Discussion Paper No. 86
The accessibility to the nearest urban
metropolitan area and rural poverty in Japan
Takeru Sugasawa

May, 2018

Data Science and Service Research
Discussion Paper

Center for Data Science and Service Research
Graduate School of Economic and Management
Tohoku University27-1 Kawauchi, Aobaku
Sendai 980-8576, JAPANThe accessibility to the nearest urban metropolitan area and rural poverty in Japan
Takeru Sugasawa†

Abstract
This study examines the effects of accessibility to the nearest urban metropolitan area on rural poverty byusing Japanese municipality-level data. We conduct nationwide cross-sectional analyses and Ibaraki
prefecture’s, a suburban area of Tokyo, municipality-level panel analyses. we find that a larger time
distance to the nearest urban metropolitan area significantly increases regional poverty rates. The resultsof the panel analyses suggest that the effects of reducing rural poverty require 6–10 years to be observed.
Therefore, regional policy makers might need to consider that transportation investments that improve
inter-regional accessibility do not affect regional economic performance for several years.

JEL classification: R11 R12 R13 R41 R42

1. Introduction
Even in developed countries, poverty is still a serious problem, and improving income levels is crucial
for reducing poverty ILO (2016). As empirical evidence of the relationship between income level andpoverty, Förster and Mira (2005) examine the case of OECD countries in the second half of the 1990s andfind that their poverty rates and income levels strongly correlate. From those discussions, there is apossibility that factors affecting regional income levels also have impacts on regional poverty situations.
As an important factor that affects regional economic performance, we consider agglomeration
spillover effects. Marshall (1920) mentions the possibility that firms tend to concentrate to get advantages
such as rich labor markets, low transportation costs of inputs and outputs, and knowledge spillover.
Moreover, Rosenthal and Strange (2004) point out that firms receive the benefits of home market effects,
opportunity of urban consumption, and rent seeking in addition to the agglomeration advantages
suggested by Marshall. Those benefits of agglomeration increase the productivity of firms in urban
metropolitan areas, and the effects are known to spill over to surrounding regions.
The magnitude of agglomeration spillover effects is known to diminish by distance from urban
metropolitan areas. Jaffe et al. (1993) investigate the location of patent citations in the United States andfind that domestic patents tend to be cited in the states and standard metropolitan statistical areas of the†the Department of Economics and Management, Tohoku University (tel: 090-8478-3247, e-mail:takeru.sugasawa@gmail.com)

1

patents. They also suggest that the diffusing of domestic patents to surrounding areas occurs very slowly.
Moreover, Adams and Jaffe (1996) focus on the relationship between plant-level productivity and firmlevel research and development activities (R&D) and find that the positive effects of firms’ R&D on
plants’ productivity diminish with the distance between the plants and the research labs. The above
discussions suggest that positive agglomeration spillover effects are attenuated by distance, and there is apossibility that a larger distance from closely located urban metropolitan areas causes a lower regional
wage level and worsens regional poverty situations.
In this study, we estimate the effects of accessibility to the nearest urban metropolitan area onregional poverty by using Japanese municipality-level data. From our nationwide cross-sectional
analyses, we find that a larger time distance to the nearest urban metropolitan area significantly increases
regional poverty rates. The monetary magnitude is such that a one-minute increase of time distance to thenearest urban metropolitan area increases the number of households in poverty by about 0.78, and it
increases the annual expenditure of regional governments for public assistance by about 1.75 million yen
on average in 2014.
In addition, we focus on the case of a new commuting train that opened in 2005, the Tsukuba Express(TX), which connects Tokyo and suburban areas, and conduct panel analyses to understand the impacts ofchanging accessibility to closely located urban metropolitan areas on rural poverty. From our panel
analysis, we find that improvements in accessibility to the nearest urban metropolitan area significantly
decreases poverty rates, even controlling for municipality fixed effects. We also find that the effects ofreducing regional poverty are observed in municipalities located close to TX. The result is consistent with
our hypothesis that improvement in accessibility to the nearest urban metropolitan area will spread therange of positive spillover effects from urban metropolitan areas, stimulate their economic performance,and reduce poverty.
In a related study, Partridge and Rickman (2008) investigate the relationship between regional poverty
rates and distance from the nearest urban metropolitan areas by using county-level data from the UnitedStates, showing that larger linear distance from a nearby urban metropolitan areas causes higher poverty
rates in counties. However, there are two issues that are not considered in their study. First, Partridge andRickman use linear distance as a distance variable, which cannot measure accurate inter-regional
accessibility1. To solve the problem, we adopt time distance, which can measure actual transportation

1

Boscoe, Henry, and Zdeb (2012) focus on the relationship between housing prices and distance to thenearest hospital in the case of the United States, the District of Colombia and Puerto Rico. They find thatlinear distance does not work appropriately as a measure of accessibility if there are geographic barriers
that prevent people from travaling.

2

costs as an inter-regional accessibility variable, as well as linear distance. From our estimations, a larger
time distance to the nearest urban metropolitan area causes higher regional poverty rates, while linear
distance to the nearest urban metropolitan area does not explain rural poverty. Second, Partridge andRickman (2008) only conduct a cross-sectional analysis, and their results may contain biases fromneglecting unobservable regional characteristics. Against this background, we conduct a panel analysis tounderstand the effects of changing transportation costs to closely located urban metropolitan areas onrural poverty rates, controlling for time invariant regional characteristics, and find that the opening of a
new commuting train reduces poverty in regions located close to the commuting train.
The structure of this paper is as follows. The next section describes the mechanism of the relationshipwhereby access to urban metropolitan areas affects rural poverty, in reference to previous studies. Section
3 explains our estimation models and variables. Section 4 describes the results of the cross-sectional
analysis. Section 5 describes the configuration of the panel analysis and its results. Section 6 is theconclusion.

2. Background mechanism
This section describes the mechanism whereby regional accessibility to closely located urban
metropolitan areas affects rural poverty, referring to previous studies. The seriousness of the poverty
situation in a region depends on the regional wage and employee level, which is determined as anequilibrium of regional labor demand and labor supply. Partridge and Rickman (2008) formulate therelationship whereby regional employment and wage rates affect regional poverty, which can be written
as the following function.
,-.

Poverty( = f(𝒑𝒐𝒗/𝑒𝑟( , 𝑤𝑟( , 𝒐𝒕𝒉𝒆𝒓𝒊

(1)

<,
𝒑𝒐𝒗

where 𝑒𝑟( is the employment rate of region i and 𝑤𝑟( is its wage rate. 𝒐𝒕𝒉𝒆𝒓𝒊is the vector of othervariables that affect the poverty situation of region i. To understand how Poverty( exerts influence, we
consider how 𝑒𝑟( and 𝑤𝑟( exert influence.
The employee and wage rates of a region depend on the interaction between regional labor demandand labor supply. The relationships are written as the following functions.
𝑒𝑟( = f@AB /𝑙 D( , 𝑙 (E <,(2)𝑤𝑟( = f@GB /𝑙 D( , 𝑙 (E <,(3)3

where 𝑙 D( is the labor demand of region i, and 𝑙 (E is the labor supply. If other factors are given, an
increase of 𝑙 D( increases 𝑒𝑟( or 𝑤𝑟( , and an increase of 𝑙 (E decreases 𝑒𝑟( or 𝑤𝑟( . Then, we consider
factors that determine the level of regional labor demand and labor supply.
On the labor demand side, agglomeration economy spillovers and increases in the labor demand of thesurrounding regions occur, and the spillover effects are known to diminish by distance. Rosenthal and
Strange (2003 and 2006) focus on six kinds of industries’ agglomeration and the ranges of their spillover
effects in the United States. Their results suggest that the amount of employment rapidly diminishes by
distance from agglomeration in five out of six industries. In addition, Audretsch et al. (2005) focus on
knowledge spillover effects from universities on new establishments of high technology-based firms in
the United States. They find that high technology-based firms tend to locate close to universities, andthose firms are strongly concentrated within 50 kilometers of universities. The above studies suggest thatthe amount of regional labor demand decreases by distance from economic agglomeration.
On the labor supply side, rural workers are known to have difficultly accessing urban labor markets
across regions. Lucas (2001) introduces evidence suggesting that rural workers stay in their own areas inspite of higher income levels in urban metropolitan areas. Molho (1995) provides one of the evidencesreferenced in Lucas (2001), suggesting that rural workers tend to stay in rural areas because of their
attachment to the culture or human relations in areas in which they live. In addition, Lucas (2001)
identifies information costs about urban labor markets, which become larger by distance from urban
metropolitan areas, as a reason that rural workers stay in the areas in which they live.
The above discussions suggest that whereas labor demand concentrates in urban metropolitan areas
and the amount rapidly diminishes by distance from economic agglomeration, rural workers’ mobility is
at a low level and they have difficulty migrating to urban metropolitan areas across regions in pursuit ofhigher wages. This causes the amount of labor demand to diminish by distance from economic
agglomeration with a larger slope than that of the labor supply. We can express the relationships as thefollowing functions:VW Y
X𝑙 D( = f IJ /𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒(RS , 𝒐𝒕𝒉𝒆𝒓𝒍𝒅𝒊 <,VZ(E[\]^_X`a
VW fX𝑙 (E = f Id /𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒(RS , 𝒐𝒕𝒉𝒆𝒓𝒍𝒔
𝒊 <,
VW Y
X

VZ(E[\]^_X`agZ(E[\]^_X`a

< 0,

(4)

< 0,

(5)

VW f

X
< gZ(E[\]^_
`a ,
X

(6)

where 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒(RS is the distance to the nearest urban metropolitan area of region i. From functions (2)and (3), increases of 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒(RS decrease regional wages or employees level through changes of 𝑙 D(

4and 𝑙 (E , and from function (1), the increases of 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒(RS cause a worsening of the regional poverty
situation. The relationship can be written as the following function:ijk

poverty( = f@

(𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒(RS , 𝒐𝒕𝒉𝒆𝒓𝒊),VijkABlmn

VZ(E[\]^_X`a

> 0,

(7)

where poverty( is the poverty rate of region i.
From the above discussion, we assume that better accessibility to closely located economic
agglomeration improve the regional poverty situation. We examine the impact of distance to the nearest
urban metropolitan area on rural poverty rates with Japanese municipality level data.

3. Cross-sectional analysis
3.1. Empirical strategy
Based on the theoretical background, this section describes the estimation model that explains thelevel of municipalities’ poverty rates. The estimation model is as follows:
Pov( = 𝛼( + βDistance( + δ𝐗 𝒊 + θPrefecture( + u( .

(8)

Pov( is municipality i’s poverty rate. Distance( is municipality i’s distance to the nearest urban
metropolitan area. According to the above discussion, we expect that β is negative.
𝐗 𝒊 is the vector that contains variables relating to municipality i’s poverty rate. It contains three typesof variables, explaining municipality i’s population structure, economic activity, and education level.
Prefecture( is a prefecture dummy in which municipality i is contained. We adopt it to control forthe heterogeneity that comes from the prefecture of municipality i. u( is the error term.

3.2. Data
This section describes the data we use. First, we clarify the definition of municipalities and urban
metropolitan areas.
The Japanese government defines a municipality as the smallest unit of administrative districts, and it
is composed of cities, towns, villages, and specified districts2. We define urban metropolitan areas asmunicipalities with populations of over 300,000; that is a condition of the core city, which is a legal urban
metropolitan area determined by article 252 of the Local Autonomy Law3. In Japan, there were 71 urban

2

In 2012, there were 1747 municipalities composed of 786 cities, 754 towns, 184 villages and 23
specified districts in the Tokyo metropolitan area.
3
In 2016, the definition of core city changed to cities with a population over 200,000. However, we
adopt the previous definition used in 2012, in which our sample is observed.

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metropolitan areas in 2012. In addition, we regard 23 specified districts in the Tokyo metropolitan area as
one urban metropolitan area. In this study, each municipality has a nearest urban metropolitan area. The
nearest urban metropolitan area of a municipality is defined as the urban metropolitan area that has theclosest linear distance to the rural municipality.
To conduct cross-sectional analysis, we except municipalities that are urban metropolitan areas
themselves and use only rural municipalities with populations under 300,000 as our sample. The reason
for this is that we cannot observe the distribution of poverty in each municipality and cannot calculate thedistance between poor households in urban areas and the nearest urban metropolitan area. That renders us
unable to estimate the effects of distance to the urban metropolitan area located in the municipality on
regional poverty rates.
Distance( is municipality i’s distance to the nearest urban metropolitan area. In our cross-sectional
analysis, we adopt linear distance, time distance by car, and time distance by public transport asaccessibility variables to the nearest urban metropolitan area.
Linear distance is measured as the linear distance by kilometer between the government offices of arural municipality and the municipality’s nearest urban metropolitan area. We calculate the linear distance
between municipalities using a location-based service and coordinate conversion service from theGeospatial Information Authority of Japan.
Time distance by car measures how long people would spend to travel between two government
offices by car. This variable is calculated using Google Maps. Time distance by public transport is thetime people spend to travel between the municipality in which they live and its nearest urban metropolitan
area by train and bus4. We calculate this variable using the Timetable published by the Japan Travel
Bureau (JTB), which is a representative time table of public transportation in Japan. In the case ofmunicipalities that contain stations, we measure the time distance between their representative station,
which is defined in the Timetables, and the representative station of its nearest urban metropolitan area. In
the case of municipalities that do not contain stations in them, we add the time distance between theirgovernments office’s nearest bus stop and its nearest station to the time distance from the rural station to
the representative station of its nearest urban metropolitan area. We calculate the optimal path between arural municipality and its nearest urban metropolitan area for commuters5. The unit of time distance is
one minute.
4

A municipality’s principal station is defined by each government of the municipality.

5

The optimal path is calculated as the fastest path from a rural station to the representative station of thenearest urban metropolitan area, but we except limited express trains and bullet trains, which require
additional fare as commuting methods.

6

As the measure of regional poverty rates, we adopt a municipality’s share of households receiving
public assistance. This is defined as the ratio of households receiving public assistance to 100 households
in each municipality. The requirement for receiving public assistance is that a household lives under thepoverty line, which is determined by the standards created by the Ministry of Health, Labour and Welfare(MHLW). Each municipality’s income threshold for providing public assistance controls for eachmunicipality’s price level determined by MHLW. We assume that municipalities’ share of households
receiving public assistance work as a proxy for the absolute poverty rate, which is defined as “theinability to meet basic needs of health and nutrition” Deaton (2004). To calculate the public assistance
rate of each municipality, we use the Prefectural Statistic Manuals (2012).
Partridge and Rickman (2008) use the poverty standard that is defined by the U.S. Census Bureau.
The standard aims to identify whether a household is in an absolute poverty situation, and it controls forstates’ price levels and the number of household members; it has similar requirements to those of theJapanese income threshold for receiving public assistance. The similarity between the two poverty
standards is convenient for us to compare our estimation results with those of Partridge and Rickman(2008).
Using 𝐗 𝒊, we control for factors relating to municipality i’s poverty rate. It contains three types ofvariables. (I) The variables that explain municipality i’s population structure are the number ofhouseholds and age structure (the share of population under 15 and the share of population over 65). (Ⅱ)
The variables about municipality i’s economic performance are industrial structure (share of laborers in
the primary sector and in the manufacture sector) and municipality i’s unemployment rate. (Ⅲ) The
variables reflecting municipalities’ education level are the share of people who graduate from a university
and the share of high school graduates. To obtain those variables, we use the Statistical Observations ofPrefectures, constructed by the Ministry of Internal Affairs and Communications Statistics Bureau (2010).

7

We use 33 prefectures from the total of 47, which announce the share of households receiving public
6
assistance at the municipality level . Table 1 displays the summary statistics.

4. Cross-sectional results
This section describes the results of our cross-sectional estimation. Table 2 shows the estimation
results.
Column (1) uses linear distance as Distance( , and its coefficient is positive and insignificant. This result
suggests that linear distance to the nearest urban metropolitan area does not have significant effects onrural poverty rates. Column (2) adopts time distance by car as Distance(. The coefficient is positive witha 10% statistical significance. The magnitude is that one-minute increases of time distance by car to thenearest urban metropolitan area increase the rural poverty rates by about 0.003%. Column (3) shows the6

The 33 available prefectures are Hokkaido, Iwate, Fukushima, Ibaraki, Tochigi, Saitama, Chiba,
Tokyo, Kanagawa, Toyama, Fukui, Gifu, Aichi, Shiga, Kyoto, Osaka, Hyogo, Nara, Wakayama, Tottori,
Shimane, Hiroshima, Yamaguchi, Kagawa, Kochi, Fukuoka, Saga, Nagasaki, Kumamoto, Oita, Miyazaki,
Kagoshima, and Okinawa.

8

estimation of time distance by public transport, and its coefficient is positive with a five percent statisticalsignificance. This result shows basically the same result as estimation (2). One minute increases of time
distance by public transports to the nearest urban metropolitan area increase rural poverty rates by about
0.002%.
Columns (1)-(3) are fundamentally consistent with the results of Partridge and Rickman (2008) insuggesting that a larger distance to the nearest urban metropolitan area will cause a worsening of the rural
poverty situation. However, our results also show that there is difference between the significance of
linear distance and time distance. From columns (1)-(3), we find that, whereas a municipality’s linear
distance to the nearest urban metropolitan area does not have significant effects on rural poverty rates,
time distance significantly affects rural poverty rates. These results suggest that, whereas linear distancedoes not precisely capture inter-regional accessibility in regions, such as Japan, which have many
geographical barriers, time distance could measure accessibility.
We consider the monetary impact of distance to the nearest urban metropolitan area on rural
governments. From column (3)’s result, the magnitude of a one-minute increase of time distance is about
a 0.002% increase of rural poverty rates. These results show that, for instance, an increase of 10 minutesof time distance to the nearest urban metropolitan area causes municipalities’ annual expenditures for9

public assistance to increase by about 17.5 million yen on average7.

There is a possibility that the scale of economic agglomeration affects the magnitude of spillover
effects on surrounding regions. In analyses (4)-(9), we divide municipalities by the scale of population of
their nearest urban metropolitan area. We define the threshold of municipalities as whether their nearest

7

In 2014, there were about 56.4 million households in Japan, and about 1.6 million households received
public assistance; thus, the share of households receiving public assistance was about 2.38%. There were1718 municipalities in 2014, and each municipality has about 931 households receiving public assistanceon average. The total expenditure of the Japanese government for public assistance was about 3843
billion yen, and, dividing the amount of total expenditure by the number of municipalities, amunicipality’s average expenditure for public assistance was about 2.2 billion yen. From the average
number of households receiving public assistance and the amount of the expenditure, the average amountof public assistance for each household was about 2.4 million yen. From the results of (2) and (3), an
increase of 10 minutes of time distance increases the share of public assistance by about 0.02; thus, it
adds about 7.82 households receiving public assistance. In this result, a municipality’s annual expenditure
for public assistance increases by about 17.5 million yen on average.

10

urban metropolitan area has a population over 500,000; this is one of the requirements to be an ordinancedesignated city, which is a representative system to define metropolises in Japan. Columns (4)-(6) present
the results of estimations (using the municipalities) of which nearest urban metropolitan area has apopulation larger than 500,000. Columns (7)-(9) use the municipalities that only include municipalities
with a population smaller than 500,000.
The results of (4)-(6) suggest that distance to the nearest urban metropolitan area does not havesignificant effects on rural poverty rates in municipalities whose nearest urban metropolitan area has apopulation larger than 500,000. From columns (7)-(9), we can observe qualitatively similar results to
those of columns (1)-(3) in that the coefficient of linear distance is positive and insignificant, and thecoefficients of time distance by car and by public transport are positive and statistically significant.
Column (7) uses linear distance as Distance( , and its coefficient is positive and insignificant. Column (8)adopts time distance by car as Distance( . The coefficient is positive with 10% statistical significance.
The result shows that one-minute increases of Distance( increase the rural poverty rates by about
0.002%. Column (9) provides the estimation with time distance by public transport, and its coefficient is
positive with 10% statistical significance. This result suggests that one-minute increases of Distance(
cause rural poverty rates to increase by about 0.002%. Columns (4)-(9) show that whereas distance to thesmaller urban metropolitan areas impacts rural poverty rates, accessibility to larger urban metropolitan
areas does not have significant effects.
We consider the reason that only municipalities closely located to a smaller urban metropolitan area
experience significant effects from distance to urban metropolitan areas. From columns (5) and (8), we
find that in municipalities close to a larger urban metropolitan area, the magnitude of the coefficient(about 0.002) is larger than that of municipalities close to a smaller urban metropolitan area (about0.0018). However, the coefficient for municipalities close to a larger urban metropolitan area has aconsiderably higher standard error (about 0.0012) than that of municipalities close to a smaller urban
metropolitan area (about 0.00075), and that causes the insignificance of time distance in column (5).
From columns (5), (6), (8), and (9), we can observe that municipalities close to larger urban metropolitan
areas consistently have a larger standard error of time distance to the nearest urban metropolitan areas
than municipalities close to smaller urban metropolitan areas. This suggests that there are larger
heterogeneities among municipalities close to larger urban metropolitan areas than among those close tosmaller urban metropolitan areas; for example, only some of the municipalities receive a large benefit
from good accessibility to the nearest urban metropolitan area, and the others do not receive it. We
consider that the reason for this is that large urban metropolitan areas resorb the economic activities ofsurrounding regions and generate regions with inactive economies, which is called the urban shadow

11effect. From this discussion, there is a possibility that only some of the municipalities located close tolarge urban metropolitan areas have their economic activity resorbed by the urban metropolitan areas,
which causes a larger dispersion among the surrounding municipalities of large urban metropolitan areas
than those of smaller urban metropolitan areas.
Although our estimation model includes all the possible variables to control for municipality
heterogeneity, there is a possibility that our model still misses unobservable characteristics cannot be
controlled by these variables’ and the results may include some biases. To control for unobservable timeinvariant heterogeneities of municipalities, we conduct a panel analysis and estimate the impact ofimproving accessibility to the nearest urban metropolitan area on rural poverty rates.

5. Panel analysis
5.1. The opening of the new Tsukuba Express
To control for unobservable and time invariant characteristics of municipalities, we focus on the caseof the opening of the new TX in 2005. TX is a commuting train connecting Tsukuba (a suburban city in
Ibaraki prefecture) to Akihabara (a terminal station locating in CBD in Tokyo). Opening TX shortened
the time distance between Tsukuba and Akihabara from 60 minutes to 40 minutes. The drastic change ofaccessibility to urban metropolitan areas might affect the geographic ranges of spillover effects from
economic agglomeration on the surrounding regions of the railroad. For instance, households’ job
selecting behavior or firms’ decision of location could reflect the impacts of changing accessibility insurrounding municipalities of the railway. Since the railway also contains Kashiwa (an urban
metropolitan area in Chiba) as its passing station, the opening of TX might have impacts that come fromthe change of accessibility to other smaller urban metropolitan areas as well as to Tokyo. Figure 1 shows
the routes of TX and the Joban line, which is an existing railway connecting municipalities in Ibaraki toTokyo. The route of TX is colored red, and the Joban line is colored blue. Each circle represents a stationof the railways.

125.2. Panel analysis
We create panel data and estimate the impacts of accessibility to urban metropolitan areas onmunicipalities’ poverty rates, controlling for the time invariant characteristics of municipalities. To create
the panel data, we use Timetable (JTB: 2000, 2005, 2010, 2015) and calculate the time distance among
stations using the appropriate commuting route. The estimation model is as follows:
Pov([ = α([ + βTimeDistance([ + δ𝐗 ([ + η( + ε([ .

(9)

13

η( is municipality fixed effects. ε([ is the error term, which comes from the time invariant characteristics
of municipality i.

In our panel analysis, only the time distance by public transport is used as distance variables. Linear
distance cannot identify changes of inter-regional accessibility. Additionally, we cannot observe previous
time distance by car.
Ibaraki prefecture is divided into five regions: Central, South, West, North and Rokko region8. TX
terminates at Tsukuba city, in the South region. Figure 2 shows the regions of Ibaraki prefecture and the8Each region is composed of municipalities as follows: Central (Mito, Kasama, and Omitama cities, andIbaraki, Oarai, and Shirosato towns), South (Tsuchiura, Ishioka, Ryugasaki, Toride, Ushiku, Tsukuba,
Inashiki, Kasumigaura, and Thukubamirai cities; Miho village; and Ami and Kawachi towns), West
(Koga, Yuki, Shimotsuma, Joso, Chikusei, Bando, and Sakuragawa cities, and Yachiyo town), North(Hitachi, Hitachiota, Takahagi, Kitaibaraki, Hitachinaka, Hitachiomiya, and Naka cities; and Tokai
village; and Daigo town), and Rokko region (Kashima, Itako, Kamisu, Namekata, and Hokota cities).

14

route of TX. Since there is a difference in proximity to the new commuting train among themunicipalities, the impact of opening TX might affect only municipalities that have better accessibility to
the new railway. To control for the characteristics of proximity to TX, we introduce a cross-term of
TimeDistance([ and an indicator that identifies whether a municipality is close to TX. The estimation
model is as follows:
Pov([ = α([ + βTimeDistance([ + γTimeDistance([ × ClosetoTX( + δ𝐗 ([ + η( + ε([ .

(10)We define the closer regions to TX as the West, South, and Rokko regions; if a municipality is includedin the regions, the indicator becomes one.

5.3. The results
Table 3 describes the results of our panel analysis. Columns (1)–(2) are the results of the analysis
using all of the sample (2000, 2005, 2010, and 2015). In column (1), the coefficient of TimeDistance([
is positive and insignificant. This result shows that time distance to the nearest urban metropolitan area
does not affect rural poverty rates. Then we include the interaction term between TimeDistance([ andClosetoTX( in our model, as well as TimeDistance([. In column (2), the coefficient of TimeDistance([
is negative and insignificant, and the coefficient of the cross term is positive with 10% significance. The
magnitude of the interaction term is that one-minute decreases of TimeDistance([ reduce rural poverty
rates by about 0.006%.
From columns (1) and (2), we find that accessibility to the nearest urban metropolitan area does nothave significant impacts on rural poverty rates overall in the Ibaraki prefecture. However, column (2)suggests that time distance to urban metropolitan areas has impacts on rural poverty in municipalities thatare closer to TX. From the results, we find that the opening of TX affected only municipalities that havebetter accessibility to TX. Although time distance to the nearest urban area also changed in the regionsthat are not close to TX, their poverty rates did not reflect the changes of accessibility. We consider that aslight change of accessibility to urban metropolitan areas cannot spread or strengthen agglomeration
spillover effects enough to cause the rural poverty situation to be improved, but that the opening of the
new TX caused a significant improvement of accessibility to urban metropolitan areas, which was enough15

to improve the poverty rates of areas peripheral to TX.

Table 4 shows the average time distance to the nearest urban metropolitan area before (2005) andafter the (2010) opening of TX. From the table, we find that whereas municipalities close to TX decrease
their time distance to the nearest urban metropolitan area by about 8.7 minutes on average, municipalities
far from TX decrease their time distance by only about 3.3 minutes. We consider that the difference ofmagnitude reflects the municipalities’ accessibility to TX, and municipalities close to TX might receive
the impact of opening TX more sensitively than municipalities far from TX.
In addition, the difference of impacts of changing accessibility to urban metropolitan areas might be
explained by the preference for commuting costs of people in poverty. Roberto (2008) empiricallyinvestigates the case of the United States and finds that the working poor have a larger burden ofcommuting costs than the national median in eight metropolitan cities. The study suggests the possibility
that people living in a poor situation have lower price elasticity of commuting costs than people not inpoverty. If improved commuting costs to the nearest urban metropolitan area are still too expensive forpeople in poverty, the municipalities’ poverty situations might not reflect the changing accessibility tourban areas. From this discussion, we consider that a slight change of accessibility to urban areas could
not affect rural poverty rates significantly.
There could be some lags between the opening of TX and the economic effects of the new commuting
train. Chandra and Thompson (2000) focus on the lag of firms’ relocation after improvements ofinterstate transportation in the United States, finding that the impacts of the new interstate highways on

16suburban regions’ labor demand do not appear significantly until several years later. Their results suggestthat improvement of accessibility to a closely located urban metropolitan area will spread ranges ofagglomeration spillover effects and increase labor demand in surrounding regions, but some lags occur
before the effects appear. Providing that the lags occur in cases of opening commuting trains, there is apossibility that an improvement in poverty rates will not be observed until several years after the TX
opening in surrounding areas of TX.
To observe the lags of effects appearing after the TX opening, we conduct panel analyses (3)–(6).
Columns (3)–(4) are the results of analyses using periods 2005 and 2010 as the sample. On the other
hand, columns (5)–(6) adopt years 2005 and 2015 as the sample. Columns (3) and (5) adopt time distanceto the nearest urban metropolitan area as the distance variable. In addition, columns (4) and (6) adopt thecross term between ClosetoTX( and TimeDistance([, as well as TimeDistance([.

In column (3), the coefficient of TimeDistance([ is negative and insignificant. In column (4), thecoefficient of TimeDistance([ is positive and insignificant, and one of the cross terms is negative andinsignificant. In column (5), the coefficient of TimeDistance([ is positive and insignificant. In column(6), the coefficient of TimeDistance([ is negative and insignificant, and one of the cross terms is
positive with five percent statistical significance.
Columns (3)–(4) show that time distance to urban metropolitan areas does not have significant effects
on rural poverty rates in 2005 and 2010. However, from columns (5) and (6), we find that one-minuteincreases of time distance to the nearest urban metropolitan area cause rural poverty rates to increaseabout 0.012% in municipalities close to TX. The results show that the economic effects of opening TX on
the peripheral regions did not appear until 6–10 later. We consider that our results are consistent with thefindings of Chandra and Thompson (2000), which suggest that the effects of improvement accessibility tourban metropolitan areas take some years to be observed.

6. Conclusion

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We investigate the relationship between municipalities’ accessibility to the nearest urban metropolitan
area and rural poverty rates by focusing on the case of Japanese municipalities. We find that whereas
increasing the time distance to the nearest urban metropolitan area increases rural poverty rates, linear
distance does not explain the situation of rural poverty. When focusing on the case with many
geographical barriers, there will be some difference between linear distance and time distance in
explaining the spillover effects of urban agglomeration on regional economic performance.
Moreover, we focus on the impacts of the opening of a commuting train, TX in 2005 on surrounding
municipalities’ poverty rates and conduct panel analysis to control for time invariant characteristics ofmunicipalities. Even when we control for regional time invariant characteristics, the causal effects ofaccess to the nearest urban metropolitan area on rural poverty rates are observed significantly inmunicipalities close to the new commuting train. This result suggests that slight changes of accessibility
to the nearest urban metropolitan area do not affect the regional poverty situation significantly.
From our estimations, one-minute increases of time distance to the nearest urban metropolitan area
increase rural poverty rates by about 0.002%. This implies that about 0.78 additional households begin toreceive public assistance and cause increases of the annual expenditure for public assistance of about
1.75 million yen on average. In addition, we find that the economic effects of opening TX on thesurrounding regions did not appear until 6–10 years later. This result suggests that when governments
invest in transportation infrastructures to improve their economic performance, they ought to expect thatthe effects will not appear until a few years later.
Our results show that economic agglomeration spillovers are effective in reducing the poverty ofsurrounding regions, and improving accessibility to closely located urban metropolitan areas enlarges themagnitude and ranges of the effects. Transportation investments that improve accessibility to urban
metropolitan areas may stimulate their own economic performance and reduce poverty.

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