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6225 words + 5 tables = 7475 word counts
Regional and Local Economic Effect from Proximity of High-Speed Rail Stations in Japan:
Difference-in-Differences and Propensity Score Matching Analysis
Jetpan Wetwitoo (Corresponding author)
Creative Research and Planning Co., Ltd.
6Fl, Mitomi Building, 1-20-22, Ebisu, Shibuya-ku, Tokyo 150-0013, Japan
Phone: +81-3-5791-1133; Fax: +81-3-5791-1144
E-mail: j_wetwitoo@crp.co.jp
Hironori Kato
Department of Civil Engineering, The University of Tokyo
7-3-1, Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
Phone: +81-3-5841-7451; Fax: +81-3-5841-7496
E-mail: kato@civil.t.u-tokyo.ac.jp

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ABSTRACT

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This paper gives empirical evidence about the economic effect of proximity of high-speed rail (HSR) on
regional/local production and labor productivity in the case of Japan. The effect on a regional scale and alocal scale are analyzed based on a Difference-in-Differences (DID) method and Propensity Score
Matching (PSM) method. The prefecture-level analysis investigates the effect on regional production in
prefecture-level during 1981-2006 and the municipality-level analysis investigates the effect on the local
tax revenue and the tax revenue per capita in municipality level, particularly focusing on new HSR
extensions during 2010-2015. The results from both levels show statistically insignificant estimations ofboth DID effect and the Average Treatment Effect on the Treated (ATT) from PSM, which implies that,on average, no direct effect of the proximity to HSR service on the regional/local production nor
productivity. One of the potential reasons, the effective range of HSR service to the local economy, arediscussed. Based on the local characteristics where HSR station is located, the effective range of HSR
service could vary from 10 to 30 km.

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Keywords.

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High-speed rail; proximity effect, Difference-in-Differences (DID); Propensity Score Matching (PSM);
regional economic development; Japan;

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1

INTRODUCTION

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The high-speed rail (HSR) has been regarded as one of the main inter-city modes of transportation in
many developed countries in Europe and Asia. Recently, the rapid development of extensive HSR
network in China has caught an attention from many developing countries since it coincided with therapid economic growth in China in the past few decades (1). Not only the case of China, but thecontributions of HSR to economic development in developed countries have been reported by manystudies (2, 3). Yin et al. (1) provides many great examples of how HSR affects the development fromseveral perspectives.

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The effect of HSR to the economy could be observed with many indicators, such as the reduction of travel
time (4), change in land use pattern (5), as well as the employment effect (6–8) and migration effect (9,10). However, the production, determined by the gross domestic product (GDP), could be the moststraightforward indicator to determine the contribution from HSR to the economy. The positive effect of
HSR to production, typically measured with GDP, and the positive effect on productivity, typicallymeasured with a labor productivity such as GDP per employment, have been reported from many
countries. Generally, studies from European countries have reported the positive effect of HSR. For
instance, in France, GDP per capita in the region with HSR service tends to be higher with the expense oflower GDP per capita in an adjacent region (11). In Spain, the investment of HSR positively affects GDP
and employment level through better accessibility (12). A similar argument was also suggested in the caseof UK HS2 project where HSR investment could boost the economy through an agglomeration impact(13). The availability of HSR is also reported positively affecting regional GDP in South Korea (10).
However, empirical studies on Japanese HSR have displayed quite mixed results. Sasaki et al. (14)concluded HSR network expansion contributes to regional development in non-core areas to some extent,
yet it strengthens the development in core areas in a long run. Bernard et al. (15) reported that firms
located close to HSR service tend to perform better, especially those located within 30 km from HSR
station. Wetwitoo and Kato (16) also found that the HSR effects on productivity are significant in theearly stages of introduction where HSR network was still limited in the core areas of Japan.

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This study highlights expected impacts particularly from the proximity of HSR stations on the regional
economic productivity, or the proximity effect of HSR. The proximity effect means that the economic
impacts in the zones that are located within the buffer of HSR stations. In many countries where HSR has
been already introduced, local municipalities along the existing HSR lines often request an introduction of
additional stations into their municipalities since they expect an improvement of accessibility for local
people/business as well as its indirect impacts on the local economy through the better accessibility and/or
existence of HSR station. One example in Japan is an introduction of a new station between ShinYokohama and Odawara along the Tokaido Shinkansen1 line, which has been proposed by local
municipalities since 1975, but it has not yet been realized. The main issue in proposing the introduction of
new HSR stations is the financial burden of local governments for the construction of new infrastructure.
Thus, the local/regional economic impacts should be one of the major concerns for many local societies tojustify an expensive spending in the construction of new HSR station facilities. However, for example,
Wetwitoo and Kato (16) showed that the HSR effects are found positive with respect to the network
utilization and the agglomeration effect but the presence of HSR stations does not affect the productivity.
Does the proximity of HSR stations really have no impact on economic productivity?

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Although several estimation techniques have been utilized in past studies, many of them failed to clearlypresent the relevant outcomes by comparing the situation before an introduction of HSR/HSR stations
1

The word for “Japanese High-Speed Rail”

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with that after. One of the simple, but powerful approaches to solve this issue is to apply the estimation
under the framework of Difference-in-Differences (DID) and Propensity Score Matching (PSM). DID has
been widely applied in social sciences, including transportation-related studies in urban scale (17), in
regional scale (18) as well as the effect of HSR introduction in Japan (19, 20), Spain (21), Uzbekistan(22), and China (23). Similarly, PSM originally introduced in social sciences studies, and later applied intransportation-related studies (24–26). PSM was also applied by Komikado and Kato (27) to investigate
the effect of HSR on regional innovation. These two matching methods will be utilized in this paper toinvestigate the effect of the proximity of HSR stations to regional/local production and productivity in thecase of Japan. The long history of HSR service, combined with the continuous network expansion in
Japan could be one of the unique characteristics to the robustness in the results where further investigation
would be appealing to understand the effect of HSR to the economy.

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The paper is organized as follows. Next section discusses a concept behind both matching technique andreviews the empirical evidence where DID and PSM have been utilized in the studies related to HSR.
Next, the empirical data used in this study and definitions of treatment and control groups in DID and
PSM analysis are shown. The empirical analysis section where DID and PSM are both applied includes
the analysis in prefecture-level during 1981-2006 and the analysis in municipality-level, focusing on theregions with recent HSR extensions during 2010-2015. The conclusions section summarizes the findings
from the empirical analyses and further issues to be discussed.

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METHODOLOGY

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Difference-In-Differences Design
This study assumes a proximity to HSR as one of the major policies to promote regional development inJapan. DID is applied to evaluate the effect of introducing HSR stations by comparing two situations:
before the introduction and after the introduction. DID is an econometric technique that estimates thepolicy effect from the difference between a treatment group (regions/municipalities in which HSR
stations have been newly introduced) and a control group (other regions/municipalities), based on thechanges from before a policy shock (the introduction of HSR stations) and after the shock. Under the DID
framework, the parallel trend assumption must be held. In other words, without the consideration of the
HSR proximity effect, “time effect,” which captures other effects that occur between before and after
consideration period, in the treatment group must be the same as that in the control group. In order toachieve more accurate estimation, additional control variables should be introduced to ensure the parallel
trend assumption. In contrary, “Treatment effect” captures the possible difference between the treatmentgroup and the control group. Finally, “DID effect” captures the possible difference, which deviates theoutcome from the parallel trend assumption. It is assumed that the concerned policy (introduction of HSR
stations, in our case) which implemented during this period creates the DID effect.

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Although the DID framework expresses the concept under the two time-period analysis, it is also possible
to extend it to DID with multiple years. In this case, some studies introduced year control variables
instead of the time effect or even other studies introduced both. However, the estimation with multiple
years under the DID framework could suffer from an autocorrelation problem which leads tooverestimation of the significant level (28). Thus, it is suggested that if the sample size were large
enough, the analysis in two time-period, before and after, would yield a more consistent result with somecost of a reduction of insignificance (28). Therefore, our study will strictly follow the analysis in a twotime-frame approach.

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To apply the DID method, a general model of our analysis is formulated as:

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=

+

+

+

+

+

(1)

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where

,

= Dependent variable in region i at year t;= Treatment effect. 0 if i belongs to the control group and 1 if i belongs to the treatment group;= Time effect. 0 if t is before HSR introduction and 1 if t is after HSR introduction;= DID effect, which equals to × ;= A vector of control variables in region i at year t;= Unknown coefficients; and= Error component.

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The model is estimated with the Ordinary Least Square (OLS) method. When the coefficient of thetreatment effect ( ) is estimated positive, the treatment group has a higher effect than the control groupeven before the HSR introduction. This could represent a selection bias, which arises from the fact theregion with higher economic performance could be selected as a member of the treatment group. Whenthe coefficient of the time effect ( ) is estimated positive, the trend of economic growth is positive under
the parallel trend assumption. Finally, when the coefficient of the DID effect ( ) is estimated positive,
we may conclude that the introduction of HSR stations positively affect the economic performance.

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Propensity Score Matching
PSM is another econometric technique that estimates the policy effect from the difference between atreatment group. However, the time before and after implementation, and the assumption of the parallel
trend is not considered. Instead, PSM estimates the score as the probability of being a treatment groupfrom the rest of the explanatory variables based on the logit model. The probability function to estimate
propensity score can be expressed as:22( )= (= 1| )

(2)

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where

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(∙) = A propensity score function;= Treatment effect. 0 if zone i belongs to the control group and 1 if i belongs to the treatmentgroup; and= A vector of control variables in zone i;

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Once the score is estimated, PSM matches the samples between treatment group and control group withthe similar score. In practice, several methods and criteria have been applied to the matching process but
in this study, we selected the nearest matching method with a 1-to-1 matching restriction which is themost popular method used in PSM. In addition, we do not set any matching caliper, as our treated samples
are quite limited so we want to match the treated samples as much as possible.

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In the PSM analysis section, we provide two methods to estimate the HSR proximity effect on theeconomy. First, we follow the typical PSM approach by estimate the Average Treatment Effect on theTreated (ATT) which can be calculated as the difference of the concerning response variable between thematched pair. This ATT could be treated as an equivalent to DID effect if the time effect is alreadyabsorbed by other control variables. Second, we consider the possibility of the endogeneity between HSR
effect and other control variable. In another word, the possibility that HSR could affect not only the
economy but also the other control variable such as input levels and industrial structure as well.
Therefore, we use only the matched data to re-estimate DID effect following Eq.(1). Later, we will callthis method “matched DID.”

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Table 1 compiles the literature related to HSR that analyzed its impacts using the DID and PSM method.
As for DID, most of the studies revealed a statistical significance in the DID parameter which implies that
HSR creates positive economic impact in the region with new HSR service. However, it should be notedthat all studies with DID analysis listed in Table 1 used a panel data with multiple years of data to analyze
the DID effect. In contrary, in order to provide the clear distinction between before and after the
implementation of HSR, this study considers the economic condition only before the introduction of HSR
stations and after that. Similarly, PSM analysis from other studies usually considers the treatment effectwithin the single-time frame. However, two-time frame data is also applied to PSM in order to make aclear comparison between the same dataset in this study. A two-time frame data is also applied to PSM inorder to make a clear comparison between the same dataset whereas PSM from other studies usuallyconsiders the treatment effect within one-time frame.

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Table 1. Examples of studies related to HSR effect analyzed under DID and PSM framework
Study

Study
Area

Time

Dependent
Variable(s)Independent Variables

DID / PSM
significanceSpain20012010

treatment effect + time effect + DID + other control
variables (population, unemployment, pop.density,
area, time-control, etc.)

Significance(conditionally)

Yoshino &Abidhadjaev (22)Uzbekistan

20052012

Revenues/capita, Public
debt/capita, Fiscal
gap/capita,Yearlyproperty tax
GDP growth rateSignificanceYoshino &Abidhadjaev (19)
Li & Xu (20)Japan

Tax Revenue

Jia et al. (23)China

19822013
19802003
20002013

DID + year control + pref. control + other control
variables (government expenditure/private
investment on each sector)
DID + year control + pref. control + no. of tax payer
DID + year control + municipality control + highway
investment
treatment effect + time effect + DID + other control
variables (HSR frequency, investment, local
expenditure, industrial structure, road density, etc.)

Significance(negative)
No significance

PSM
Komikado andKato (27)Japan

19812006

Patent applications

GDP, GDP per capita, Firm density, Employment
density, Number of University, etc.

Significance isfound with asmall caliper

DID
Hernandez &Jimenez (21)Japan

Population
GDP
GDP/capita

Significance

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DATA

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In order to examine the effect of the proximity of HSR stations on economic production and laborproductivity in Japan, we divide the analysis into two parts by utilizing the dataset measured in two
different geographical levels: prefecture level and municipality level. All of the data used in this study are
acquired from e-Stat provided by The Statistics Bureau of Japan. The prefecture-level analysis uses thedataset at the prefecture level in Japan, the first administrative level whose population size is equivalent toaround the half of that in states of United States. It assumes the situation in 1981 as the year before theshock and that in 2006 as the year after the shock. The municipality-level analysis uses the dataset in themunicipality level in Japan, the second administrative level similar to a county in the U.S. In themunicipality-level analysis, we exploit the rich dataset in later years to investigate effect from the new
HSR stations in the smaller scope between 2010 and 2015.

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One of the most important issues in the DID analysis is to define the treatment and control groups. In ourcontext, the treatment group should consist of prefectures/municipalities in which HSR stations had been
newly introduced during our time frame. Then, how should we define the control group? For example, thecontrol group could include the regions that have already had HSR services in the before year together
with other regions that have no HSR services at both before and after years, although the megacities were

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discarded from the analysis (19, 21). As we are aware of the long-term effect of HSR, we have decided todivide the control group into two subgroups. The control group 1 consists of the regions where no HSR
service was available at both before and after years, while the control group 2 consists of the regions
where HSR service was available at both before and after years.

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In the prefecture-level analysis, we estimate two DID models, two PSM models and two matched DID
models using the control groups 1 and 2 respectively. Certainly, the control group 1 should be more
highlighted since the objective of our study is to clarify the economic effect of the proximity of HSR
stations when its service becomes available. The model estimation with the control group 2 aims to ensure
whether or not the introduction of HSR stations has caused a single-time boost in the regional economies
right after HSR station service becomes available since the control group 2 is also assumed not to beaffected by new HSR stations. Furthermore, the test with control group 2 also intends to examine a
potential correlation between site selection of HSR stations and size of the regional economy because
regions with higher economic activities tend to be selected as the sites of HSR stations before others. The
prefecture-level analysis covers the period from 1981-2006, which could be the best period to investigate
the effect of HSR station investment in the mid-to-late HSR extension plan in Japan. These HSR
investments were intended to promote the regional economy rather relieve the traffic congestion, which
had been the main objective for early HSR development in Japan. Thus, in the prefecture-level analysis, it
is assumed that the treatment group contains the prefectures where the HSR services were available
during 1981 and 2006; the control group 1 contains the prefectures where no HSR station was available
before 2006; and the control group 2 contains the prefectures where the HSR services had been alreadyavailable before 1981.

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In the municipality-level analysis, we estimate regression models with the treatment group and the control
group 1 only, using the municipality-level dataset; but we shift our scope to the period of 2010-2015. The
municipality-level analysis focuses on the municipalities in seven prefectures where the latest extensionsof the Kyushu Shinkansen in 2011 and Hokuriku Shinkansen in 2015 were implemented. Other
municipalities outside the study area are excluded from the analysis. The treatment group is assumed tocontain the municipalities within 5 km buffer from new HSR stations (21) along the HSR lines extended
during the period from 2010-2015, and the control group 1 assumes other municipalities beyond 5 km
buffer from HSR station.

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EMPIRICAL ANALYSES

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The prefecture-level analysis investigates the economic impact of the proximities of HSR stations inregional scale from 1981-2006. The network extensions during this period are intended to promote
regional development in the Eastern and the Western parts of Japan. From the general model in Eq. (1),we restructure the to-be estimated DID model as a form of production function as follows:

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=

where

+

+

+

+

+

+

(3)

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= ln Gross Regional Product (GRP) in prefecture i at year t;= Treatment effect. 0 if i belongs to the control group and 1 if i belongs to the treatment group;= Time effect. 0 if t is 1981 and 1 if t is 2006;= DID effect, which equals to × ;= ln Capital input in prefecture i at year t; and= ln Labor input in prefecture i at year t.

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The municipality-level analysis investigates the economic impact of two new HSR lines and stations inmunicipality scale from 2010-2015. As richer socio-economic data are available in municipality level
during this period, it enables us to control the economic growth so that the effect from DID components
could be estimated as close to the parallel trend assumption as possible. This also benefits PSM as wellsince better matching could be achieved from more control variable. It should be noted that, as the resultof urbanization and depopulation in Japan, there are many mergers among neighboring municipalities,
thus we aggregate data of those merged municipalities based on the municipality list after 20142.

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Yet, another question arises: “what is the effective range of HSR to the local economy?” Bernard et al.
(15) utilized the firms’ data in Kyushu island, Japan and suggested 30 km could be the effective range of
HSR service to firms’ performance. However, an area with a radius of 30 km roughly is, in fact, nearly
equal to that of the prefecture in Japan, which we have already investigated in the prefecture-level
analysis. Then, we assume that the treatment group contains the municipalities within 5 km buffer fromnew HSR stations, following Hernandez & Jimenez (21). In the municipality-level analysis, we use tax
revenue as the dependent variable instead of GRP since municipality-level GRP is not available across thenation. This analysis further analyzes the labor productivity in addition to the production, using tax
revenue per capita. The dependent variables are the tax revenue and the tax revenue per taxpayer. The
potential control variables are selected, following Hernandez & Jimenez (21) and Jia et al. (23); then themodel is specified as follows:

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=

where

2

+

+

+

+

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+

+

+

+

+

+

= ln tax revenue, or ln tax revenue per number of taxpayer in municipality i at year t;= Treatment effect. 0 if i belongs to the control group and 1 if i belongs to the treatment group;= Time effect. 0 if t is 2010 and 1 if t is 2015;= DID effect, which equals to × ;= ln number of taxpayer in municipality i at year t;= ln area of municipality i at year t;= male/female ratio of municipality i at year t;

No municipality merging was conducted after 2014(4)

= working3 population/total population ratio of municipality i at year t;= daytime population/nighttime population ratio of municipality i at year t;= share of workers in secondary industry in municipality i at year t; and= share of workers in tertiary industry in municipality i at year t.

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Table 2 summarizes the estimation results of the DID model in the prefecture-level analysis. This showsthat the estimates of the time effect are significantly positive in both Models 1-1a and 1-2a, while theestimate of the treatment effect is positive in Model 1-1a but that is negative in Model 1-2a. It also showsthat the estimate of the treatment effect is significantly positive in Model 1-1b but insignificant in Model
1-2b and that both Models 1-1b and 1-2b have the significantly negative time effect. First, the results thatthe treatment effect is positive for the control group 1 but negative or insignificant for the control group 2suggest a selection bias that the regions with higher GRP tend to have HSR service earlier. Next, theresults that the time effect is positive without controlling capital and labor inputs but negative with
controlling them suggest that the capital and labor inputs have grown significantly during the period but
the overall technology or productivity has declined. This reduction in technological productivity may
reflect the reduction of productivity of Japanese service firms during this period due to the lack ofinvestment and increase of the share of part-time workers (29). Finally, the results of all models unveilthat our concerned parameter, DID, does not reach an acceptable significance level.

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As for the municipality-level analysis, first, when the control variables are not introduced, the treatment
effect is found significantly positive on tax revenue as shown in Models 2-1k and 2-1h, but not on tax
revenue per taxpayer as shown in Models 2-3k and 2-3h. However, when the control variables are
introduced, the treatment effects are insignificant in both HSR lines as shown in Models 2-2k, 2-2h, 2-4k,
and 2-4h. They mean that the control variables can well absorb the difference between the treatmentgroup and the control group, suggesting that the municipalities where the HSR lines were introduced had
not been biasedly selected in these extension cases. Next, the results show that the time effect isconsistently insignificant when the control variables are not introduced, which obviously reflects the
economic stagnation during 2010-2015 especially in the non-core areas of Japan such as Kyushu andHokuriku regions. However, as the shrinkage of the local economy has been controlled in Model 2-2 andModel 2-4, the time effect becomes significant. Finally, the results show that again, DID effect is not
significant in any model.

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Working population is defined as population in the age between 15-64 years old

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Table 2. Estimation results from DID model
Prefecture Level

CONST.
C
T
DID
K
L
ADJ. R2
N

3

CONTROL GROUP 1
Model 1-1aModel 1-1b
Estimatet-statsig
Estimatet-stat28.678260.205 ***
0.077
0.249
0.483
2.493
*
0.045
2.977
0.534
3.426
**
-0.159-5.9470.049
0.178-0.008-0.393
0.365
8.79
0.638
16.486
0.303
0.996
68

sig
**
***
***
***

CONTROL GROUP 2
Model 1-2aModel 1-2b
Estimatet-statsig
Estimatet-stat29.965159.065 ***
0.216
0.313
-0.803-2.887
*
0.02
0.778
0.558
2.095
**
-0.107-2.1780.025
0.063-0.016-0.478
0.282
4.003
0.721
13.257
0.315
0.995
48

*
***
***

Municipality LevelYTAX REVENUE

KyushuCONST.
C
T
DID
P
A
S
W
D
ISE
ITE
ADJ. R2N
Y

Model 2-1k
Estimatet-stat
16.940117.253
1.297
3.138
0.034
0.166
0.001
0.001

Hokuriku

Model 2-2k
Estimatet-stat6.661
63.199
0.011
0.624
0.053
4.494
0.000
0.016
1.009
128.987-0.007-1.043-0.001-0.759
0.017
7.472
0.001
2.868
-0.002-2.072
0.002
2.527
0.999

sig
***
**

0.086

sig
***
***

Model 2-1h
Estimatet-stat
16.61284.504
1.613
2.595
0.002
0.008
0.008
0.009

***

***
**
*
*
0.070

180

CONST.
C
T
DID
A
S
W
D
ISE
ITE
ADJ. R2N

Model 2-2h
Estimatet-stat7.283
68.509
0.021
1.163
0.028
2.870
-0.006-0.2651.034
194.161-0.035-6.442
0.001
1.115
0.002
1.151
0.001
1.887
0.002
2.761
0.001
1.476
0.999
140

sig
***
*

sig
***
**
***
***

.
**

TAX REVENUE PER PAYER

Kyushu45

sigModel 2-3k
Estimatet-stat7.847
629.353
0.043
1.205
0.006
0.322
0.005
0.105

Model 2-4k
Estimatet-stat6.643
63.710
0.015
0.926
0.059
5.545
0.000
-0.006-0.001-0.292-0.002-1.2850.019
11.6800.001
2.721-0.001-1.924
0.002
3.119
0.796

sig
***

0.002

Hokuriku
sig
***
***

Model 2-3h
Estimatet-stat7.846
809.916
0.055
1.788
0.023
1.653
-0.005-0.111

***
**
.
**

180

Note: “***”: p < 0.001; “**”: p < 0.01; “*”: p < 0.05; “.” p < 0.1.

0.040

Model 2-4h
Estimatet-stat7.150
60.118
0.029
1.436
0.052
5.105
-0.006-0.208-0.014-2.859-0.001-0.534
0.009
7.844
0.002
2.702
0.002
3.661
0.001
1.868
0.613
140

sig
***
.

sig
***
***
**
***
**
***
.

1
2
3
4
5
6
7
8
9
10

Next, we present the analysis from the PSM model. The prefecture-level analysis in control group 1 isbased on Model 1-1b and control group 2 is based on Model 1-2b. We also assume that the time effect isalready been absorbed by capital and labor inputs. From the general model in Eq. (2), we restructure theto-be estimated PSM model as follows:
(

)= (

= 1|

)

,

(5)

As for the municipality-level, the analysis for tax revenue is based on Model 2-2k for Kyushu region andModel 2-2h for Hokuriku region, while the analysis for tax revenue per capita is based on Model 2-4k for
Kyushu region and Model 2-4h for Hokuriku region. We also assume that the time effect is already beenabsorbed by control variables. Following Eq. (5), we restructure the to-be estimated PSM model as:
(

)= (

= 1|

,

,

,

,

,

,

)

(6)

11
12
13
14
15
16
17
18
19
20
21

Table 3 summarizes the estimation results of ATT estimation from the PSM model in the prefecture-level
analysis. The result of the PSM model shows a positive significant result at 95% confidence interval incontrol group 1. It suggests that there is an additional premium to GRP once new HSR and its stations
have been established in the region. However, the result suggests no difference between the treated region
and the region where HSR has already been established (control group 2). Originally, in the data section,
we assumed that there is no HSR effect control group 2. However, according to the insignificant estimate
from control group 2 analysis, this assumption could be wrong. There might be a long-term HSR effect incontrol group 2 so the result shows no difference of ATT between treatment group and control group 2. In
contrary, all of the results from the municipality-level analysis show the insignificant result. This could
further confirm the result from DID analysis that there is no significant difference between cities with
new HSR service and those without in tax revenue and tax revenue per capita.

22
23

Table 3. Estimation results of ATT from PSM model
Prefecture Level

24

ESTIMATE

T-STAT

SIG

CONTROL GROUP 1

0.210

1.969

*

CONTROL GROUP 2-0.381-1.578ESTIMATE

T-STAT

Municipality Level
SIG

Tax revenue
KYUSHU

0.553

0.880

HOKURIKU

0.372

0.355

Tax revenue per payer
KYUSHU

0.046

0.901

HOKURIKU

0.010

0.208

25

Note: “***”: p < 0.001; “**”: p < 0.01; “*”: p < 0.05.

26
27
28
29
30
31
32

Next, we present the results from the matched DID model in Table 4. As we utilize the matched data,
similarly to the PSM model, the prefecture-level analysis in control group 1 is based on Model 1-1b andcontrol group 2 is based on Model 1-2b. Similarly, in the municipality-level, the analysis for tax revenue
is based on Model 2-2k for Kyushu region and Model 2-2h for Hokuriku region, while the analysis for tax
revenue per capita is based on Model 2-4k for Kyushu region and Model 2-4h for Hokuriku region. By
considering only the matched data, we expected the significant improvement from matched DID.
However, the results shown in Table 4 are quite identical to the result shown in Table 2. This similarity

12
3

could be explained by several reasons, such as the similarity between matched and unmatched data, or thelimited sample size used in this analysis. Consequently, we still could not find the significant effect fromnew HSR lines/stations at both levels.

4
5

Table 4. Estimation results from matched DID model
Prefecture Level
CONTROL GROUP 1
CONST.

Estimatet-stat

0.421

1.107

Estimatet-stat

0.561

0.881

0.043

2.502

T

-0.150-4.655DID-0.013-0.509

K

0.369

7.594

***

0.305

4.845

***

L

0.621

13.468***0.685

13.496

***

***

0.012

0.525

-0.115-2.622-0.013-0.420sig

C

ADJ. R2

6

sig

CONTROL GROUP 2

0.996

0.994

Municipality Level
TAX REVENUE

KyushuTAX REVENUE PER PAYER

Hokuriku

KyushuHokuriku

Estimatet-statsig

Estimatet-statsig

Estimatet-statsig

Estimatet-statsig

CONST.

7.018

15.579***6.569

27.535***6.854

29.088***5.941

12.911***C-0.001-0.072

0.007

0.457

0.035

2.792

**

0.004

0.225

T

0.070

2.461

0.068

3.365

0.061

3.618

**

0.073

2.582

DID

-0.004-0.152-0.007-0.384

0.002

0.067

P

1.019

61.452A-0.007-0.590S-0.004-0.746

***

W

0.014

3.269

**

0.015

4.103

D

0.001

0.749

0.000

-0.173

ISE

0.001

0.748

-0.003

ITE

0.002

0.791

-0.001ADJ. R

2

1.000

***

**

-0.014-0.632

1.036

67.395-0.021-1.734

0.003

0.476

0.005

0.363

0.003

0.827

-0.004-1.191

0.004

0.800

0.017

6.252

***

0.019

5.278

0.002

2.550

*

0.004

1.884

-1.913-0.001-0.6950.000-0.226-0.666

0.002

0.999

*

***

***

2.159
0.933

*

0.001

***

0.352
0.890

7

Note: “***”: p < 0.001; “**”: p < 0.01; “*”: p < 0.05.

8
9
10
11
12
13
14
15
16
17
18
19

HSR Effective Range
Results from both prefecture- and municipality-level suggested the insignificance of the regional/local
economic effect from the proximity to HSR stations. We considered the potential reason behind theseresults and found out that one of the common assumptions in this study is the effective range of a 5km
radius from HSR station. This assumption could be potentially wrong; one of the possible reasons to
explain the insignificant result likely comes from the spillover effects from the treatment group to control
group. Not only the region with HSR service, but benefit could also extend to the adjacent regions if
people in those regions are able to utilize the HSR service. Especially in Japan where the feeder systems
such as local trains and buses are well-established, users can transfer from the feeder system to the HSR
network seamlessly. Therefore, it might be difficult to distinguish the economic effect from the proximityof HSR station as a treatment and control group under DID framework as HSR effect may be observed in
the control group as well.

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4
5
6
7
8
9
10
11
12

Table 5 shows the DID estimates from municipality-level analysis with the assumption of different
treatment groups ranging from 5 km radius to 30 km radius. Most of the estimates shown in Table 5 do
not give a significant result. However, in the estimation of tax revenue per payer, we found a significant
result at 30 km radius in Kyushu region and at 10 km radius in Hokuriku region. These results could
support our assumption above that the effect of HSR service could extend to the adjacent regions as well
if the accessibility to HSR station is well established. They also obviously reflect the fact that HSR in
Kyushu region mainly runs through the plain area so the effective range of HSR service could be higher
up to 30 km. While in Hokuriku region, the accessibility to HSR station is limited by the wide range ofmountains so the effective range of HSR service could be limited at 10 km. Not only the geographicalfactor, but the feeder system to HSR stations may also play an important role to boost the spillover effectof HSR service although more analysis is needed to clarify the factors affecting the effective range of
HSR.

13

Table 5. Estimation results of DID effect with a different radiusY

TAX REVENUE
Kyushu (Model 3-2k)

RADIUS (KM)

Estimatet-statHokuriku (Model 3-2h)
sig

Estimatet-stat

5

0.000

0.016

-0.006-0.265

10

0.007

0.620

0.019

1.458

20

0.009

0.793

-0.016-1.425

30

0.024

1.829

-0.016-1.381Y.

sig

TAX REVENUE PER PAYER
Kyushu (Model 3-4k)

RADIUS (KM)

Hokuriku (Model 3-4h)

Estimatet-stat50.000-0.006

sig

10

0.010

0.896

0.033

2.206

20

0.013

1.096

-0.014-1.045

30

0.025

1.998

-0.018-1.312*Estimatet-stat-0.006-0.208sig*

14

Note: “***”: p < 0.001; “**”: p < 0.01; “*”: p < 0.05; “.” p < 0.1.

15

CONCLUSION

16
17
18

This study investigated empirically the effect from the proximity to HSR stations to the regional/local
economies, using the case of Japan’s HSR development during 1981-2006 and 2010-2015. The results of
empirical analyses from Japanese HSR are summarized as:

19
20
21
22
23
24
25
26
27
28

(1) In prefecture level, the treatment effect on production (GRP) is significantly positive but the DID
effect is insignificant;(2) In prefecture level, a significant difference between treatment group and control group 1 (NoHSR) is suggested from ATT estimation;(3) In municipality level, the treatment effect on production (tax revenue) is significantly positive but
the DID effect is insignificant, and both the treatment effect and the DID effect on productivity(tax revenue per taxpayer) are insignificant.
(4) In municipality level, ATT estimations are not significant for both estimation of production andproductivity(5) Depends on the region, DID effect is found positively significance in various effective ranges.

1
2
3
4
5
6
7
8
9
10
11

The insignificant DID effect and ATT estimate can be interpreted that, after the new introduction of HSR,
on average, there is no significant economic boost in the regions where the HSR stations were introduced,
compared to the economic performance in the regions where HSR stations were not introduced andcompared to those where HSR stations have been already in service. Furthermore, this concurs with theresults shown by Jia et al. (23), which reported an insignificant DID effect from the case study of HSR
network in China. This result is also supported by the insignificant result from PSM model analysis aswell. However, in the effective range analysis, significant results are found at a different range in eachregion. The significant result at 30 km radius found in Kyushu region is also supported by the suggestion
from Bernard et al. (15). Therefore, based on the result from this study, it can be concluded that there is asignificant effect from the proximity to HSR stations to the local economy, but the proximity range could
vary upon the characteristics of such local economy.

12
13
14
15
16
17
18
19
20
21
22
23

Analysis under DID or PSM framework requires the clear distinction between the treatment group and thecontrol group. In our case, we distinguish the treatment group from the control group by the proximity of
new HSR stations under the implicit assumption that the local economy in one group is different from thatin the other group. However, it should be noted that our analysis results could not confirm whether the
introduction of HSR stations leads to regional economic development or not. For instance, if the effective
range of HSR service really differs across locations, then further study with deeper consideration of theaccessibility, geographical condition, and spatial interaction between economies should be further
elaborated. Additionally, the discussion regarding the causal relationship of indirect effects, such as theagglomeration effect from HSR (13), would be also valuable for understanding the effect of HSR.
Furthermore, it is also interesting to discuss the effect of HSR through HSR performance such as HSR
usage (16) or service frequency (23) for a deeper understanding of the mechanism between HSR service
and economic development.

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25

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