
By contrast, land prices for the treatment group within 100 km of Nagoya station rise,
while such land price rises in the treatment group within 20 km are greater. These results
imply that the expectation of public urban development and the integration of the private
sector as an indirect effect of the LCS are higher in Nagoya station and its periphery. The
ATT of the area within 50 km to 100 km is greater than that within 20 km to 50 km sincethat area includes the city, which would develop further owing to the time distanceshortening. The area within 100 km to 200 km includes the Osaka area, but land prices
have not yet increased because the extension of the LCS from Nagoya to Osaka is set for
2045.
Next, we explain the results for the intermediate stations. Land prices do not risesignificantly in the treatment group over 50 km from the station in Kanagawa prefecture.
This finding could explain why only one Shinkansen stops at intermediate stations every
hour. Land prices rise in the treatment group within 20 km of the station in Yamanashi
prefecture. The station in Yamanashi prefecture is in the center of the Kofu Basin, which
has an area of 275 km2. This result suggests that land prices in this basin rise. The risingland prices in the area within 50 km to 100 km of the station in Yamanashi and within 20
km to 50 km of the station in Nagano prefecture is likely to have occurred because we
could not control for the economic development effect of extending the Hokuriku
Shinkansen. Moreover, the ATT of Nagano and Gifu is significantly negative in Table 3
because this captures the situation of falling land prices of the treated group far from each23

station. In sum, the results in Table 4 are consistent with those in Table 3.

Insert Talbe 4

V I. Conclusion
This study investigated whether the benefits of the LCS, a high-speed railway inJapan scheduled to open in 2027, were capitalized in land prices immediately after theannouncement of the construction decision. We found that residential land prices in thearea that reduced the time distance to the Tokyo metropolitan area rose, except in the area
where the population is decreasing. This result implies that benefits are capitalized in land
prices when there is demand for time distance shortening.
This study confirmed that the estimation problems that location selection bias andthe timing of the treatment must be addressed in impact evaluations. Because theconstruction of infrastructure is always accompanied by location selection bias, theestimation results in many cases can be overestimated if not carefully controlled for. The
findings of this study indicated that sample selection bias may occur when samples areconcentrated spatially, even when exploiting an geographical experiment for location
selection. Furthermore, the presented findings also indicated the difficulty interpretingthe estimation results without considering the starting timing of the treatment. In
particular, this problem is serious when the outcome is a stock price and the theoreticalbackground of the analysis is consumer utility maximization. Although infrastructure
construction takes a long time, rational consumers take action when construction
information is disclosed if its effect is obvious. If the analysis had focused only on the24timing after the construction information was disclosed (i.e., a comparative analysis ofbefore and after starting the railway service), it could not have measured the direct effectof the railway.
Two future works are proposed from the findings of this study. The first is to examinechanges in the land prices from the disclosure of the construction information to the
opening of the LCS. If the time-shortening effect of the LCS is all capitalized immediately
after the disclosure, land price changes thereafter would be dependent on the discount rate
and additional urban development. The second is to examine how much demand risingland prices need. This is an important issue in determining the burden of construction
costs. If there is no demand or demand cannot be created, it would be preferable not tobuild a station, or the entire infrastructure.

Appendix: Definition of the treatment group
We define the treatment group as the point where the time distance to Tokyo station
or Nagoya station by using the LCS is shorter than the current time distance. Therefore,
we need information about the time distance to each city station using and not using the
LCS, at each survey point.
First, we determine the time distance to the city stations in the case of not using the
LCS. Each survey point of the official land price is combined with the spatial informationof the nearest railway station. At this time, the time distance from the survey point to thenearest station is defined as the travel time on the shortest time distance route based on
the speed limit of the road infrastructure (i.e., the digital roadmap). After this, each survey
point is given the time distance to each city station from the nearest station. The
information of this time distance is based on YAHOO! JAPAN route information on
25

October 21, 2011. In the case of changing the nearest train station after October 21, 2011,
such as opening or shutting stations, we used information on May 1, 2015.15 This time
distance is the shortest travel time when using all public transportation modes to city
stations from each station. This process provides the average waiting time and travel timefor each route based on the timetable, and uses the shortest travel time for eachtransportation mode. Of course, if the survey points of official land prices are far from themetropolis, the transportation mode of airplane is selected. In a word, we define the time
distance from the survey points to city stations when not using the LCS as the sum of theshortest travel time from survey points to the nearest station and the shortest travel timefrom the nearest station to the city station using existing transportation modes.
Next, we describe how to determine the time distance to the city station in the caseof using the LCS. Each survey point of the official land prices is combined with thenearest station of the LCS. Here, the method of calculating the time distance to theterminal or intermediate stations is different. First, we describe how to determine the time
distance to the terminal stations. For the treatment group belonging to Nagoya station, thetime distance is shortened only for Tokyo station. The route from this treatment group toTokyo station using the LCS is taking the LCS from Nagoya station and transferring to a
local train to Shinagawa station. Hence, we define the time distance to Tokyo station of
the treatment group belonging to Nagoya station as the sum of the time distance to Tokyo
station from Nagoya station using the LCS and the time distance already calculated fromthe survey points to Nagoya station. Conversely, the treatment group belonging to
Shinagawa station is the point where the time distance is shortened to Nagoya station. We
define the time distance to Nagoya station as the sum of the time distance to Shinagawa

15

We did not have digital information on May 1, 2015.
26

station via Tokyo station from the survey points because Shinagawa station is close to
Tokyo station and the time distance to Nagoya station from Shinagawa station using the
LCS. There is a point that the time distance is shorter going directly to Shinagawa station;
however, the effect of this measurement error in defining the treatment group is limited.
Second, we describe how to determine the time distance to the intermediate stations.
We define the time distance to each city station from the treatment group belonging to anintermediate station as the sum of the time distance to the nearest intermediate station of
the LCS from the survey point by car and the time distance to city stations fromintermediate stations. The time distance from the survey points to intermediate stations is
adopted as the shortest time distance based on the speed limit of each route rather than
the shortest route distance, as with the travel time to the nearest station from the survey
points.
A timetable for the LCS does not exist. Table A3 reports the average time distances
based on the published information. For example, the average time distance from
Shinagawa station to Nagoya station is the sum of travel time, 40 minutes, using the LCS
and average waiting time, seven minutes. The average time distance to transfer from
Shinagawa station to Tokyo station is 16 minutes. The average time distance from theintermediate station to city stations assumes the direct train overtakes the local train.
Although this assumption may include a measurement error, the estimation results inTable 4 allow us to check the robustness of these results.

27

A cknowledgements
This study benefitted from discussions and conversations with Keishi Kikuchi. It also
substantially benefited from comments by Takahiro Ito, Katsuo Kogure, Ryo Nakajima,
Mitsuru Ota, and Hajime Seya as well as helpful comments from the seminar participantsat the University of Tokyo, Osaka Prefecture University, and Hosei University. This
research was the result of the joint research with CSIS, the University of Tokyo (No. 596)
and used the following data: Digital Road Map data provided by Japan Digital Road Map
Association and Ekispert data provided by VAL Laboratory Corporation.

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Fogel, R., 1962. A quantitative approach to the study of railroads in American economic
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Fogel, R., 1964. Railroads and American Economic Growth: Essays in Econometric
History, Johns Hopkins Press, Baltimore.
Gibbons, S., Machin, S., 2005. Valuing rail access using transport innovations, Journalof Urban Economics 57(1), 148±169.
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strategies, The Economic Journal 109, 313±348.
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Fukushima accident, Discussion Papers 2014-18, Graduate School of Economics,
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0F0LOOHQ'30F'RQDOG-³5HDFWLRQRI+RXVH3ULFHWRD1HZ5DSLG7UDQVLW/LQH&KLFDJR¶V0LGZD\/LQH-´5HDO(VWDWH(FRQRPLFV  -486.
Michaels, G., 2008. The effect of trade on the demand for skill: Evidence from theinterstate highway system, The Review of Economic and Statistics 90(4), 683±701.
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regions of China, Papers in Regional Science, DOI: 10.1111/pirs.12198.

29

T able 1 : De sc riptive Statis tics
Treatment Group
Station / (City)*1

Shinagawa(Hashimoto)(Kofu)(Iida)(Nakatsugawa)

NagoyaPrefecture
L and Price (ye n/m2)
Land Price (before)Tokyo

Kanagawa

Yamanashi

Nagano

Gifu

Aichi

428780(1419431)
385811(1240708)

104433(54384)97537(52616)48116(29010)42541(25281)49783(45182)43255(37511)56110(25857)49242(21010)150651(361691)137822(297479)114637(448476)104163(395143)

182
(33)
131
(48)
251
(50)
127
(48)
2.6
(3.4)
65.3
(77.9)
423
(693)
62
(13)
200
(95)

242
(40)
155
(25)
213
(35)
117
(25)
1.6
(1.5)
77.2
(34.9)
300
(163)
64
(10)
221
(105)

243
(45)
180
(47)
133
(45)
112
(47)
2.8
(2.7)
72.5
(51.9)
296
(242)
64
(9)
222
(94)

219
(66)
185
(67)

191
(64)
155
(62)
1.7
(2.0)
80.5
(109.2)359(1599)
62
(12)
237
(152)

102
(31)
92
(32)
188
(47)
119
(32)
2.7
(2.6)
28.2
(34.2)
443(1979)
55
(12)
159
(81)

216
(106)
289
(213)
244
(93)
273
(180)
2.5
(5.3)
357.4
(332.8)
364
(897)
62
(12)
212
(119)

Land Price (after)
C haracte ris tics
Time distance to Tokyo Station(minutes)
Time distance to Tokyo Station
using Linear motor car (minutes)
Time distance to Nagoya Station(minutes)
Time distance to Nagoya Station
using Linear motor car (minutes)
Distance tothe nearest station (km)Distance to the nearest
Linear Station (km)Acreage
Building coverage ratio
Floor-area ratio

1.8
(3.2)
147.8
(104.3)457(3696)
63
(10)
224
(119)

Control group

M aching
Propensity Score*2

0.515
0.133
0.118
0.135
0.139
0.473
0.524
(0.265)(0.148)(0.118)(0.114)(0.205)(0.148)(0.133)Population
25198323591911798711234764165
174065
195117(204635)(178032)(87574)(93787)(42810)(135663)(150707)
Population density_per_km2
6639
2926
490
257
322
3611
2187
(5992)(2327)
(394)
(115)
(912)
(3806)(2782)
Population ratio of over 65
21.24
20.89
24.76
25.84
28.41
22.82
23.76
(3.47)(2.56)(4.25)(2.66)(3.48)(3.93)(4.56)
Population trends
2.82
1.36
-1.72
-1.25
-4.00
0.45
-0.42
from 2005 to 2010
(4.79)(2.40)(2.82)(2.34)(2.46)(3.94)(3.95)
office number
11575
12804
6478
5968
3907
8073
8940
(10058)(9187)(5209)(5413)(2665)(6907)(7326)
number of the employees
145669143660
59672
55433
31784
86764
93951
(183551)(100408)(49642)(51378)(24313)(83546)(81372)
O bse rvations
35160
2224
1472
928
272
45920
63832
Notes : standard deviation in parentheses. *1; This line reports the station name. If the station name is undecided, the cell reports the name of the city thatthe station will be built in. *2; This propensity score is a value calculated using the total sample. It is not a propensity score to be used the analysis in eachstations.

30

Table 2: ATT of transportation innovation on residential land price: estimates on whole sample

DID

treatD
Other Controls
Fixed effectYear fixed effectNumber of obs
Number of individuals
R-sq (within)(1)
0.0158(0.0067)✓✓149808
0.656**

Fixed Effect DID

Fixed Effect DID(0.1<=PS<=0.9)(2)
0.0284(0.0011)✓✓✓14980818726(3)0.0283(0.0011)✓✓✓
144360
18045

0.644

***

0.648

***

DID
(IPW: 0.1<=PS<=0.9)(4)0.0049(0.0011)✓✓✓
144360
18045

***

0.623

Notes : Robust standard error in parentheses. *, **, *** denote significance at the 10, 5 and 1% levels, respectively.

31

Table 3: ATT of transportation innovation on residential land price by station

DID

Tokyo

(1)
0.0250(0.0099)[83216]**

Fixed Effect DID

Fixed Effect DID(0.1<=PS<=0.9)(2)
0.0326(0.0015)[83216](3)
0.0258(0.0017)[70832]******

Fixed Effect DID(IPW: 0.1<=PS<=0.9)(4)-0.0055(0.0020)[70832]**Kanagawa

0.0188(0.0165)[66056]

0.0317(0.0030)[66056]***0.0178(0.0051)[4320]***

0.0168(0.0054)[4320]

Yamanashi-0.0037(0.0262)[65304]-0.0079(0.0042)[65304]*0.0127(0.0069)[3632]*

0.0067(0.0071)[3632]

Nagano-0.0029(0.0290)[64760]-0.0183(0.0048)[64760]***-0.0109(0.0085)[2176]-0.0166(0.0084)[2176]**

Gifu

0.0069(0.0681)[64104]-0.0006(0.0130)[64104]-0.0461(0.0322)[176]-0.0724(0.0361)[176]*

Nagoya***0.0299***0.0445***0.0443***0.0172***(0.0074)(0.0014)(0.0014)(0.0016)[93976][93976][91528][91528]Notes : Robust standard error in parentheses. The Number of observations in square brackets. *, **, *** denote significanceat the 10, 5 and 1% levels, respectively.
32

Table 4: ATT of transportation innovation on residential land price by station & distanceTokyo
Kanagawa
Yamanashi
DID (IPW: 0.1<=PS<=0.9)
within 20km
-0.00640.0142***0.0216**(0.0111)(0.0051)(0.0100)[1912][2624][1648]
20km ~ 50km

-0.0248(0.0022)[15384]***0.0205(0.0107)[776]*

0.0385(0.0268)[360]

50km ~ 100km-0.0139(0.0028)[28712]***-0.02433(0.0161)[184]

0.0423(0.0240)[296]

100km ~ 200km-0.0234(0.0067)[12928]***0.01078(0.0213)[280]-0.0168(0.0132)[80]

Nagano0.0207(0.0216)[240]
0.0267(0.0147)
[136]

*

Gifu

*

-0.00473(0.0113)[1040]-0.0539(0.0138)[96]***

Nagoya-0.0382(0.0040)[11544]***-0.0237(0.0316)[32]

0.0209(0.0032)[19304]***0.01410(0.0438)[80]

0.0336(0.0048)[11088]***-0.2004(0.0297)[56]***

0.0022(0.0019)[55968]Notes : Robust standard error in parentheses. The Number of observations in square brackets. *, **, *** denote significance at the 10, 5 and 1% levels, respectively.

33

Figure 1: Shinkansen stations in Japan34

Figure 2: Treatment group and control group

Note: Fukushima prefecture, Toyama prefecture, Ishikawa prefecture, and Okinawa prefecture are dropped in all
analyses. Hokkaido prefecture and the Kyusyu area are dropped in the analysis of the robustness checks.

35

Table A1: ATT of transportation innovation on residential land price by station: estimates except Hokkaido

DID

**

Fixed Effect DID

Fixed Effect DID(0.1<=PS<=0.9)

Fixed Effect DID(IPW: 0.1<=PS<=0.9)

0.0286(0.0015)[74168]***0.0224(0.0018)[61568]***-0.0070(0.0021)[61568]

**

***

Tokyo

0.0235(0.0097)[74168]Kanagawa

0.0183(0.0158)[57008]0.0274(0.0030)[57008]***0.0176(0.0048)[4456]***

0.0176(0.0060)[4456]

Yamanashi-0.0072(0.0260)[56256]-0.0122(0.0042)[56256]***0.0119(0.0063)[4160]*

0.0082(0.0065)[4160]

Nagano-0.0042(0.0292)[55712]-0.0227(0.0048)[55712]***0.0125(0.0073)[2896]*

0.0020(0.0072)[2896]

Gifu

0.0053(0.0693)[55056]-0.0051(0.0130)[55056]-0.0508(0.0244)[280]**-0.0605(0.0247)[280]

Nagoya**0.0263***0.0406***0.0409***0.0132**(0.0071)(0.0015)(0.0015)(0.0017)[84928][84928][82320][82320]Notes : Robust standard error in parentheses. The Number of observations in square brackets. *, **, *** denote significanceat the 10, 5 and 1% levels, respectively.
36

Table A2: ATT of transportation innovation on residential land price by station: estimates except Kyusyu

DID

**

Fixed Effect DID

Fixed Effect DID(0.1<=PS<=0.9)

Fixed Effect DID(IPW: 0.1<=PS<=0.9)

0.0363(0.0017)[68848]***0.0313(0.0019)[57064]***0.0046(0.0021)[57064]*****0.0212(0.0047)[5360]***

Tokyo

0.0284(0.0110)[68848]Kanagawa

0.0193(0.0172)[51688]0.0315(0.0031)[51688]***0.0295(0.0046)[5360]

Yamanashi-0.0018(0.0268)[50936]-0.0079(0.0043)[50936]*0.0059(0.0066)[4248]

0.0017(0.0069)[4248]

Nagano-0.0034(0.0302)[50392]-0.0186(0.0048)[50392]***-0.0008(0.0078)[2568]-0.0087(0.0081)[2568]

Gifu

0.0060(0.0698)[49736]-0.0008(0.0130)[49736]

Nagoya-0.0477(0.0254)[280]*-0.0651(0.0313)[280]**

0.0326***
0.0482***0.0495***0.0263**(0.0088)(0.0016)(0.0017)(0.0017)[79608][79608][75848][75848]Notes : Robust standard error in parentheses. The Number of observations in square brackets. *, **, *** denote significanceat the 10, 5 and 1% levels, respectively.
37

Table A3: time distance to metropolirtan Stations by Linear staions (minuts)
to Tokyo Station
to Nagoya Station
Shinagawa
16
47(Kanagawa)
57
88(Yamanashi)
74
74(Nagano)91
57(Gifu)
106
42
Ngoya
63
0
Notes : In parentheses of the intermediate stations, it is entered the name of
the city that the station is buil.

38