Institute for Economic Studies, Keio University
Keio-IES Discussion Paper Series

Do Winners Win More from Transport Megaprojects?
Evidence from the Great Seto Bridge in Japan
Yoshifumi Konishi, Akari Ono
11 August, 2024
DP2024-018
https://ies.keio.ac.jp/en/publications/24108/

Institute for Economic Studies, Keio University
2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan
ies-office@adst.keio.ac.jp
11 August, 2024

* Revised DP2024-003

Do Winners Win More from Transport Megaprojects? Evidence from the Great Seto
Bridge in Japan
Yoshifumi Konishi, Akari Ono
Keio-IES DP2024-018
11 August, 2024
JEL Classification: O18, R4, R11, R12
Keywords: Market Access, Transportation Investment, Core-Periphery Model, Economic
Geography, Quantitative Spatial Model, Treatment Effect under Spatial Network
Abstract
Economists are increasingly concerned with the heterogeneous impacts of transportation
infrastructure investments on economic outcomes, particularly the phenomenon known
as the “Straw Effect”: Core cities that were already in economic prosperity may gain
more, and peripheral cities may lose, from transportation megaprojects. We empiricallyinvestigate whether such an effect manifests in the case of the Great Seto Bridges inJapan, a 70-billion-dollar project implemented in the 1980s-1990s as part of the“Building-a-New-Japan” initiative. We employ the recentered instrumental variableapproach in the difference-in-differences design, exploiting the sharp decline in transport
costs and its unexpected impacts on market access across cities as the exogenous
sources of variation. Contrary to the Straw Effect, we find that large peripheral cities gain
more than core cities from the megaproject, demonstrating that the distribution of
winners and losers from the megaproject depends on how the transport cost reductions
pass through in the existing network structures.
Yoshifumi Konishi
Department of Economics, Keio University
ykonishi@econ.keio.ac.jp
Akari Ono
Graduate School of Economics, Keio University
ono.akari@keio.jp
Acknowledgement: Our special thanks go to Sho Kuroda who provided valuablecomments and discussions throughout the development of this paper. We also thankJevan Cherniwchan, Shota Fujishima, Kentaro Fukumoto, Kozo Kiyota, Kentaro Nakajima,
Ryo Nakajima, Koichi Ushijima as well as participants at the Hitotsubashi Urban Economic
Seminar, the 14th Empirical Moral Science Workshop, and at the Japanese Economic
Association Annual Meetings in 2021 and 2022. The study was in part supported by thefinancial support from Japan Society for the Promotion of Science (JSPS) Grant-in-aid for
Scientific Research (21H00712), JSPS Doctoral Research Fellowship (23KJ1886), and Japan
Science and Technology (JST) SPRING Fellowship (JPMJSP2123).

Do Winners Win More from Transport Megaprojects?
Evidence from the Great Seto Bridge in Japan

Yoshifumi Konishi
Department of Economics
Keio University
andAkari Ono*
Graduate School of Economics
Keio University* Corresponding author: Ono Akari (email: akari.ono@keio.jp).

Acknowledgement: Our special thanks go to Sho Kuroda who provided valuable comments anddiscussions throughout the development of this paper. We also thank Jevan Cherniwchan, Shota
Fujishima, Kentaro Fukumoto, Kozo Kiyota, Kentaro Nakajima, Ryo Nakajima, Koichi Ushijima as
well as participants at the Hitotsubashi Urban Economic Seminar, the 14th Empirical Moral Science
Workshop, and at the Japanese Economic Association Annual Meetings in 2021 and 2022. The study
was in part supported by the financial support from Japan Society for the Promotion of Science (JSPS)
Grant-in-aid for Scientific Research (21H00712), JSPS Doctoral Research Fellowship (23KJ1886),
and Japan Science and Technology (JST) SPRING Fellowship (JPMJSP2123).

Abstract
Economists are increasingly concerned with the heterogeneous impacts of transportation
infrastructure investments on economic outcomes, particularly the phenomenon known
as the “Straw Effect”: Core cities that were already in economic prosperity may gain more,
and peripheral cities may lose, from transportation megaprojects. We empiricallyinvestigate whether such an effect manifests in the case of the Great Seto Bridges in Japan,
a 70-billion-dollar project implemented in the 1980s-1990s as part of the “Building-aNew-Japan” initiative. We employ the recentered instrumental variable approach in the
difference-in-differences design, exploiting the sharp decline in transport costs and its
unexpected impacts on market access across cities as the exogenous sources of variation.
Contrary to the Straw Effect, we find that large peripheral cities gain more than core cities
from the megaproject, demonstrating that the distribution of winners and losers from themegaproject depends on how the transport cost reductions pass through in the existing
network structures.

JEL Codes: O18, R4, R11, R12
Keywords: Market Access, Transportation Investment, Core-Periphery Model, Economic
Geography, Quantitative Spatial Model, Treatment Effect under Spatial Network

1

I. Introduction
Public transportation infrastructure is essential for economic growth and for the efficient andequitable distribution of goods and services. However, economists are increasingly concerned
about its heterogeneous impacts (Krugman, 1991; Krugman and Venables, 1995; Puga, 1999;
Fujita et al., 1999; Fujita et al., 2001; Fujita and Thisse, 2002; Baldwin et al., 2003; Ottaviano
and Thisse, 2004; Faber, 2014; Baum-Snow et al., 2020). “Core” cities may simply gain morefrom subsequent public transportation investments than “peripheral” ones. Or even worse, theperipheral cities may lose from such investments. This phenomenon is sometimes known as
the Straw Effect in the literature (e.g., Ono and Asano, 2005; Kim and Han, 2016; Zheng et al.,2020), making an analogy to a plastic straw drawing water (“economic resources”) away froma cup (“periphery”).
Indeed, the Straw-effect phenomenon has a strong foundation in economic theory. Since
Krugman (1991), economists have incorporated into the theory of trade and economic
geography various economic mechanisms such as agglomeration economies, endogenous labor
migration, and returns to scale, virtually all of which generally predict heterogeneous impactsof public transportation investments (Krugman, 1991; Krugman and Venables, 1995; Helpman,
1998; Ottaviano et al., 2002; Hanson, 2005). Therefore, the economic abundance some cities
enjoy today likely stems more from historical, political, or geographic advantages rather thandirect investment in public transportation. This line of argument can be easily adapted to thequantitative spatial model (QSM) to demonstrate that there are generally winners and losers
from any given transportation investment in a given network of cities under a variety of initialconditions.
This manuscript empirically investigates whether the Straw-effect phenomenon manifests inthe case of the Great Seto Bridges in Japan during the 1980-1990s, a period marked byaccelerated economic growth (see Figure1, Section II). During the period, the Japanese
government embarked on major highway constructions (often known as the “Building-a-NewJapan” initiative) in the western Japan region. The largest of such projects was the constructionof the Great Seto Bridges: three bridges that connect the isolated island region (Shikoku) to themain island of Japan (Honshu). The Bridges’ construction lasted 21 years from 1978 to 1999,
and its financial cost is estimated to be roughly 70 billion dollars, making it one of the mostexpensive transport megaprojects in Japan and worldwide (see Table 1, Section II).
There were concerns, even before the construction began, regarding the economic impactsof the Bridges on the surrounding regions. While the project’s intention was to promote the2economic development of peripheral cities, the opponents of the project expressed serious
concerns that the economic activities might simply be drawn from the peripheral region(Shikoku) to core cities in the main island such as Osaka and Fukuoka, that the peripheral
regions may simply end up with a large financial debt without much economic gain (Asahi
Newspaper, 1987; 1998), and that it would take at least 42 years for these regions to fully repay
the debt (Mainichi Newspaper, 2008).
Given this background, our primary objectives are, first, to quantify the heterogeneous causal
effects of this transport megaproject on peripheral cities’ economic outcomes (manufacturing
output, population, and employment), and second, to evaluate the distribution of winners andlosers relative to their initial conditions (do winners tend to win while losers lose more?). Doingso allows us to empirically evaluate the extent to which the Straw-effect phenomenon has (orhas not) manifested in our context and whether the geographic distribution of the economic
gains from the project matches that of the financial cost burdens. Addressing these questions ishighly policy-relevant, for a large sum of public investments is still being made intransportation infrastructures in low- and middle-income countries.2
Accomplishing these goals, however, is empirically quite challenging. There is a large bodyof economic literature attempting to estimate the causal effect of public transportation
investments on a variety of city-level economic variables. This line of literature generally
suggests three important empirical challenges. First, the location or timing of public
transportation projects is highly endogenous because such projects may target areas expected
to experience high or low economic growth. To address this empirical challenge, the earlier
literature typically relies on quasi-experimental variation or sharp discontinuities in investment
location or timing (e.g., Baum-Snow, 2007; Duranton and Turner, 2012; Faber, 2014;
Donaldson and Hornbeck, 2016; Asher and Novosad, 2020; Baum-Snow et al., 2020). Second,
the general equilibrium effects might “contaminate” credible quasi-experimental identification
strategies. 3 To address this problem, the literature often employs the market access (MA)approach, in which the quasi-experimental variation in transport costs is translated into thechanges in the MA variable, capturing all general equilibrium effects (Donaldson and Hornbeck,2 According to the World Bank (2022), $62.1 billion was spent on transportation projects worldwide in 2022 alone, accountingfor 68% of the total public investments.
3 The literature often discusses three types of general equilibrium effects. Cities, connected directly or indirectly throughtransportation network, may: (i) affect cities far-distant cities not directly impacted (“spillover effect”), (ii) have heterogeneous
impacts on cities based on pre-treatment economic and transportation conditions (“stock effect”), and (iii) have “feedback
effects” where the economic impact on one city reverberates through others and back to the original city.

3

2016; Jaworski and Kitchens, 2019; Mori and Takeda, 2019; Baum-Snow et al., 2020; Jedwab
and Storeygard, 2022). Third, even if public transportation projects are as good as randomly
assigned, either in location or in timing, the MA may still be endogenous because the resulting
change in the MA for a city depends on its intrinsic location in the structure of the given
economic network (Borusyak and Hull, 2023). To address this fundamental problem, Borusyak
and Hull (2023) proposes a recentered instrumental variable approach.
Our empirical strategy takes advantage of all these advances. First, we exploit the sharp
decline in transport costs, induced by the construction of the Central Seto Bridge and otherhighways during the 1980s-1990s, in the difference-in-differences design. Second, weconstruct the MA variable based on the QSM framework (Allen and Arkolakis, 2014; Redding
and Turner, 2015; Redding and Rossi-Hansberg, 2017; Redding, 2020), and use the differential
impacts of the Bridge and other highway construction on the MA levels across cities of different
economic prosperity as the exogenous source of variation. Third, we apply the “recenteredmarket access” (RMA) as our instrumental variable à la Borusyak and Hull (2023). Intuitively,
the RMA extracts the purely exogenous variation from the observed MA, by purging out the
expected market access (EMA), i.e., the endogenous component of MA that comes from its
geographic network characteristics. That is, some cities receive more transport projects andenjoy higher MAs precisely because they are located in geographically advantageous positionsin the transportation network. By construction, the RMA is neither affected by the geographic
location of the city nor by the non-random timing of the construction of the transportation
network and satisfies the exclusion restriction because it has no direct effect on the economic
outcomes. The RMA is also a relevant instrument for the observed MA because it is constructedfrom the MA variable.
We find that the OLS estimates are positive and statistically highly significant, confirming
the positive correlations between the changes in MA and the growth of cities, as measured in
manufacturing output, population, and employment. However, our IV estimates suggest thatsome of these correlations are only spurious. The estimated effects remain positive andstatistically significant only on manufacturing output, but turn insignificant on population andemployment. In the RMA regressions using the EMA as an additional control, we show thatthe population and employment sizes of cities respond more to the expectations of the MA
growth rather than to the shock components of the MA growth induced by the transport
megaproject. Hence, we conclude that the transport megaproject had a positive causal effecton the city-level manufacturing output, but less so on the population and employment levels.
These results are indeed consistent with economic theory and the earlier empirical literature.
4

Furthermore, the estimated impact is not economically small: Using the IV estimate at the meanof the data, the project-induced increase in MA during 1985-1995 leads to a 2.54% increase in
manufacturing output on average during the subsequent 10-year period.
Next, we make use of the IV estimates to empirically evaluate the geographic distribution of
winners and losers from the transport megaproject. We approach this question in two steps. The
first step is to calculate the predicted changes in the cities’ economic outcomes due to thetransport megaproject over the 1995-2005 period. The second step is to estimate the net benefitof the transport megaproject for each city relative to the counterfactual in its absence. For bothsteps, we evaluate the impacts of two transportation investment scenarios: (a) both the Central
Seto Bridge and the other highways and (b) the Central Seto Bridge only, against thecounterfactual without such investments. To simulate the outcomes under the second scenario,
we re-calculate the transport costs, removing the other highways, and make use of the QSM to
simulate the resulting changes in MA for all cities in our sample. We then use the IV estimatesto predict the economic outputs of the cities. To translate these into the net benefits, we makeuse of the publicly available information on the construction costs and the cost-sharing rules.
Our analysis reveals a number of important results, signifying the importance of accounting
for the general-equilibrium effects of transport megaprojects. First, a majority of cities gain net
benefits from the transport megaproject, regardless of their initial economic abundance orwhether they are directly connected by the project. Second, there is a sign of agglomeration
economies in the sense that initially larger cities tend to gain more from the project. Thistendency becomes moderate, however, once we account for the construction costs. Third, there
is substantial heterogeneity in the distribution of net benefits across cities. In fact, a nonnegligible number of cities are estimated to lose substantially from the transport investment
within each city-size cohort. Fourth, the Straw-effect phenomenon did not materialize, at least
not in the form originally claimed by the critics: Large peripheral cities in Shikoku andChugoku are estimated to gain substantial net benefits from the project. Lastly, our results
indicate that the pure effect of the Central Seto Bridge is not necessarily large and that muchof the effect of the transport megaproject comes from the combined effect of the other highways
and the Central Seto Bridge. In sum, our results provide evidence in support of the idea thatthe distribution of winners and losers from transport projects depends, in a complex way, on
how the transport cost reductions pass through in the existing network structures.
Our results also imply an important question for public finance economists: How should we
finance a transport megaproject when the general-equilibrium mechanism determines the
distribution of winners and losers from the project in somewhat unexpected way? Based on our5estimates, the Central Seto Bridge is estimated to generate an annual aggregate net benefit ofapproximately 34 billion yen for the western Japan region over the period of 1988-2005. Thismeans that in theory, we can re-design the cost-sharing rule in such a way that all cities gainfrom the project. However, the fact that a non-negligible number of cities are estimated to lose
from the project implies that the government’s current cost-sharing rule does not match theempirical distribution of economic benefits across cities. How to account for the empirical
distribution of benefits in designing the cost-sharing rules may become an important questionin the coming era of rapid demographic transition.
Our work closely complements the five strands of literature. To economize space, we only
touch on each, with more thorough discussions offered in Appendix A. First, it is closely
related to the studies that empirically investigate the impacts of transportation investments on
disadvantaged communities (Chandra and Thompson, 2000; Storeygard, 2016; Asher andNovosad, 2020; Jedwab and Storeygard, 2022). Second, there is also a rich literature that
evaluates the economic impacts of public transportation investments (Baum-Snow, 2007;
Duranton and Turner, 2011, 2012; Allen and Arkolakis, 2014; Duranton et al., 2014; Faber,2014; Ghani et al., 2016; Donaldson and Hornbeck, 2016; Jedwab and Moradi, 2016;
Storeygard, 2016; Donaldson, 2018; Jaworski and Kitchens, 2019; Mori and Takeda, 2019;
Asher and Novosad, 2020; Baum-Snow et al., 2020; Banerjee et al., 2020; Jedwab andStoreygard, 2022). Third, ours builds on a large number of empirical studies that use themarket-access approach (Davis and Weinstein, 2003; Hanson, 2005; Breinlich, 2006; Redding
and Sturm, 2008; Nakajima, 2008; Head and Mayer, 2011; Donaldson and Hornbeck, 2016;
Jaworski and Kitchens, 2019; Mori and Takeda, 2019; Jedwab and Storeygard, 2022). Fourth,ours is also related to the theoretical literature on quantitative spatial models (Redding andTurner, 2015; Redding and Rossi-Hansberg, 2017; Redding, 2020). Lastly, our paper has animportant implication for the public finance literature studying how the burden of large public
transportation investments should be shared among stakeholders (Anguera, 2006; Boardman
et al., 2018).
II. Background and Data
II-A. BackgroundOur study area covers the western Japan region, which consists of 23 prefectures with 614
municipalities. The western Japan is separated by the Seto Inland Sea into three major islands:
6the main island (called Honshu), the Shikoku Island, and the Kyushu Island. The region has
two well-known metropolitan areas: the Kinki area and the Kitakyushu area. The Kinki area
hosts three major cities: Kyoto, Osaka and Kobe. The Kitakyushu area hosts two major cities:
Kitakyushu and Fukuoka. These five cities are labeled “Core” cities (blue triangles) in Figure
1. There are also several large cities along the Seto Inland Sea, which serves as a waterway fortransporting large industrial materials such as petroleum and iron ore. These relatively large
regional cities are labeled “Large Peripheral Cities” (blue circles) in Figure 1.4
The highway construction began in the 1950s in Japan. By the early 1980s, major highways
connecting the core cities were completed, including an important connection between
Fukuoka, the largest city in the Kyushu Island, and the main island. Our study focuses on the
period of public transportation investments in the late 1980s-1990s that are designed to connect
peripheral cities to core cities. The purpose of these investments was to “disperse economic
and manufacturing activities”, which have been concentrated in core cities, to othersurrounding areas (Ministry of Land, Infrastructure, Transport and Tourism (MLIT), 1969).
The largest of these investments during this period was the construction of the Great Seto
Bridges, which are designed to connect the Shikoku Island to the main island. Historically, theShikoku region was separated from the main island by the Inland Sea. Before the constructionof the Bridges, commuting and goods transport were done by maritime routes. The Great Seto
Bridges consist of three bridges: the Central Seto Bridge, the East Seto Bridge, and the West
Seto Bridge. The Central Seto Bridge [labeled (c)], completed in 1988, connects Kagawa in the
Shikoku region to Okayama in the Chugoku region. The East Seto Bridge [labeled (d)], finishedin 1998, links Tokushima in the Shikoku region, the Awaji Island, and Hyogo in the Kinki
region. Lastly, the West Seto Bridge [labeled (b)], completed in 1999, joins Ehime in the
Shikoku region to Hiroshima in the Chugoku region.

4 These core cities had a population size of 500,000 or more prior to 1980. The large peripheral cities had apopulation size of 200,000 or more before 1980. This classification follows the official classification of large
cities in Japan.
7

Figure 1. Major Highway Routes and the Great Seto Bridges in Western Japan

Notes: The area surrounded by blue line is the Kinki region, green is the Chugoku region, red is the Shikoku
Island, and light brown is the Kyushu Island. The Kinki and the Chugoku regions lie on the Honshu Island.
The prefectures in red are those that directly bear the construction costs of the Great Seto Bridges. The thinblack lines represent- highways built before 1985, and the thick black lines represent highways built between
1985 and 1995. Four transport megaprojects are also labeled as (a) the Kanmon Bridge (opening in 1973),(b) the West Seto Bridge (opening in 1999), (c) the Central Seto Bridge (opening in 1988), and (d) the East
Seto Bridge (opening in 1998).

Figure 2 illustrates the estimated impact of the Central Seto Bridge on transport costs. We
calculate the transport cost for each city as the sum of all bilateral transport costs between thatcity and all other cities in the study area (see a more detailed explanation in Subsection II-B).5
The figure plots the changes in logged values of the transport costs between 1985 and 1995.
As shown, the largest reduction in transport cost occurred in the Shikoku region, and the next
largest was in the inner part of the main island, implying that the construction of the Central
Seto Bridge is critical in explaining the changes in transport costs during this period.

5 We use the term "transport costs" instead of "trade costs" as in Duranton and Turner (2012) and Redding(2020).
8

Figure 2. Transport Cost Reductions during the 1985-1995

Notes: This figure shows the changes in logged values of the transport costs from 1985 to 1995. As in Figure
1, the thin black lines represent highways built before 1985, and the thick black lines represent highways
built between 1985 and 1995. Transport cost for each city shown in this figure is the sum of all pair-wisetransport costs between the city and all other cities in the study area.

From its inception, the construction of the Bridges was highly contentious for several reasons.
First, it was one of the largest transport megaprojects in Japan and worldwide (see Table 1).
The total construction cost was estimated to be around 70 billion dollars, and the constructionof the three bridges took roughly 20 years. The cost per kilometer is the highest compared toother large-scale public investments in the world, and is approximately 5.8 times the cost of
the Channel Tunnel connecting the U.K. and France. Due to the huge financial burden, there
was a debate as to whether the project would be profitable and which prefectures should bear
the cost and how much (Asahi Newspaper, 1985; Nikkei Newspaper, 1985).

9

Table 1. Construction Costs of Transport Megaprojects

Source: Board of Audit of Japan, 1998; Veditz 1993; Müller and Baumann, 2016; Neuharth, 2006; Faber,2014; Bundesministerium für Verkehr, Bau und Stadtentwicklung, 2014; Mount, 2014; Hollingham, 2015.

Second, some critics expressed concerns about the Straw effect of the project, arguing that
economic activity in peripheral cities in the Shikoku region would be swallowed by the corecities such as Osaka (Ihara et al., 2020). Such a concern is well grounded in economic theory,and hence, not unrealistic. Since Krugman (1991), economists have long investigated how
cities grow. It is indeed easy to construct a numerical example in the stylized spatial model
where the core cities simply gain more, and the peripheral cities lose from a transportation
project. In this paper, we interpret the Straw effect slightly more broadly in a manner consistent
with the economic literature: Peripheral cities not just in Shikoku, but also along the inland sea
coast of the Chugoku region may lose economic prosperity upon the construction of the Great
Seto Bridges.
Lastly, the realization of economic gains from the project and its distribution over space areimportant from a public finance standpoint. Currently, eight prefectures unevenly bear theburden of the construction costs, and these prefectures have not been able to fully pay the debt
after 30 years of its construction. The cost shares of the eight prefectures are shown as shaded
areas in Figure 1, with darker red colors indicating higher cost shares. Comparing Figure 1
and Figure 2, we see that the geographic distribution of the cost shares does not match that of
the transport cost reductions. This highlights the importance of our question: Does cost share
match the distribution of economic gains?

10II-B. Data
Transport Cost and Market Access Variables: We define a bilateral transport cost between
any pair of cities as the sum of the travel time cost and the user fee between the cities. Prior tothe construction of the Bridges, transportation in our study area relied heavily on local roads
and ferries for the transport of goods and people. Hence, to calculate travel time costs, we use
transportation network data from the following sources: local road network data (ESRI Japan,2021), highway road data (MLIT, 2020), and ferry route data (MLIT, 2012). We adjust thetravel time on the road for road widths and road types (local vs. highway), accounting fordifferences in travel speeds. For this, we use the General Traffic Volume Survey Results of the
National Road and Street Traffic Situation Survey by MLIT (2015). We then multiply the travel
time by the official estimate of the time value of 3,060 JPY per hour (MLIT, 2008). For user
fees, we use toll rates by NEXCO West Japan and ferry fares for different ferry routes. To
construct the market access variable, we use the population size of each city following
Donaldson and Hornbeck (2016) and others. The city-level population data come from theStatistics Bureau of the Ministry of Internal Affairs and Communications (MIC).
Outcome Variables: Our outcome variables are manufacturing output, population, andemployment. We use these as our outcomes not only because they have been used in previous
studies (e.g., Duranton and Turner, 2012; Faber, 2014; Baum-Snow et al., 2020; Jedwab andStoreygard, 2022; Borusyak and Hull, 2023) but also because they are the primary indicators
when the critics express their concerns about the Straw effect. A city’s manufacturing output isobtained from the value of manufactured product shipment from the Industrial Statistics Survey(METI, 1980-2010). For population and employment, we use data from the MIC’s Statistics
Bureau. We use the labor force population as a proxy for the employment level of each city.
Although this may be an inaccurate measure of employment, it is considered a good proxy inthe literature because the residential choices of the labor force population highly correlate withthe employment demand across cities.
Control Variables: Our control variables include both the physical geographic
characteristics (“first nature”) and the sociodemographic characteristics (“second nature”)
aggregated at the city level as of 1980. These are known as the primary drivers of economic
growth (see Krugman (1993) and the quantitative spatial model in Appendix B). For thephysical characteristics, we follow Faber (2014), and use the average land steepness, theaverage elevation, the percentage of land areas suitable for building, the percentage of water
areas, and the percentage of wetland areas before 1980. All these variables use data from the11National Land Numerical Information Download Service (MLIT, 1981; 1976). For thesociodemographic characteristics, we use the percentage of elderly residents, the percentage of
employees in the manufacturing sector, and the taxable income per capita as of 1980 from the
Census Statistics (MIC, 1980).
II-C. Descriptive Statistics
We report the descriptive statistics on key variables used in our empirical analysis in Table
2. The first two columns in Table 2 report the means and standard deviations using the fullsample, whereas the remaining columns report these by city size as of 1980: small, large
peripheral, and core cities.
Panel A displays the changes in logged transport costs, market access (MA), and expectedmarket access (EMA) between 1985 and 1995 (see the next section on how the MA and EMA
variables are calculated). We see a substantial decline in transport costs, by about 47% on
average, and an associated increase in MA, by 13% on average. Importantly, the decline intransport costs is larger in smaller cities, whereas the increase in the MA is more pronounced
in larger cities. This weak correlation between the two variables signifies the importance of ourempirical approach.
Panel B reports the means and standard deviations of our outcome variables in 1995 and2005, the beginning and ending years of the ten-year period we use to evaluate the economic
impact. In 1995, our sample had the mean manufacturing output of approximately 176 billion
yen, the population size of roughly 76,900, and the labor force population of 37,900 per city.
Overall, cities in the western Japan region were on a declining trend during this period.
Interestingly, however, larger cities tend to lose manufacturing output while gaining population.
On the other hand, smaller cities tend to gain manufacturing output while losing population.
Panel C presents the means and standard deviations of our control variables as of 1980, thepre-treatment period. The table indicates that larger cities were typically located in morefavorable terrain, with suitable land for building and accessible water resources. These factors
are positively correlated with the changes in the MA variable, leading to bias in the estimates
if not controlled for. However, these control variables show little correlation with both changes
in transport costs and the outcome variables.

12

Table 2. Descriptive Statistics
The Size of Economy as of 1980
Small Periphery
Overall
Mean
S.D.
Panel A: Transport Cost and MA⊿ln Transport Cost⊿ln MA⊿EMA
Panel B: Outcome Variablesi) 1995
Manufacturing Output (Bn.)
Population (K)Employment (K)ii) 2005
Manufacturing Output (Bn.)
Population (K)Employment (K)Panel C: Control Variablesi) Geographical Conditions
Average Elevation
Average Land Steepness
Share of Water Area
Share of Wetland Area
Share of Building Area
ii) Socioeconomic Conditions
Income per capita (K)Share of Manuf. Labor
Share of Elderly

Lowest Tertile
Mean
S.D.

Second Tertile
Mean
S.D.

Highest Tertile Large Periphery
Mean
S.D.
Mean
S.D.

Core
Mean

S.D.
(0.01)(0.12)(0.12)

-0.47
0.13
0.10

(0.28)(0.23)(0.13)

-0.48
0.10
0.07

(0.31)(0.20)(0.13)

-0.46
0.12
0.10

(0.28)(0.20)(0.13)

-0.47
0.18
0.12

(0.26)(0.28)(0.13)

-0.48
0.19
0.10

(0.25)(0.25)(0.07)

-0.38
0.18
0.18

176
76.9
37.9

(441)
(179)
(125)

22.3
9.6
4.2

(45.7)
(5.0)
(2.4)

72.7
29.6
12.8

(108)
(10.3)
(4.8)

209
86.9
38.9

(204)
(41.2)(20.6)

939
418
198

(733)
(181)
(101)

3110 (2139)
1556 (607)1058 (800)

173
76.9
35.9

(397)
(182)
(111)

29.1
9.2
4.0

(77.8)
(5.3)
(2.6)

80.8
29.2
12.4

(150)
(12.4)
(5.5)

211
85.9
37.4

(242)
(42.9)(20.0)

910
421
189

(855)
(186)
(98.0)

2262 (1241)
1591 (598)960 (649)

238
12.8
0.10
0.010
0.22

(191)
(7.0)
(0.09)(0.05)(0.19)

317 (242)
15.4
(7.9)
0.08 (0.09)
0.008 (0.040)
0.15 (0.15)

226 (167)
12.8
(6.6)
0.09 (0.08)
0.007 (0.027)
0.20 (0.16)

196 (139)
11.3
(5.7)
0.11 (0.10)
0.017 (0.067)
0.26 (0.20)

125 (83.4)
8.3
(4.7)
0.13 (0.07)
0.009 (0.035)
0.41 (0.24)

158 (136)
8.8(6.3)
0.11 (0.07)
0.005 (0.008)
0.43 (0.24)

1812
0.08
0.12

(302)
(0.05)(0.03)

1683
0.07
0.14

1770
0.08
0.13

1904
0.09
0.11

2166
0.08
0.08

2192
0.09
0.09

Observations

614

(270)
(0.07)(0.03)

191

191

(222)
(0.05)(0.03)

190(323)(0.05)(0.03)

37(250)(0.04)(0.02)(136)(0.03)(0.01)

5

Notes: This table shows the means and standard deviations of the variables by city size. This classification
is defined based on the 1980 population and the urban hierarchy criteria in Japan. Core cities have
populations of 500,000 or more, large periphery cities have populations over 200,000 and below 500,000,
and small periphery cities have populations below 200,000. The small periphery cities are further subdivided
into three groups: the lowest tertile, the second tertile, and the highest tertile.

III. Empirical Strategy
III-A. The Estimation Equation
Our goal is to quantify the causal effect of the Great Seto Bridges on the economic growthof cities in the western Japan region, with a particular focus on its heterogeneous impacts:
which cities gain (or lose) more, and why? The essential problem in identifying the causal
effect is that virtually all cities are “treated” via the general equilibrium effects, not just the13cities directly connected by the Bridges. Hence, the usual quasi-experimental methods, such as
difference-in-differences or regression-discontinuity methods, are unlikely to work well in thiscontext. Instead, we start by the structural market-access equation that can be derived from thequantitative spatial model (Donaldson and Hornbeck, 2016; Redding, 2020; Jedwab andStoreygard, 2022; see the derivation in Appendix B):
ln 𝑌𝑖,𝑡 = 𝛽 ln 𝑀𝐴𝑖,𝑡 + 𝑊𝑖,𝑡 𝛾 + 𝜉𝑖 + 𝜂𝑖,𝑡 + 𝜖𝑖,𝑡 ,(1)

where 𝑌𝑖,𝑡 is a variable that measures the economic size of city 𝑖 in year 𝑡, 𝑊𝑖,𝑡 denotes a vector
of controls, 𝜉𝑖 is time-invariant unobservables, 𝜂𝑖,𝑡 is time-varying observables, and 𝜖𝑖,𝑡 is purei.i.d. errors. We follow Donaldson and Hornbeck (2016) and Jedwab and Storeygard (2022),
and define the MA variable, 𝑀𝐴𝑖,𝑡 for city 𝑖 at time 𝑡, by taking the first-order approximation
to the standard market-access equation [see the equation (B.11) in Appendix B]:−𝜃
𝑀𝐴𝑖,𝑡 ≈ ∑ 𝜏𝑖𝑗,𝑡𝑃𝑂𝑃𝑗,𝑡−1 ,

(2)

𝑗≠𝑖

where 𝑃𝑂𝑃𝑗,𝑡 is the city 𝑗’s population size in year 𝑡 and we set the value of trade elasticity to𝜃 = 4.0 following Simonovska and Waugh (2014).6 Note that in this expression, the value ofcity 𝑖’s own outcome 𝑌𝑖,𝑡 does not enter the value of 𝑀𝐴𝑖,𝑡 and we take a time delay between
transport cost and the size of the economy, i.e., 𝑃𝑂𝑃𝑗,𝑡−1 relative to 𝜏𝑖𝑗,𝑡 , to avoid endogeneity
problems coming from simultaneous changes in these. 7
To obtain the unbiased estimate of 𝛽, we combine three types of identification strategies.
Our first strategy is to apply the conventional wisdom from the panel-data methods. We takethe first-difference in equation (1) to eliminate the time-invariant unobservable term 𝜉𝑖 . We
also run the regression in lags: i.e., ∆(ln 𝑌𝑖,𝑡 ) are regressed on ∆(ln 𝑀𝐴𝑖,𝑡−1 ). This ensures that

6 We also report the results of our estimation using alternative trade elasticities in Appendix E.2. Intuitively,

𝜃 strikes a balance between transport costs and market size: smaller values assign more weight to transport
costs, while higher values assign more weight to the market size. The trade elasticity of 𝜃 = 4.0 appears to
hit the right balance, as discussed in both Simonovska and Waugh (2014), and Appendix E.2.
7 Following Borusyak and Hull (2023) and Baum-Snow et al. (2020), we also report our estimation results

using varying specifications of the MA variable in Appendix E.4.
14

the MA variable does not depend on the contemporaneous values of the outcome. In general,
the economic benefits of a transportation megaproject take quite some time to materialize. In
our case, the government documents report that the effects were observed several years after
the construction of the Bridges (MLIT, 2019; Honshu-Shikoku Bridge Expressway Company
Limited, 2023). Our base specification uses 10-year lags.8 We also include various controls toabsorb the remaining terms, ∆𝑊𝑖,𝑡 𝛾 and ∆𝜂𝑖,𝑡 .
Our second strategy is to exploit the quasi-experimental variation in transport costs inducedby the construction of the Central Seto Bridge. The construction of the Central Seto Bridge
dramatically reduced the transport costs for not only the connected cities but all cities (Figure2). This creates unexpected changes in the MA variable, particularly for cities far away fromthe Bridge. Furthermore, Figure 3-A reveals that the construction of the Central Seto Bridge(completed in 1988) caused a sharp decline in transport cost in cities in the Shikoku region in
the late 1980s, while the construction of the other two bridges in the late 1990s led to relatively
small transport cost reductions, with magnitudes similar across regions.
In Figure 3-B, we present the changes in MA (in log) relative to the values in 1985 as aresult of the reduction in transport costs. The gray shaded areas, ranging from lightest to darkest,
denote the 1st to 99th percentile, 5th to 95th percentile, and 25th to 75th percentile of allobservations, respectively. Lines with markers represent the median values for five
representative regions, and dots represent the values for individual cities. The figure delivers
several key messages. First, the correlation between the decline in transport costs and thegrowth in the MA variable is not uniform. In particular, cities in the Shikoku region experienced
both increases and decreases in the MA variable, even though the decrease in transport costs is
far more pronounced. Second, the transport cost reductions have persistent impacts on the MA
variable. We see a substantial increase in MA not only in 1990, following the sharp decline intransport costs in 1988, and again in 1995 in the Chugoku and the Shikoku regions. The MA
growth levels off after 2000, despite the transport cost declines in 2005. Third, the medianvalues indicate that core cities have a higher growth of the MA possibly due to their originaladvantages of geographic and economic networks. This suggests that pre-existing network
dynamics play a significant role in determining the impacts of transportation cost reductions.
8

Previous studies estimate this type of regression without a time lag (e.g., Donaldson and Hornbeck, 2016;

Jedwab and Storeygard, 2021; Borusyak and Hull, 2023). In Appendix E.1., we also present the estimation
results without the time lags as well as varying time lags, and discuss why the 10-year lag is our preferred
specification.
15

Figure 3. Transport Cost Reductions and Change in MA over Time

Notes: Panel A shows the transport cost reduction. Each line represents the median for each region, excluding
the core cities. Small dots represent the medians for the prefectures. Panel B shows the changes in MA (inlog) relative to the values in 1985. Gray shaded areas represent the 99-percentile range, 95-percentile range,
and 75-percentile range of all observations, in order from lightest to darkest color. Each line represents themedian for each region. Small dots are the raw values for cities.

Combining these identification strategies, we arrive at the following estimation equation:
16

∆(ln 𝑌𝑖 )95−05 = 𝛽∆(ln 𝑀𝐴𝑖 )85−95 + 𝑔(𝑋𝑖,80 ) + Δ𝜖𝑖,95−05 ,(3)where 𝑔(𝑋𝑖,80 ) is the term that controls for the influence of the observables ∆𝑊𝑖,𝑡 and theunobservables ∆𝜂𝑖,𝑡 . We use socioeconomic and geographic variables in 1980 as pre-treatment
controls. Note that in this specification, we use the difference-in-differences design in terms of
the MA variable, exploiting the transport cost shocks as the exogenous source of variation. In
a nutshell, this equation compares the outcome trends of cities that experience larger (orpositive) shocks in MA against those of cities with smaller (or negative) shocks, controlling forother economic factors.
III-B. The Recentered Market Access Approach
Economists are increasingly concerned with the fundamental endogeneity that remains evenafter taking all these identification strategies into account. That is, public infrastructure
investments are made in cities where higher (or lower) economic growth is expected, thereby
inducing the changes in MA according to the future expectations. In the earlier literature,
economists have used geography-based instruments such as planned route IV (e.g., BaumSnow, 2007), historical route IV (e.g., Duranton and Turner, 2012), and optimal least cost path
IV (Faber, 2014). However, Borusyak and Hull (2023) demonstrate that such geography-based
instruments fail to address this fundamental endogeneity because a city’s location in the
intrinsic structure of economic networks is inherently endogenous. As an alternative, Borusyak
and Hull proposes a recentered instrumental variable approach.
In the current setup, the approach “recenters” the MA variable, and is henceforth called a
recentered market access (RMA) approach. The approach proceeds in three steps. First, we
generate a sequence of 𝑆 random draws of public transportation investments, or equivalently,
draws of a vector of transport costs {𝝉𝑠 }𝑠∈𝑆 . Second, we calculate a sample analogue of the
expected value of MA (EMA) that follows from this sequence of random draws: 𝜇𝑖 ≡1

𝐸𝑠 [𝑀𝐴𝑖 |𝝉𝑠 ] ≈ #𝑆 ∑𝑠∈𝑆 𝑀𝐴𝑖 (𝝉𝑠 ) . Third, we “recenters” the MA variable by calculating thedifference between the observed and the expected MA for each realized vector of 𝝉 :𝑅𝑀𝐴𝑖 (𝝉) ≡ 𝑀𝐴𝑖 (𝝉) − 𝜇𝑖 .
By construction, these recentered MA changes must be correlated with the observed changes
in MA (relevance) and must be orthogonal to any non-random components that are related to17the changes in MA (exogeneity). Simply put, the approach essentially purges out the “pureshock”, which originates from the “as-good-as-random” assignment of public transportation
investments, from the observed changes in the market access level, which arise in a complex
manner from the non-random exposure of cities in a given network structure to the “as-goodas-random” assignment. In Appendix C, we provide a more detailed account of how the RMA
approach resolves the fundamental endogeneity problem in a stylized QSM model.
In the current empirical setup, we operationalize this RMA approach in the following manner.
We first randomly select the location and timing of transportation investments in the westernJapan, and calculate the corresponding changes in transport costs and the MA variable.9 We
then calculate the expected MA growth for each city as the average of the 10-year growth of
the simulated MAs in logs:∆𝐸𝑀𝐴𝑖,85−95 ≡ 𝐸[∆(ln 𝑀𝐴𝑖 )85−95 ] =1𝑠̃ 𝑖,95∑ [ln 𝑀𝐴− ln 𝑀𝐴𝑖,85 ] ,𝑆(4)𝑠∈{1,…,𝑆}𝑠̃ 𝑖,95where 𝑀𝐴is city 𝑖’s counterfactual MA (simulated for year 1995) from each random draw𝑠, and 𝑀𝐴𝑖,85 is city 𝑖’s observed MA before treatment in 1985. We then define the recenteredmarket access growth as the difference between the observed MA growth and the EMA growth:∆𝑅𝑀𝐴𝑖,85−95 ≡ ∆(ln 𝑀𝐴𝑖 )85−95 − ∆𝐸𝑀𝐴𝑖,85−95 .

(5)

From the way it is constructed, it is clear that ∆𝑅𝑀𝐴𝑖 is the exogenous component of theobserved variation in MA that is realized from the actual public infrastructure investments. We
can make use of this RMA growth as a valid instrument because it must be correlated with therealized MA growth (relevance) but uncorrelated with the unobserved error (exogeneity) byconstruction. On the latter, note that the RMA growth instrument has an intuitive appeal for
those who have concerns about bias arising from reverse causality or selection --- thegovernment agencies may decide when and where to make public transportation investments
anticipating the expected growth of cities in different areas, but ∆𝑅𝑀𝐴𝑖 purges out that
expected growth component from the MA variable. By the same argument, we may
9 In each draw, we randomly select a highway segment from the set of highway segments that were built between 1985 and2015 with a 50% probability. Due to high computational burden, we only repeat 100 times of random draws.

18

alternatively use ∆𝐸𝑀𝐴𝑖 as a control in the OLS regression so as to absorb the expected growthcomponent. Indeed, Borusyak and Hull (2023) shows that the OLS regression with EMA
growth as a control and the IV regression with RMA growth as an instrument generally lead tosimilar estimates.
III-C. The Instrument Validity
This section discusses the spatial and temporal distribution of both the MA growth and the
RMA growth in our study. Figure 4 shows the changes in logged values of MA (Panel A) andin the logged values of RMA (Panel B) over the period of 1985-1995.
First, the spatial distribution of changes in market access (Figure 4-A) does not necessarily
coincide with that of transport costs (Figure 2). As expected, we see high MA growth in cities
where highways were constructed (e.g., near the Central Seto Bridge). However, some cities(e.g., northeastern Kyushu) substantially increase their market access despite the relatively
small transport cost reductions there, whereas some other cities (e.g., in the southern Shikoku
region) decrease their market access despite their substantial transport cost reductions.
Furthermore, cities near Osaka experience negative MA growth despite the new constructionof highways there. This reinforces the importance of accounting for the general equilibrium
effects of public infrastructure investments.
Second, Figure 4-B indicates that the RMA growth is spatially highly correlated with theobserved MA growth, suggesting that it is indeed a relevant instrument. The figure also
illustrates that some of these changes in MA are unexpected, in the sense that the changes
exceed, or fall short of, the expected MA growth for some cities. For example, the northern
Shikoku and the southern Chugoku regions experience large positive changes in RMA,
implying that the MA growth due to the construction of the Central Seto Bridge is something
we do not expect a priori from the intrinsic structure of economic networks. On the other hand,the core cities, such as Kobe and Kyoto, receive a negative MA shock after accounting for the
expected growth. These large cities are expected to grow faster than others due to theirgeographic advantage, yet the realized MA growth for these cities fell short of that expectation.
We exploit this kind of unexpected (exogenous) variation in MA for identification of its
economic impact. We report the results of the first stage regression in Appendix D.

19

Figure 4. Spatial Variation in Observed and Recentered Market Access

Notes: Panel A shows the changes in logged values of MA over 1985-1995. Panel B shows the changes inlogged values of RMA over 1985-1995. As in Figure 1, the thin black lines represent highways built before
1985, and the thick black lines represent highways built between 1985 and 1995.

20

IV. Estimation Results
IV-A. Main Results
We present the results of the OLS regression of equation (3) as well as the IV regression
using the RMA approach in Table 3. The first column reports on the OLS estimates while thesecond and the third columns show the results of the RMA approach, using ∆𝐸𝑀𝐴 as anadditional control (the second column) and ∆𝑅𝑀𝐴 as an instrument for ∆ ln 𝑀𝐴 (the thirdcolumn). For each regression, the estimates on three outcomes are reported: (A) manufacturing
output, (B) population, and (C) employment. For all outcomes, we use 1995-2005 as theoutcome period. All regressions use the following city-level variables as of 1980 as controls:
the average land steepness, average elevation, percentage of building areas, percentage of water
areas, percentage of wetlands, percentage of elderly, percentage of employees in the
manufacturing sector, and the taxable income per capita. These are meant to capture theinfluence of city-level unobservables on the economic growth trends.
The OLS estimates confirm a positive correlation between the transport-cost-inducedchanges in MA during 1985-1995 and the observed changes in all three economic outcomes
during 1995-2005. The estimated impacts are 0.200, 0.032, and 0.035, respectively, formanufacturing output, population, and employment, and are statistically significant at theconventional levels. Using the mean values reported in Table 2, these estimates would have
implied 2.60%, 0.42%, and 0.46% increases in these outcomes.
Our RMA results suggest, however, that some of these impacts are spurious. The estimatesremain robust to the use of EMA or RMA only on manufacturing output: 0.194 (EMA-OLS)
and 0.195 (RMA-IV) and statistically significant. On population, the RMA estimates become
smaller than the OLS estimate and marginally significant. On employment, there is a sign that
the RMA estimates are imprecise: estimates are insignificant, flip signs, and have relatively
large standard errors.

21

Table 3. Estimation Results
OLS

EMA-OLS

RMA-IV

(1)

(2)

(3)

Panel A: Manuf. Output⊿ ln MA⊿ EMA
R-squared
Panel B: Population⊿ ln MA⊿ EMA
R-squared
Panel C: Employment⊿ ln MA⊿ EMA

0.200 *** 0.194 **0.195 ***
(0.069)(0.090)(0.074)0.017(0.230)
0.081

0.081

0.032 *** 0.013(0.009)(0.013)0.055 *
(0.028)
0.463

0.466

0.081
0.019 *
(0.010)0.462

0.035 ** -0.011
0.003(0.015)(0.021)(0.017)0.130 ***
(0.050)R-squared

0.393

0.402

0.389

First-Stage F-Stat.
Observations

614

614

209
614

Notes: Asterisks represent 1%, 5%, 10% significance levels. In parentheses are the cluster robust standard
errors clustered at the city level. All regressions use the following controls: average land steepness, average
elevation, percentage of building areas, percentage of water areas, percentage of wetlands, percentage ofelderly, percentage of manufacturing employees, manufacturing output and the taxable income per capita.

As discussed in Borusyak and Hull (2023), the sign and statistical significance of thecoefficient on the EMA variable tell us how we might interpret the results. For manufacturing
output, the EMA coefficient is positive but insignificant. This implies that the manufacturing
outputs of cities respond largely to the unexpected changes in MA and that the confounding
effect of the intrinsic expectation of the MA growth is negligible. In contrast, for populationand employment outcomes, the EMA coefficient is positive and significant, implying that the
population and employment sizes of cities respond to the intrinsic expectation of the MA
growth, and hence, its omission would lead to the omitted variable bias. Based on these results,22

we conclude that the transport megaproject (the Central Seto Bridge and other highways)
during 1985-1995 had a positive causal effect on the manufacturing outputs of the affected
cities, but had no or limited effect on their population and employment.
These results are economically intuitive and are also consistent with the earlier empiricalstudies. On one hand, the market access of a city measures how accessible the market demands
across cities are to the producers of the city. When there is a positive shock to the city’s market
access, the firms in the city can increase the value of goods and services they produce throughtrading without relocating their production. Hence, the first-order impacts of the shock arelikely to appear in manufacturing outputs. On the other hand, the market access also measures
how accessible the goods and services produced across cities are to the consumers of the city.
When there is a positive shock to the city’s market access, some of the consumers may be
drawn to the city. However, the consumers make their residential location decisions based ona longer-term employment prospect for the city. Hence, the population sizes of cities are likely
to depend more on the expectation of the future MA growth than on the temporary shocks to
the MA growth. The employment sizes of the cities depend on the firms’ production locations,which in turn rest on the comparative advantage of the cities and are known to be robust to
various shocks and generally take longer time to adjust (Baldwin and Okubo, 2005).
The earlier empirical literature provides evidence in support of these economic mechanisms.
For example, Ahlfeldt and Feddersen (2018) find an economically large causal effect of theGerman high-speed railways on surrounding countries’ GDPs and argue that the GDP growthis primarily driven by higher labor productivity rather than employment increase. Gibbons etal. (2019) examine the effects of road openings using the firm-level panel data in Britain andfind that road openings causally increase the number of small-scale manufacturing
establishments, but do not increase manufacturing employment in the short run. Mayer et al.
(2017) study the impact of an enterprise zoning program in France on firms’ location and labormarket outcomes and find that, although the program has a positive effect on the probability oflocating in the enterprise zone, larger establishments are much less likely to relocate thansmaller ones.
IV-B. Alternative Specifications
Our main empirical results are based on the specification in equation (3). The specification
relies on the following set of assumptions: a 10-year lag between the changes in the MA
variable and the outcome, the trade-elasticity parameter of 𝜃 = 4 , no heterogeneity in the23treatment effect 𝛽, and the first-order approximation of the MA using the lagged population
size. We run several alternative specifications to check the validity of our specification: (a)altering our outcome period to 1980-90, 1985-95, 1990-2000, and 2000-10, (b) using
alternative values of trade elasticity, (c) interacting the MA variable with dummies representing
various sources of heterogeneity, and (d) fixing the population size of each city at the pretreatment level instead of the lagged population size when calculating MA in equation (2).
When different outcome periods are used, the OLS estimate on the manufacturing output isstatistically insignificant during the placebo period (1980-90), negative but statisticallyinsignificant during the post-treatment periods (1985-95 and 1990-2000), and becomes positiveand significant only during 2000-2010 and the IV estimates only magnify these estimates
without changing signs. Hence, we conclude that using earlier outcome periods would only
pick out the spurious correlation: trade-cost reductions occurred in cities with low economic
growth. When smaller trade elasticities (𝜃 < 3) are used, the MA responds too sharply to thechanges in transport costs, with magnitudes that are hard to justify. With moderate tradeelasticities (𝜃 ∈ [3,5]), the MA responses as well as the estimates of 𝛽 are similar to our basespecification. To account for heterogeneity, we interact the MA variable with three types oftertile dummies: pre-treatment population size, pre-treatment MA level, and transport cost
reductions. We fail to reject the null of joint significance of the interaction terms in these
regressions. Hence, we prefer our parsimonious specification assuming the homogeneous
effect 𝛽. Lastly, we confirm that all of our estimates (both OLS and IV) are robust to using the
pre-treatment population size in the MA variable. All these results are reported with more indepth discussions in the Appendix E.
V. The Economic Impact of the Transport Megaproject and Its Spatial Distribution
We now turn to the main question of the paper: Do winners win more (losers gain less oreven lose) from the transport megaproject? We approach this question in two steps.
The first step is to calculate the predicted changes in the cities’ manufacturing outputs dueto the transport megaproject over the 1995-2005 period against their pre-treatment population
sizes as of 1980. This step tells us how the estimated impacts on cities’ economic outcomes are
distributed relative to their initial population sizes, and hence, gives us a quantitative answer
to the question: Do larger cities tend to grow more than smaller cities due to the transport
megaproject? The second step attempts to estimate the net benefit of the transport megaproject24

for each city relative to the counterfactual in its absence. To do so, we use the publicly available
construction cost estimates.
For both steps, we make use of the RMA-IV estimates from Table 3 and evaluate the impactsof two scenarios, (a) the Central Seto Bridge and other highways and (b) the Central Seto
Bridge, against the counterfactual with no such investment. To simulate the outcomes under
the second scenario, we re-calculate the transport costs, removing the other highways, andmake use of the QSM to simulate the resulting changes in MA for all cities in our sample.
V-A. Geographic Distribution of Economic Impacts of the Transport Megaproject
Figure 5 shows the results of the first step. Panel A plots the economic impacts of the Central
Seto Bridge and other highways on the growth rates of the manufacturing output over the period
1995-2005 against the 1980 population size. In the same manner, Panel B plots the impacts of
the Central Seto Bridge only. In the figures, the cities directly connected by the transport
projects are marked as diamonds while the cities not directly connected are indicated as dots.
The cities in Shikoku are marked in red and those in other regions are in blue. Appendix F also
provides the results showing the impacts on population and employment, which convey
essentially the same points we discuss below.
This figure illustrates five important take-away messages. First, Panel A shows that theCentral Seto Bridge and other highway construction during the 1985-95 period had positive
impacts on virtually all cities’ manufacturing output, and the magnitudes of the impacts are
economically large. On average, these cities gain a 2.78% manufacturing output growth rate
relative to no transport investment.
Second, there is a sign of agglomeration economy in the sense that larger cities tend to attract
more economic activity than smaller cities, though there is substantial heterogeneity withineach city-size cohort. At the median, the largest cities (the fifth category) gain manufacturing
output by 3.33% (Panel A) and 1.11% (Panel B), whereas the smallest cities (the first category)gain manufacturing output by 0.92% and 0.06%.
Third, there is substantial heterogeneity across cities in these economic impacts, with a nonnegligible number of cities experiencing negative output growth. Importantly, there are winners
and losers, regardless of the initial city size or whether they are directly connected by thetransport projects. We also see that the economic impacts of the Central Seto Bridge differ
substantially with and without other highways constructed during the same period. For example,
Okayama would have experienced a 20.1% increase in manufacturing output if the other25

Figure 5. Economic Impacts of the Transport Megaprojectacross Cities of Different Population Sizes

Notes: The size of economy on the horizontal axis is based on the population size as of 1980, following thesame classification as in Table 2. The cities directly connected by the transport projects are marked as
diamonds while the cities not directly connected are indicated as dots. The cities in Shikoku are marked in
red and those in other regions are in blue. The black lines indicate the median for each category.

highways were constructed (Panel A), but only a 0.66% increase if only the Central Seto Bridge
were constructed (Panel B).
Fourth, the magnitudes of the estimated impacts are much smaller in Panel B than in PanelA, implying that the pure effect of the Central Seto Bridge is not necessarily large and thatmuch of the effect in Panel A comes from the combined effect of the other highways and theCentral Seto Bridge. In fact, Osaka, the largest city in our sample, is predicted to experience
positive output growth if the other highways were constructed (Panel A), but negative growth
if only the Central Seto Bridge were constructed (Panel B). This also signifies the importanceof our approach in accounting for the general equilibrium impacts.
Lastly, the Straw effect did not materialize, at least not in the way the critics had anticipated
before the construction of the Bridge. The critics were particularly concerned that the economic
activity may be drawn from the large peripheral cities (such as Hiroshima, Okayama,
Matsuyama, and Takamatsu, that are of critical importance to the regional economy) to the corecities (such as Osaka, Kobe, and Fukuoka). On the contrary, however, these peripheral cities
26

are predicted to gain manufacturing growth of 0.58-1.05% due to the construction of theCentral Seto Bridge alone and 1.67-20.1% including other highways. There are cities in thesouthern Shikoku region that lose from the transport project, but the same is true for cities inother regions.
In sum, we conclude from these observations that the distribution of winners and losers fromthe megaproject depends, in a complex way, on how the transport cost reductions induced by
the project permeate through the existing network structures.
V-B. Geographic Distribution of Net Benefits of the Transport Megaproject
Our next step is to convert these counterfactual impacts into net benefits. To do so, we apply
the following simplifying accounting rules.
On the cost side, we make use of the publicly available information on the construction costsof the Central Seto Bridge and other highways (Asahi Newspaper, 1992; 1994) and their cost sharesby prefectures from MLIT (2001; 2011). The estimated cost of the Central Seto Bridge is about
1.13 trillion yen (in 1988 JPY) and that of highways constructed between 1985 and 1995 isabout 9.0 trillion yen (in 1988 JPY).10 Unfortunately, we do not have the cost burden at thecity level. However, the construction cost burden born by each prefecture is eventually
collected through taxes from each resident within the prefecture. Hence, for simplicity, we
assume the cost burden is shared in proportion to each city's population size as of 1985.
On the benefit side, we assume that the primary benefit of the project arises in the form of
the value of the goods and services produced within the city’s boundary that is purely
attributable to the project. Hence, we make direct use of the estimated impacts on cities’
manufacturing output and assume away other kinds of (mostly non-pecuniary) benefits. Thisis arguably a simplifying assumption. However, when discussing the benefits of this kind oftransport megaprojects, the city governor’s interest often lies in increasing the city’s tax base,for which the value of total economic output is a good measure.
There is one subtle issue, in making use of our regression/simulation results. On one hand,
the Central Seto Bridge has a useful life of 100 years, while the highways have a useful life of
70 years. Hence, ideally, we would like to do the cost-benefit analysis, estimating the benefit
flows over the entire life years of the project. On the other hand, our preferred estimates get at
10 We use the estimated cost per kilometer of highways (4.87 billion yen) and multiply it by the total lengthof highways constructed during the period in western Japan (1,807 km).
27

the counterfactual impacts of the Bridge and the highways over the period of 1995-2005 only.
Extending the estimated effect beyond this period is spurious, for reasons similar to why wechose this period as the impulse response window in our main regression. As a compromise,
we estimate the net benefit for the period of 1995-2005, assuming that construction costs will
be paid equally over the useful life and applying the social discount rate of 4% per year
following Circular-4 guidelines (which is the same social discount rate used in Japan).
To be precise, we calculate the net benefit of each transportation project for city 𝑖 during theperiod 1988 to 2005 as follows:
2005

1 𝑡−1988)
𝑁𝐵𝑖,88−05 = ∑ ([𝐵𝑖,𝑡 − 𝐶𝑖,𝑡 ]
1+𝜌

(10𝑎)

𝑡=1988(∆𝑌̂)/10𝐵𝑖,𝑡 ≅ { 𝑖,95−050̂𝐶𝑖,𝑡 ≅ 𝐶𝑖 /𝑇

for 𝑡 > 1995
for 𝑡 ≤ 1995
for ∀ 𝑡

(10𝑏)
(10𝑐)

where 𝜌 is the social discount rate, ∆𝑌̂𝑖,95−05 is the estimated impact of the transport project on
city 𝑖’s manufacturing output over the period of 1995-2005, 𝐶̂𝑖 is the estimated cost burden forcity 𝑖 in 1988 JPY, and 𝑇 is the project’s effective life years. In principle, this accounting
method should yield roughly the same result as that of applying the same benefit flow over theentire life of the project against the one-time construction cost.
Figure 6 displays the estimates of the net benefits per capita per year for all cities in our
sample against their population size as of 1980, in a manner analogous to Figure 5. There areseveral important takeaways from the figure. First, Figure 6 echoes essentially the same key
messages we observe from Figure 5: (1) a majority of cities gain net benefits from the transport
megaproject, regardless of their initial economic abundance, (2)there is a sign of

agglomeration economies, (3) there is substantial heterogeneity in the distribution of net
benefits across cities within each category, with some cities losing substantially from themegaproject, and (4) the Straw effect phenomenon did not materialize, at least not in the form
originally claimed by the critics, with large peripheral cities in Shikoku and Chugoku gaining
substantial net benefits from the project.

28

Figure 6. Distribution of Net Benefits Across Cities of Different Population Sizes

Notes: The size of economy on the horizontal axis is based on the population size as of 1980, following thesame classification as in Table 2. As in Figure 5, the cities directly connected by the transport projects aremarked as diamonds while the cities not directly connected are indicated as dots. The cities in Shikoku aremarked in red and those in other regions are in blue. The black lines indicate the median for each category.

Second, the figure demonstrates that the government’s current cost-sharing rule does notquite match the distribution of economic benefits across cities. Based on our back-of-envelope
calculation, the Central Seto Bridge is estimated to generate an annual aggregate net benefit ofapproximately 34 billion yen or 460,000 yen per capita for the western Japan region over the
1988-2005. Similarly, if the other highways are included, the transport megaproject is estimatedto generate an annual aggregate net benefit of approximately 149 billion yen or 1.33 million
yen per capita. This implies that in theory, we can re-design the cost-sharing rule in such a waythat all cities gain from the project. That is, the net gainers contribute more to the project while
compensating the net losers, potentially through taxes and transfers.
Lastly, some of the “winners” in Figure 5 turn to “net losers” in Figure 6 once the cost of
construction is accounted for. If only the Central Seto Bridge were constructed, 41 cities
become net losers despite that they are predicted to gain positive output growth due to theBridge. If the other highways were constructed in addition, then the number of such cities
increases to 217. This is suggestive of an important trade-off from a public finance standpoint:
the aggregate net benefit may be larger, the larger the transport investment is, but the need for29the redistribution of its economic benefits may also become larger. We also provide additional
maps in Appendix G to help visualize the geographic distribution of benefits and costs.
There are two caveats to our results. First, the benefits may be under-estimated because weonly used the estimated impact on manufacturing output over the 1995-2005 period. Arguably,
the Bridge may stimulate growth of cities over a much longer time horizon and may also bringother pecuniary and non-pecuniary benefits to the cities. On the other hand, the net benefits
may be over-estimated because we only consider the construction costs, ignoring other kindsof costs such as a potential increase in industrial or transport-related air pollution.
VI. Conclusion
We empirically examine the Straw effect phenomenon in the unique historical context of
rapid motorization in Japan: Do core cities gain more from large transportation investments
than peripheral cities do, or worse, the peripheral cities even lose from such investments? We
estimate the heterogeneous causal effect of public transportation projects, using theconstruction of the Central Seto Bridge and other highways during the 1980s as the source ofexogenous variation in transport costs and employing the recentered instrumental variable
method in the market-access approach. The estimates are then used to evaluate the empirical
distribution of winners and losers from the Bridge construction and to gauge the extent of theStraw effect.
Our results indicate that a majority of cities, not just large cities or cities nearby the Bridge,
experience positive growth in manufacturing output. The estimated growth rates are higher for
cities that were larger before the construction of the Bridge and other highways. This suggests
the existence of agglomeration economies and is indeed consistent with the economic theory,and in some sense, the Straw-effect hypothesis. We do not find, however, that the Straw effectoccurs in the original form claimed by some critics. Some of the large peripheral cities are
estimated to experience higher economic growth purely attributable to the Bridge construction
than core cities. Importantly, the pure effect of the Bridge alone and the combined effect of theBridge and highways yield quite different results, both qualitatively and quantitatively. In
particular, the geographic distribution of winners and losers is shown to depend, in a subtle
way, on how the cities are connected by the transport projects in the existing transportation
network. Thus, our work substantiates the importance of accounting for the complex generalequilibrium effects in evaluating public transportation projects.

30

Our work also suggests an important question for public finance economists. On one hand,
we show that the transport megaproject, either the Central Seto Bridge alone or combined withthe other highways, generates a positive aggregate net benefit for the western Japan region. On
the other hand, we also show that a large number of cities are estimated to lose from the project,in terms of both the manufacturing output and the net benefits. Importantly, these losers are
spatially heterogeneous and a priori are hard to predict before the construction of the bridge.
How to finance such transport megaprojects in an equitable manner is potentially an important
policy question. Due to space and data limitations, we do not explore this question further and
it is hence left for future research.

31

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37

Appendix
Appendix A.
The Related LiteratureThis appendix discusses the literature closely related to our study. First, it is related to thetheoretical and empirical literature concerning the heterogeneous impacts of transport projects
on economic activity, particularly on disadvantaged communities (Chandra and Thompson,
2000; Faber, 2014; Storeygard, 2016; Asher and Novosad, 2020; Baum-Snow et al., 2020;
Jedwab and Storeygard, 2022). Theoretically, since Krugman (1991), it has been shown thatlower trade costs may hurt peripheral cities while having a positive impact on core cities.
Empirically, Jedwab and Storeygard (2022) finds a larger positive impact of transport projects
on peripheral cities in Africa; Asher and Novosad (2020) find a limited effect of road
construction on population growth in India. In contrast, Faber (2014) and Baum-Snow et al.
(2020) show a larger positive impact of transportation construction on core cities.
Second, our paper is related to studies that empirically evaluate the causal effects oftransportation infrastructure on various economic outcomes (Baum-Snow, 2007; Duranton andTurner, 2012; Duranton et al., 2014; Faber, 2014; Ghani et al., 2015; Donaldson and Hornbeck,
2016; Jedwab and Moradi, 2016; Storeygard, 2016; Donaldson, 2018; Jaworski and Kitchens,
2019; Mori and Takeda, 2019; Asher and Novosad, 2020; Baum-Snow et al., 2020; Banerjee
et al., 2020; Jedwab and Storeygard, 2022). Baum-Snow (2007) investigates the effects of interstate highways on suburbanization in the U.S. Duranton et al. (2014) also show that inter-state
highways have a large effect on the weight of city exports in the U.S. Donaldson (2018)
investigates the effects of railroad construction on inter-city trade and market integration inIndia. Duranton and Turner (2012), Ghani et al. (2015), and Banerjee et al. (2020) also
empirically examine the impact on the economic growth of cities. These papers use continental
countries such as the U.S., China, and India as their study area, unlike in our paper.
Third, it relates to the literature using the market access (MA) approach (Davis and Weinstein,
2003; Hanson, 2005; Breinlich, 2006; Redding and Sturm, 2008; Nakajima, 2008; Head andMayer, 2011; Donaldson and Hornbeck, 2016; Jaworski and Kitchens, 2019; Mori and Takeda,
2019; Jedwab and Storeygard, 2022). For example, Redding and Sturm (2008) use an
exogenous change in the unification of East and West Germany, and Nakajima (2008) examines
the economic separation of Japan and Korea as an exogenous change to show the importanceof market access. Donaldson and Hornbeck (2016) and Jedwab and Storeygard (2022) analyze
38the impact of changes in market access through the transportation construction. Ours differsfrom these papers in that we address the fundamental endogeneity associated with the MA
variable by applying the instrumental variable proposed by Borusyak and Hull (2023).
Fourth, it is also related to the theoretical literature on quantitative spatial models (QSM)(Allen and Arkolakis, 2014; Redding and Turner, 2015; Redding and Rossi-Hansberg, 2017;
Redding, 2020). The QSM derives empirically tractable gravity equations not only for goods
but also for flows of population, allowing for the empirical analysis of general equilibrium
effects. Theoretical studies in new economic geography (e.g., Fujita et al., 1999; Fujita andThisse, 2002; Baldwin et al., 2003) show that the love of variety, increasing returns to scale,and transport costs lead to a heterogeneous geographic distribution of economic activity.
However, these theoretical models are complex and diverge from empirical estimates. The
QSM approaches address these empirical challenges. Redding and Turner (2015) show that thegeneral equilibrium effects of transportation infrastructure development on wages, population,
trade, and industry composition based on Krugman (1991). Conversely, Allen and Arkolakis(2014) bases their model on the Armington assumption of perfect competition withdifferentiated varieties, which differs from Krugman's assumptions of homogeneous tradable
goods and a monopolistically competitive market. See the extensive review of these studies
offered in Redding and Rossi-Hansberg (2017) and Redding (2020).
Finally, it also relates to the studies that investigate how the burden of large public
transportation investments should be shared among stakeholders (Anguera, 2006; Boardman
et al., 2018). To the best of our knowledge, few studies investigate the allocation of costs anddebt among stakeholders for public investments. Moreover, many of these studies examine theex-post evaluation of public megaprojects using engineering rather than economic methods.
For example, Anguera (2006) provides a cost-benefit evaluation of the construction of the
Channel Tunnel between the UK and France. However, the total financial burden in the UK
was greater than the estimated benefits, suggesting a net economic disadvantage from its
construction. Boardman et al. (2018) provide a case study of a cost-benefit analysis of a mining
development project in British Columbia, Canada, showing that it primarily benefits the
Canadian National Railway and the federal government.

39

Appendix B.
The Market Access in the Standard Quantitative Spatial Model
B.1. Quantitative Spatial Model
The Quantitative Spatial Model (QSM) offers a comprehensive framework forunderstanding the spatial dynamics of economic activities, considering both first-nature andsecond-nature geographical factors. First-nature geography includes physical characteristics
such as terrains and climatic conditions while second-nature geography contains human-madeelements like political and legal institutions and economic policies. These aspects play animportant role in shaping the spatial interaction of economic activities, influenced by both
agglomeration forces and dispersion forces, across cities. The QSM allows us to model how
such an interaction determines the spatial distribution of economic activities across locations.
In this section, we outline the essence of the standard QSM à la Redding and Turner (2015),
Redding and Rossi-Hansberg (2017) and Redding (2020).
In the model, an economy consists of locations, 𝑖, 𝑛 ∈ 𝑁, and all locations are connected bytransportation networks. Each location has 𝐿𝑖 workers (who are also consumers), and theoverall economy is endowed with 𝐿 workers: 𝐿 = ∑𝑖 𝐿𝑖 . These consumers have a "love ofvariety" preferences and consume both tradable and non-tradable goods. They are perfectly
geographically mobile. Producers produce tradable goods under monopolistic competition withincreasing-returns-to-scale technologies. Productivity, amenity, bilateral trade costs, andsupply of floor space (non-tradable goods) are given exogenously.
A representative consumer in city 𝑛 has a utility function:
𝐶𝑛 𝛼 𝐻𝑛 1−𝛼
(
) (
)
𝑈𝑛 =
𝐵𝑛 ,
𝛼
1−𝛼
𝜎

𝜎−1
𝜎−1
𝑀
where 𝐶𝑛 = [∑𝑖∈𝑁 ∫0 𝑖 𝑐𝑛𝑖 (𝜓) 𝜎 𝑑𝜓]and 𝐻𝑛 are,respectively, the amount of tradable

goods and of non-tradable goods consumed in 𝑛, with 𝜓 representing each variety, 𝐵𝑛 is city𝑛 ’s amenity (e.g., quality, safety), and 𝑀𝑖 is the number of variety produced by location 𝑖 .
Maximizing this utility subject to the budget constraint 𝑌𝑛 = 𝑃𝑛 𝐶𝑛 + 𝑄𝑛 𝐻𝑛 , we can derive thefollowing indirect utility function:

40

𝑉𝑛 =

𝐵𝑛 𝑌𝑛
,
𝑃𝑛𝛼 𝑄𝑛1−𝛼1(B. 1)1𝑀
1−𝜎where 𝑃𝑛 = [∑𝑖∈𝑁 ∫0 𝑖 𝑝𝑛𝑖 (𝜓)1−𝜎 𝑑𝜓]≡ 𝐶𝑀𝐴1−𝜎𝑛 is the price index for city 𝑛, which also

implicitly defines the market access for consumers (𝐶𝑀𝐴𝑛 ), 𝜎 is an elasticity of substitution,and 𝑄𝑛 is the price of non-tradable good.
Next, maximizing the consumer's partial utility, we can derive the consumer demand foreach tradable good:−𝜎𝑝𝑛𝑖 (𝜓)−𝜎 𝛼𝑣𝑛
𝑝𝑛𝑖 (𝜓)𝑐𝑛𝑖 (𝜓) =
=(
)
−𝜎
𝑃𝑛
𝑃𝑛
𝑃𝑛

𝐶𝑛 .

(B. 2)

Consumers increase (decrease) their consumption of variety 𝜓 when the relative price of thatvariety is low (higher). Using equation (B.2), we can obtain the optimal pricing rule by solving
the producer's profit maximization problem:
𝜎 𝑤𝑖 𝜏𝑛𝑖.
𝜎 − 1 𝐴𝑖𝑝𝑛𝑖 (𝜓̅) = 𝑝𝑛𝑖 =(B. 3)

We can then derive the equilibrium wage from the equilibrium condition for tradable goods11:
𝜎−11𝑤𝑖 = 𝜉𝐴𝑖 𝜎 (𝐹𝑀𝐴𝑖 )𝜎 ,(B. 4)

1−𝜎 𝜎−1
where 𝐹𝑀𝐴𝑖 ≡ ∑𝑛∈𝑁(𝑤𝑛 𝐿𝑛 )𝜏𝑛𝑖
𝑃𝑛
is a measure of market access for firms and 𝜉 is a−1

constant defined as 𝜉 ≡ 𝜎 −1 (𝜎 − 1)(𝐹(𝜎 − 1)) 𝜎 .
The equilibrium of QSM can be pinned down by solving for the three set of endogenous
variables: bilateral trade flows, population shares, and wages. First, the bilateral trade flows or

11 From equations (B.2) and (B.3), and the zero-profit condition with free entry and exit, the output of the tradable goods 𝑥

𝑖 in

supply location 𝑖 can be derived as 𝑥𝑖 = 𝐴𝑖 𝐹(𝜎 − 1). The location 𝑛’s demand for the tradable goods produced in location 𝑖
is 𝑥𝑛𝑖 = (𝜎 𝜏𝑛𝑖 𝑤𝑖 −𝜎 (𝛼𝑌𝑛 𝐿𝑛)
𝜎−1 𝐴𝑖)𝑃𝑛1−𝜎. Thus 𝑥𝑖 = ∑𝑛∈𝑁 𝑥𝑛𝑖 𝜏𝑛𝑖 .

41

gravity equations are given by the share of expenditures in location 𝑛 on goods exported fromlocation 𝑖:

Γ𝑛𝑖 =𝐿𝑖 (𝜏𝑛𝑖 𝑤𝑖 ⁄𝐴𝑖 )1−𝜎.
∑𝑘∈𝑁 𝐿𝑘 (𝜏𝑛𝑘 𝑤𝑘 ⁄𝐴𝑘 )1−𝜎

(B. 5)

Second, the population shares are obtained from the population mobility condition12 as follows:

𝜆𝑛 =

𝐿𝑛
=
𝐿

σ−1
𝛼
𝛼
1−𝛼 (Γ )−σ−1 σ(1−𝛼)−1[𝐴𝑛 𝐵𝑛 𝐻𝑛
]
𝑛𝑛

σ−1 .
𝛼 σ(1−𝛼)−1∑𝑘∈𝑁 [𝐴𝛼𝑘 𝐵𝑘 𝐻𝑘1−𝛼 (Γ𝑘𝑘 )−σ−1 ]Finally, the market clearing condition in the goods market gives us wages:

𝑤𝑖 𝜆𝑖 = ∑ Γ𝑛𝑖 𝑤𝑛 𝜆𝑛 .
𝑛∈𝑁

B.2. The Estimating Equation of Interest in the Market Access Approach
In this section, we outline how the two measures of market access, CMA and FMA, can beshown to yield a single measure of market access (MA) and how it is related to our empiricalregression equation of interest.
To show the first, we start by rewriting the FMA by using the relationship between the priceindex 𝑃𝑛 and 𝐶𝑀𝐴𝑛 :1−𝜎(𝐶𝑀𝐴𝑛 )−1 .
𝐹𝑀𝐴𝑖 = ∑(𝑤𝑛 𝐿𝑛 )𝜏𝑛𝑖𝑛∈𝑁

Since the price index 𝑃𝑛 can be rewritten in equilibrium,

12 The population mobility condition is derived using equations (B.1) to (B.5) as

42

𝐿𝑛 = [σ−1σ(1−𝛼)−1
𝛼
−σ−1
𝛼
1−𝛼
𝐴𝑛 𝐵𝑛 𝐻𝑛 (Γ𝑛𝑛 )𝛼
𝜎 𝛼 1 1−σ 1−𝛼 1−𝛼
̃
𝑉 𝛼(
) ( )
(
)
𝜎−1
𝐹𝜎
𝛼

]

.

𝐿𝑖
𝜎 𝜏𝑛𝑖 𝑤𝑖 1−𝜎(),
𝐹𝜎 𝜎 − 1 𝐴𝑖

𝑃𝑛1−𝜎 = 𝐶𝑀𝐴𝑛 = ∑𝑖∈𝑁(B. 6)

we can derive the trade flow from location 𝑛 to location 𝑖 as:
𝑋𝑛𝑖 = (𝑤𝑛 𝐿𝑛 ) × Γ𝑛𝑖=

𝐿𝑖𝜎 𝑤𝑖 1−𝜎 1−𝜎()
𝜏𝑛𝑖 (𝑤𝑛 𝐿𝑛 )(𝐶𝑀𝐴𝑛 )−1 .
𝐹𝜎 𝜎 − 1 𝐴𝑖

(B. 7)

In equilibrium, the labor income 𝑤𝑖 𝐿𝑖 is equal to the total expenditures on tradable goods,
and hence, the following relationship holds:

𝑤𝑖 𝐿𝑖 = ∑ 𝑋𝑛𝑖 .

(B. 8)𝑛∈𝑁

We can then derive the relationship between CMA and FMA by using equations (B.6), (B.7),
and (B.8):

𝐶𝑀𝐴𝑛 = ∑𝑖∈𝑁𝐿𝑖𝜎 𝜏𝑛𝑖 𝑤𝑖 1−𝜎1−𝜎(𝑤𝑖 𝐿𝑖 )𝐹𝑀𝐴−1()= ∑ 𝜏𝑛𝑖𝑖 .
𝐹𝜎 𝜎 − 1 𝐴𝑖

(B. 9)𝑖∈𝑁

Assuming the symmetric trade costs 𝜏𝑖𝑗 = 𝜏𝑗𝑖 , the eigenvector solving the equations (B.6)
and (B.9) give us that
𝐹𝑀𝐴𝑖 = 𝜌𝐶𝑀𝐴𝑖 ,(B. 10)where 𝜌 > 0 is a scalar.
This indicates that FMA and CMA are essentially identical, with the former simply scaledby 𝜌 . Therefore, we can unambiguously refer to the market access (MA) reflecting bothconcepts of market access, 𝑀𝐴𝑖 ≡ 𝐹𝑀𝐴𝑖 = 𝜌𝐶𝑀𝐴𝑖 (Donaldson and Hornbeck, 2016). Notethat by plugging (B.10) into (B.9), we can formally define MA as:−𝜃
𝑀𝐴𝑖 = 𝜌 ∑ 𝑌𝑛 𝜏𝑛𝑖
𝑀𝐴−1
𝑛 ,
𝑛∈𝑁

43

(B. 11)

where 𝑌𝑛 = 𝑤𝑛 𝐿𝑛 , 𝜃 = 𝜎 − 1 . The MA measures the market potential in each location byconsidering both the market opportunity for firms to sell goods and the availability of the goods
variety for consumers.
Second, using these equations, we derive a theoretical relationship that would provide thebasis for our regression equation. Using equations (B.8), (B.7), and (B.4) with 𝑀𝐴𝑖 ≡ 𝐹𝑀𝐴𝑖 ,the relationship between output 𝑌𝑖 and market access is given by:1𝐿𝑖𝜎𝜉 1−𝜎 𝜎−1𝜎𝜎()
𝑌𝑖 = ∑ 𝑋𝑛𝑖 =𝐴𝑖 𝑀𝐴𝑖 .
𝐹𝜎 𝜎 − 1𝑛∈𝑁Taking the log, we can obtain the following relationship between output and MA:1
𝜎−1ln 𝑌𝑖 = 𝜅1 + ln 𝑀𝐴𝑖 +ln 𝐴𝑖 + ln 𝐿𝑖 ,
𝜎
𝜎
1

𝜎𝜉

1−𝜎

where 𝜅1 = ln [𝐹𝜎 (𝜎−1)(B. 12)] is a constant.

Similarly, we derive the following relationship between population (or employment) andmarket access:ln 𝐿𝑖 = 𝜅2 +𝛼(2𝜎 − 1)𝛼 𝜎−11ln 𝑀𝐴𝑖 +ln 𝐴𝑖 +
ln 𝐵𝑖 + ln 𝐻𝑖 ,𝜎(𝜎 − 1)(1 − 𝛼)
1−𝛼 𝜎
1−𝛼
−𝛼

𝛼

(B. 13)1

where 𝜅2 = ln [𝛼 1−𝛼 (1 − 𝛼)𝜉 1−𝛼 𝑉̅ −1−𝛼 ] is a constant. To see this, using the condition that the1indirect utility must be equalized across cities as well as the relationships 𝑃𝑛 = 𝐶𝑀𝐴1−𝜎𝑛 ,𝑄𝑖 𝐻𝑛 = (1 − 𝛼)𝑌𝑖 𝐿𝑖 , 𝛼𝑌𝑖 𝐿𝑖 = 𝑤𝑖 𝐿𝑖 , we obtain:𝛼(2𝜎−1)
𝛼 𝜎−1
1
−𝛼
𝛼
1
𝜎(𝜎−1)(1−𝛼)
−
1−𝛼
𝜎
1−𝛼
𝐿𝑖 = 𝛼 1−𝛼 (1 − 𝛼)𝜉 1−𝛼 𝑉̅ 1−𝛼 × 𝐴𝑖
𝐵𝑖 𝐻𝑖 𝑀𝐴𝑖.

Taking the log of this equation, we can obtain (B.13).

44

Appendix C.
The Essence of the Empirical Problem and Its Solution
This appendix provides the essence of the empirical challenges and how our recentered market
access approach might overcome the challenges in the standard quantitative spatial model(QSM) discussed in Appendix B.
C.1. How Does the Empirical Problem Arise?
We now use a simplified version of the QSM with N = 5 to illustrate (i) how the empiricalchallenges emerge, (ii) how the MA approach overcomes the challenges, but only partially, and(iii) how the recentered MA might address them fully.
We impose a particular structure of a transportation network connecting these cities. All
cities are connected to three other cities, except city B, which is connected to all other cities.
We assume cities are connected to each other, with equal symmetric trade costs: 𝜏𝑖𝑗 = 10 for
all 𝑖, 𝑗. We also assume that cities A and C have a population size of 1,000 while the other cities
have a population size of 500. These initial distributions of trade costs and population sizes aremeant to capture the intrinsic structure of the network arising from the natural or social
geographic conditions that are purely exogenous. Cities are homogeneous in all other aspects.
Of course, we can consider thousands of different initial configurations that are equallyreasonable. The following explanations do not depend on the specific configuration we
consider here. As a thought experiment, we consider two types of public transportation
investments. The first is to construct a highway that would lower the trade cost between cities
A and B from 𝜏𝐴𝐵 = 10 to 𝜏𝐴𝐵 = 5. The second is to construct a highway that would lower the
trade cost between cities A and D from 𝜏𝐴𝐷 = 10 to 𝜏𝐴𝐷 = 5.
Figure C1 illustrates the changes in logged values of populations (top panels) and logged
values of MAs (bottom panels). These illustrative examples demonstrate the empiricalchallenges we wish to address in our empirical study. Note that by construction, the public
transportation investment is endogenously related to economic variables of the model.
However, in this example, we assume that certain empirical challenges are unlikely to be facedby researchers in a real empirical context (which we do address later), allowing us to focus onother kinds of challenges. In the difference-in-differences or similar research design, we
compare the outcome trends of the treated cities versus the “untreated” cities. In this setup, the45

“untreated” cities would be cities C, D, and E in Panel A (B, C, and E in Panel B). Hence, ourestimand and sample analogue of interest would be:

𝐸[𝑌𝑖 (1) − 𝑌𝑖 (0)|𝑖 ∈ 𝑁𝑇 ] =∑𝑖∈𝑁𝑇 ∆𝑌𝑖
𝑁𝑇−∑𝑖∈𝑁𝑈 ∆𝑌𝑖
𝑁𝑈.

(C. 1)

where we abuse the notation and define 𝑁𝑇 as both the number and the set of treated cities, and𝑁𝑈 as both the number and the set of “untreated” cities.
Figure C1. The General-Equilibrium Effects of Public Transportation Investment

Notes: Initial conditions are such that 𝜏𝑖𝑗 = 10 for all 𝑖, 𝑗, 𝑌𝐴 = 𝑌𝐶 = 1000, 𝑌𝐵 = 𝑌𝐷 = 𝑌𝐸 = 500. Panels A
and B show the changes in logged values of population. Panel A is for the case where trade costs decrease
between cities A and B, whereas panel B is for the case where trade costs decrease between cities A and D.
Panels C and D show the changes in logged values of MA. Panel C is for the case where trade costs decrease
between cities A and B, whereas panel D is for the case where trade costs decrease between cities A and D.

46

Clearly, this estimation strategy does not work in this context because the “untreated” cities
are not really untreated. The construction of the route does change these cities’ population sizes,
and hence, they cannot be used as the valid counterfactual. Another problem with equation(C.1) is that there are winners and losers in both the treated and the comparison groups, as seenin Figure C1. In Panel A, city B (“treated”), and cities D and E (“untreated”) experience gains
in population, whereas city A (“treated”) and city C (“untreated”) lose. Similar comments apply
to Panel B. The problem here is that there seems to be no systematic way to account for such
heterogeneity in the difference-in-differences framework since it is theoretically impossible toknow a priori which cities are affected and by how much, and whether the effect is negative or
positive when trade costs change.
In contrast, the MA approach accounts for all the general equilibrium effects that arise fromthe construction of transportation infrastructure. In Panel C, the MA increases in cities B, D,
and E, whereas it decreases in cities A and C. In response to these MA changes, the population
sizes of cities B, D, and E increase while those of cities A and C decrease. Similar comments
apply to Panel D. Our estimand and sample analogue of interest is, thus, the expected value ofthe gradient:𝐸[∑𝑖∈𝑁 ∆𝑌𝑖
𝜕𝑌]=.
∑𝑖∈𝑁 ∆𝑀𝐴𝑖
𝜕𝑀𝐴(C. 2)

We estimate this gradient typically by regressing the change in the outcome of interest against
the change in market access. This estimation strategy should work, in principle, as long as thechanges in MA are purely exogenous (i.e., random or as good as random).
Borusyak and Hull (2023) clarifies that, unfortunately, even if the assignment oftransportation investments is random, changes in MA may not be random. Figure C1 indeed
demonstrates why this is the case. The changes in MA for each city depend not only on where
the cost shocks occur but also on where each city is located in a given network. In other words,
the same (random) cost shock can generate different the changes in MA, and the economic
mechanism that induces this difference is correlated with changes in the outcome variable,
leading to a heterogeneity term that works as the omitted variable. This is the essential
endogeneity that arises from the intrinsic network structure. Furthermore, this also implies thatthe conventional IV approach that relies on historical, planned, or inconsequential
transportation routes is unlikely to work well because all these variables are plausibly47

correlated with the intrinsic network structure. We elaborate on this point further in the next
subsection.
C.2. How the Recentered Market Access Address the Empirical Problem
As an alternative, Borusyak and Hull proposes a recentered instrumental variable approach.
In the current setup, the approach “recenters” the MA variable, and is henceforth called a
recentered market access (RMA) approach. The approach proceeds in three steps. First, we
generate a sequence of 𝑆 random draws of public transportation investments, or equivalently,
draws of a vector {𝜏 𝑠 }𝑠∈𝑆 . Second, we calculate a sample analogue of the expected value of
MA (EMA) that follows from this sequence of random draws: 𝜇𝑖 ≡ 𝐸𝑠 [𝑀𝐴𝑖 |𝜏 𝑠 ] ≈1
𝑆∑𝑠∈𝑆 𝑀𝐴𝑖 (𝜏 𝑠 ). Third, we create a recentered instrument by calculating the difference between

the observed and the expected MA for each realized vector of 𝜏: 𝑅𝑀𝐴𝑖 (𝜏) ≡ 𝑀𝐴𝑖 (𝜏) − 𝜇𝑖 .
By construction, this recentered MA change must be correlated with the observed change in
MA (relevance) and must be orthogonal to any non-random components that are related to thechange in MA (exogeneity). Simply put, the approach essentially purges out the “pure shock”,
which originates from the “as-good-as-random” assignment of public transportation
investments, from the observed change in MA level, which arises in a complex manner fromthe non-random exposure of cities in a given network structure to the “as-good-as-random”
assignment.
Figure C2 illustrates the intuition for why and how this method works. Using the same
stylized transportation network in Figure C1, we take 300 random draws of transport costs 𝜏 𝑠 .
To operationalize the randomization, we assume each draw represents a reduction in thetransport cost on a single connected route, or a pair of cities, (𝑖, 𝑗). There are a total of eight𝑠possible route connections. Hence, we randomly select a city pair and take a random draw 𝜏𝑖𝑗from a uniform distribution on (1.5, 9.5).
Figure C2 plots the results of this exercise. For each city, the realized MA change isrepresented by dots, and the expected change in MA by triangles. All values are logged changes
relative to the status quo. The figure demonstrates that each city has a unique distribution of
the realized MA change, and the distributions are not centered around zero, despite taking
randomized draws of transport costs. Cities A and C tend to receive positive MA growth more
frequently while cities D and E tend to receive negative growths more frequently. As a result,the expected MA growths are positive for cities A and C, whereas they are negative for cities48

D and E. This illustrates Borusyak-Hull’s point that cities’ exposure to shocks may not be
random even if shocks are purely random. This is crucial for ensuring valid identification in
our empirical analysis. In the meantime, by “re-centering” (i.e., subtracting the expected MA
growth), we obtain the distributions of pure MA shocks that are orthogonal to the non-random
component with zero expected means, eliminating this structural dependency.
Figure C2. An Illustrative Visualization of RMA Approach

Notes: This figure plots the outcomes of simulations drawing 300 random transport cost shocks using thestylized city network depicted in Figure C1. For each city, the circles represent the realized MA change andthe triangles denote the expected MA change in logs.

49

Appendix D.
The Results of First Stage in Two-Stage Least Squares Regression
We calculate the MA variable with several values of trade elasticity and use them to estimate
the first stage in 2SLS, as we alter the trade-elasticity parameters to check the validity of ourmain specification, as detailed later in Appendix E.2. This stage is crucial because it tests thevalidity of our instrumental variable, the recentered market access (RMA) growth.
Table D1 presents the results of the first-stage estimation in the 2SLS regression. The main
result in Column (1) shows a statistically significant and positive impact on the change in theMA variable, with a coefficient of 1.12. This result implies a strong relationship betweenchanges in the MA variable and RMA growth. Figure 4 confirms this result: the RMA growth
and the change in the MA variable increase more where highways were built during the study
period and, conversely, decrease more where no highways existed. The result in Table D1shows that the variation in the RMA growth, along with socio- and natural-geographic factors
can capture about 73% of the variation in the changes in the MA variable, as measured by theR2. This result suggests that the theoretical relationship between RMA and MA discussed inAppendix B is empirically supported and satisfies the "relevance condition" for our
instrumental variable.
We further validate these results by altering the trade elasticity parameter 𝜃 in Columns (2)to (5). These different specifications consistently reveal a strong positive relationship between
the instrumental variable and the change in the MA variable. This consistency reinforces therobustness of our first-stage estimation and the reliability of the RMA growth as an
instrumental variable in our 2SLS framework.

50

Table D1. The Results of First Stage in Two-Stage Least Squares Regression⊿RMAθ =4.0θ =1.1θ =2.0θ =3.0θ =5.0

(1)

(2)

(3)

(4)

(5)

1.12 ***

1.44 ***

0.99 ***

1.01 ***

1.17 ***(0.08)(0.16)(0.11)(0.10)(0.07)

R-squared

0.73

0.44

0.61

0.71

0.74

Observations

614

614

614

614

614

Notes: All regressions use the same set of controls as in Table 3: average land steepness, average elevation,
percentage of building areas, percentage of water areas, percentage of wetlands, percentage of elderly,
percentage of manufacturing employees, manufacturing output and the taxable income per capita. Columns(2) to (5) show the estimates by altering the trade elasticity parameter 𝜃 to calculate ∆ ln 𝑀𝐴 and ∆𝑅𝑀𝐴.
Asterisks represent 1%, 5%, 10% significance levels. In parentheses are the cluster robust standard errors
clustered at the city level.

51

Appendix E.
Alternative Specifications
This appendix presents the results of alternative specifications to check the validity of ourmain specification as follows: (a) altering our outcome period to 1980-1990, 1985-1995, 19902000, and 2000-2010, (b) using alternative values of trade elasticity, (c) interacting the MA
variable with dummies representing various sources of heterogeneity, and (d) fixing the
population size of each city at the pre-treatment level instead of the lagged population size
when calculating MA in equation (2).
E.1. Altering the Outcome Periods
In this section, we estimate the economic impacts of transportation projects using a different
outcome period from our main specification, as it is not empirically clear when the economic
impacts of transportation infrastructure investments emerge. Our main specification in equation(3) relies on the assumption of a 10-year lag between the changes in the MA variable and theoutcome, whereas several previous studies assume no time lag between treatment and outcome(e.g., Donaldson and Hornbeck, 2016; Jedwab and Storeygard, 2021; Borusyak and Hull, 2023).
Table E1 presents the estimation results: the first column for each economic outcome shows
the results of OLS estimation, the second column displays those of EMA-OLS estimation, andthe third column presents those of RMA-IV estimation. We report the estimation results foraltering outcome periods: 1980-1990 in Panel A, 1985-1995 in Panel B, 1990-2000 in Panel C,
and 2000-2010 in Panel D.
Panel A, while not strictly applicable, serves as a placebo test that can be conducted within
the available data. This test indicates that changes in the MA variable from 1985 to 1995 do
not have statistically significant impacts on economic outcomes during 1980 to 1990. Thisconfirms that our treatment not only has no retrospective effect but also has no announcement
effect. Interestingly, the EMA variable has a statistically significant positive effect on
population and employment, suggesting that inherent geographical advantages positively affecteconomic growth regardless of the time period.
Panel B represents a similar approach to many previous studies, with no time lag between
treatment and outcome (e.g., Donaldson and Hornbeck, 2016; Jedwab and Storeygard, 2021;
Borusyak and Hull, 2023), and Panel C shows a specification taking a 5-year time lag between
the changes in the MA variable and the outcome. The OLS estimate on manufacturing output
52

is negative but statistically insignificant. In contrast, the EMA-OLS and RMA-IV estimates on
manufacturing output are negative and statistically significant. These findings suggest thatearlier outcome periods would only pick out the spurious correlation: trade-cost reductions
occurred in cities with low economic growth. The OLS estimates on population andemployment are positive and statistically significant, while the EMA-OLS and RMA-IV
estimates on these outcomes are statistically insignificant. As discussed in Section IV-A, for
population and employment outcomes, the EMA coefficient is positive and significant,
implying that these are respond to the intrinsic expectation of the MA growth, but the transport
megaprojects had no or limited effect.
The results in Panel D are robust to our main specification. The OLS estimates are 0.241,
0.030, and 0.032, respectively, for manufacturing output, population, and employment, and arestatistically significant at the conventional levels. The RMA estimates are robust to the use of
EMA or RMA only on manufacturing output: 0.308 (EMA-OLS) and 0.288 (RMA-IV), andstatistically significant. For population and employment, the RMA estimates become smaller
than the OLS estimates. As the Japanese government has pointed out, positive effects oftransportation network construction may arise later in Japan. These results suggest that, as
mentioned in Section IV-A, focusing on a period with a sufficient time lag from construction
timing is appropriate for estimating and assessing the economic impacts of transportation
construction. Note, however, that the estimates in Panel D may capture additional effects dueto the large time lag from the treatment period.

53

Table E1. The Estimation Results for Altering the Outcome Period
Employment

Population

Manufacturing Output
OLS

EMA-OLS

RMA-IVOLS

EMA-OLS

RMA-IVOLS

EMA-OLS

RMA-IV

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

Panel A: 1980-1990⊿ ln MA

0.073-0.046-0.0090.017-0.013-0.0040.013-0.029-0.016(0.065)(0.091)
0.332(0.074)(0.011)(0.016)0.084 **

(0.012)(0.016)(0.023)0.119 **

(0.018)⊿ EMA
R-squared

0.072

0.077

(0.049)(0.038)(0.203)
0.070

0.476

0.480

0.474

0.571

0.576

0.569

Panel B: 1985-1995⊿ ln MA-0.071-0.218 **-0.173 **(0.057)(0.087)(0.074)⊿ EMA

0.025 *
(0.013)

Panel C: 1990-2000⊿ ln MA

0.077

0.084

0.074

-0.035-0.219 ***-0.162 **(0.077)(0.068)0.421

0.035 ***
(0.012)

0.069

0.081

0.428

0.434

0.418

0.004

0.013

(0.017)(0.014)0.440-0.019(0.018)0.188 ***
0.509

0.039 ***
(0.014)

0.521

0.504

-0.026-0.006(0.021)(0.016)0.183 ***
(0.052)(0.036)0.065-0.039 *(0.024)(0.054)0.088 **

0.514 ***
(0.195)R-squared

0.028 *
(0.016)(0.040)(0.052)
⊿ EMA

0.001
(0.015)0.102 **

0.411 **
(0.198)R-squared-0.011(0.020)

0.431

0.467

0.482

0.462

Panel D: 2000-2010⊿ ln MA

0.241 ***
(0.078)⊿ EMA

0.308 ***
(0.098)-0.1890.288 ***
(0.090)0.030 ***
(0.009)0.013(0.012)0.046 *

0.018 *
(0.009)0.032 **
(0.013)0.015(0.015)(0.041)(0.026)(0.183)0.007(0.019)0.071 *

R-squared

0.062

0.063

0.061

0.530

0.532

0.529

0.456

0.460

0.455

First-Stage F-Stat.
Observations

614

614

209
614

614

614

209
614

614

614

209
614

Notes: Asterisks represent 1%, 5%, 10% significance levels. In parentheses are the cluster robust standard
errors clustered at the city level. All regressions use the same set of controls as in Table 3: average land
steepness, average elevation, percentage of building areas, percentage of water areas, percentage of wetlands,
percentage of elderly, percentage of manufacturing employees, manufacturing output and the taxable income
per capita. The first column for each economic outcome shows the results of OLS regression, the second
column shows those of OLS regression with ∆𝐸𝑀𝐴, and the third column shows those of 2SLS with ∆𝑅𝑀𝐴
as the instrumental variable. Each panel represents a different period for outcomes: Panel A for 1980-1990,
Panel B for 1985-1995, Panel C for 1990-2000, and Panel D for 2000-2010.

54

E.2. Alternative Trade Elasticities
Trade elasticity is an important parameter that balances the size of the market and transport
costs in equations (1) and (B.11). If the balance is incorrect, it may not properly reflect thegeneral equilibrium effect, which depends on the size of the market, resulting from lower
transport costs. Unfortunately, however, we assume the trade-elasticity parameter 𝜃 = 4 ,following Simonovska and Waugh (2014) since we do not estimate trade elasticity. Therefore,
we calculate and estimate the MA variable with several values of trade elasticity in this section.
Before analyzing, we present the geographic distribution and its change over time when wevary the trade elasticity. Figure E1 shows the geographic distribution of the changes in logged
values of MA using trade elasticities 𝜃 ∈ [1.1, 2.0, 3.0, 5.0]. The red legend represents a large
positive logarithmic change in the MA variable, whereas the blue legend indicates a large
negative change. As 𝜃 decreases, the MA responds too sharply to the changes in transport costs.
Panels A and B in Figure E1 are similar to the geographic distribution of transport cost
reductions shown in Figure 2. This suggests that a smaller trade elasticity (𝜃 < 3.0) may notadequately reflect shifts in the size of the economy or capture the full extent of the generalequilibrium effect. On the other hand, when 𝜃 ∈ [3,5] , the geographic distribution closely
resembles that shown in Figure 4-A.

55

Figure E1. The Geographic Distribution of the MA Variables by Each Trade Elasticity

Notes: These figures show the changes in logged values of MA from 1985-1995. Panel A shows the case
𝜃 = 1.1, Panel B shows 𝜃 = 2.0, Panel C shows 𝜃 = 3.0, and Panel D shows 𝜃 = 5.0. As in Figure 1, thethin black lines represent highways built before 1985, and the thick black lines represent highways built
between 1985 and 1995.

Figure E2 illustrates the changes in MA (in log) relative to the values in 1985 as a result of
the reduction in transport costs. The gray shaded areas, from lightest to darkest, represent the
1st to 99th, 5th to 95th, and 25th to 75th percentiles of all observations, respectively. Each line
represents the median for each region. Dots represent the raw values for cities. These figuresindicate that the MA variable increases faster in the Shikoku region with lower trade elasticity(𝜃 < 3.0), reflecting the change in transport cost reductions within Shikoku. The figures also
show that when the elasticity is small, a rapid change in MA occurs in two stages: 1985-1990
and 2000-2005. The sharp increase in the MA variable over 2000-2005 clearly reflects the56decline in transport costs in 2005 shown in Figure 3-A, rather than the decline in transport costs
due to the opening of the Central Seto Bridge. This graphical result, as well as Figure E1,
supports the finding that when we use smaller trade elasticities (𝜃 < 3), the MA responds too
sharply to the changes in transport costs, with magnitudes that are hard to justify.
Figure E2. Changes in MA Variable over Time by Each Trade Elasticity

Notes: Each figure shows the changes in MA (in log) relative to the values in 1985, in a manner analogous
to Figure 3-B. Panel A shows the result of the trade elasticity 𝜃 = 1.1, Panel B shows the result of 𝜃 = 2.0,
Panel C shows the result of 𝜃 = 3.0, and Panel D shows the result of 𝜃 = 5.0.

Table E2 presents the impacts of changes in the MA variable with each trade elasticity on
economic outcomes. As in Table E1, the first column of each economic outcome shows theresults of OLS estimation, the second column shows those of EMA-OLS estimation, and thethird column shows those of RMA-IV estimation. Each panel corresponds to a different trade57

elasticity: 𝜃 = 1.1 in Panel A, 𝜃 = 2.0 in Panel B, 𝜃 = 3.0 in Panel C, and 𝜃 = 5.0 in PanelD.
Panel A shows that the results for 𝜃 = 1.1 are not statistically significant, except for
population. This effect on population may only pick out spurious correlations. This is because
the variation of the MA variable is similar to the transport cost variations, making small-sizeand low-growth cities in the Shikoku region, which would normally be the untreated group,
become the treated group. The fact that the EMA growth, which should be positive, is estimatedto be statistically significant and negative supports this argument. Panel B shows that both
EMA-OLS and RMA-IV estimates are marginally significant rather than statisticallysignificant. Given the geographic variation in Panel B, it indicates that we may not correctly
estimate the causal effects between the MA variable and economic growth.
The results in Panels C and D are robust to our main specification. In Panel C, the OLS
estimates are 0.262 and 0.035, respectively, for manufacturing output and population, and arestatistically significant at the 1% significance level. The RMA estimates are robust to the useof EMA or RMA only on manufacturing output: 0.278 (EMA-OLS) and 0.278 (RMA-IV) andstatistically significant. In Panel D, the OLS estimates are 0.142, 0.028, and 0.033, respectively,
for manufacturing output, population, and employment, and are statistically significant at theconventional levels. The RMA estimates are 0.133 and 0.016, respectively, for manufacturing
output and population, and are statistically significant at the 5% significance level.

58

Table E2. The Estimation Results for Altering Trade Elasticity
Manuf. Output

Population

Employment

OLS

EMA-OLS

RMA-IVOLS

EMA-OLS

RMA-IVOLS

EMA-OLS

RMA-IV

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

Panel A: θ =1.1
⊿lnMA

-0.040-0.118-0.087(0.074)(0.190)(0.121)0.028 ***
(0.008)

0.102

⊿EMA

0.073

0.073

0.051 ***
(0.014)0.002-0.019-0.011(0.013)(0.035)(0.024)-0.050 **(0.254)First-Stage F-stat.
R-squared

0.066 ***
(0.020)0.028(0.022)
85
0.072

0.463

0.466

(0.040)
85
0.458

0.389

0.389

85
0.388

Panel B: θ =2.0
⊿lnMA

0.088
(0.089)⊿EMA

First-Stage F-stat.
R-squared

0.073

0.206 *
(0.110)0.209 *
(0.113)0.033 ***
(0.012)0.027 *
(0.016)0.027 *
(0.015)-0.005-0.011-0.011(0.019)(0.024)(0.024)-0.256

0.013

0.013

(0.164)(0.022)(0.029)

0.076

79
0.071

0.461

0.461

79
0.461

0.389

0.389

79
0.389

Panel C: θ =3.0
⊿lnMA

0.262 ***
(0.084)⊿EMA

First-Stage F-stat.
R-squared

0.082

0.278 ***
(0.097)0.278 ***
(0.096)0.035 ***
(0.012)

0.021

0.021

0.021

-0.011-0.010(0.015)(0.013)(0.019)(0.024)(0.022)-0.0500.045(0.217)(0.034)

0.082

96
0.082

0.461

0.463

0.101 *
(0.052)
96
0.460

0.390

0.394

96
0.388

Panel D: θ =5.0
⊿lnMA

0.142 **
(0.057)0.127(0.082)0.133 **
(0.062)0.028 ***
(0.008)

0.041

⊿EMA

Observations

0.016 **
(0.008)0.033 ***
(0.012)0.051 **

(0.209)First-Stage F-stat.
R-squared

0.009(0.011)-0.0080.008(0.018)(0.013)0.111 ***

(0.023)(0.042)

0.079

0.079

259
0.079

0.464

0.467

259
0.462

0.394

0.403

259
0.391

614

614

614

614

614

614

614

614

614

Notes: Asterisks represent 1%, 5%, 10% significance levels. In parentheses are the cluster robust standard
errors clustered at the city level. All regressions use the same set of controls as in Table 3: average land
steepness, average elevation, percentage of building areas, percentage of water areas, percentage of wetlands,
percentage of elderly, percentage of manufacturing employees, manufacturing output and the taxable income
per capita. Each column is the same as in Table E1. Each panel represents different trade elasticities: Panel
A with 𝜃 = 1.1, Panel B with 𝜃 = 2.0, Panel C with 𝜃 = 3.0, and Panel D with 𝜃 = 5.0.

59

E.3. Heterogeneity
Our main specification is valid when the elasticity is homogeneous or if heterogeneity is
uncorrelated with MA changes because it estimates the average elasticity of a city’s economic
size with respect to the market access. Yet, omitting the true MA variable defined in equation(B.11), as in equation (1), may create unobserved heterogeneity in economic impacts that may
be correlated with the changes in MA. To address this concern, we estimate a version ofequation (3) allowing for the heterogeneous effects of MA:∆(ln 𝑌𝑖 )05−95 = ∑ 𝛽𝑟 𝕀[𝑖 ∈ 𝐺𝑟 ]∆(ln 𝑀𝐴𝑖 )85−95 + 𝑔(𝑋𝑖,80 ) + Δ𝜖𝑖,95−05 ,𝑟where 𝕀[∙] is an indicator, which equals one if city 𝑖 belongs to a group 𝐺𝑟 and zero otherwise.
We classify cities into three groups (heterogeneity dummies) using the following threealternatives: the tertiles of change in transport cost from 1985 to 1995, population size as of1980 and MA level as of 1985. Unfortunately, we have only a single instrumental variable;
hence, we instead use the EMA as a control, which has already been shown to be helpful inaddressing endogeneity.
Table E3 presents the estimation results based on the above equation. Columns 1 to 3 show
the results of interaction terms with changes in transport costs from 1985 to 1995, Columns 4
to 6 show those of interaction terms with population size as of 1980, and Columns 7 to 9 show
those of interaction terms with the level of the MA variable as of 1985. The first column ofthese three interaction terms represents the effect on manufacturing output, the second column
indicates the effect on population, and the third column shows the effect on employment. All
regressions use the same set of controls as in Table 3: average land steepness, average elevation,
percentage of building areas, percentage of water areas, percentage of wetlands, percentage ofelderly, percentage of manufacturing employees, manufacturing output and the taxable income
per capita, and the EMA variable.
For all results, the p-value of the joint hypotheses test exceeds 0.05, which means that we
fail to reject the null hypothesis of no interaction effects on economic outcomes. This does notnecessarily imply that heterogeneous effects are omitted, even if we use the MA variable basedon equation (1). The results for population and employment show, as in our main estimation,that they are relatively more affected by the intrinsic expectation of the MA growth rather thanby the transport-cost-induced changes in the MA variable.
60

For the interaction terms with changes in transport costs, the estimate on manufacturing
output is a statistically significant 0.204% increase in the lowest tertile, where transport costs
decrease the most, while a marginally significant 0.351% increase in the highest tertile. For theinteraction terms with the population size as of 1980, the estimate on manufacturing output isa statistically significant 0.195% increase in the highest tertile, while a marginally significant
0.293% increase in the second tertile. For the interaction terms with the level of the MA variable
as of 1985, the estimates on manufacturing output are statistically insignificant. The results forthe first two interaction terms suggest that statistically significant larger manufacturing output
growth occurs where economic growth is expected. However, the magnitude of the coefficients
does not follow the order of the tertiles, suggesting that this outcome may grow under complex
general equilibrium effects.
For the interaction terms with changes in transport costs, the effects on population andemployment are statistically insignificant, but the economic impacts are unexpectedly smaller
as the reduction in transport costs is larger. This supports our main estimations that these
economic outcomes do not respond to lower transport costs through the megaprojects. For theinteraction terms with the population size as of 1980, the effects on these outcomes are
statistically insignificant. However, for the interaction terms with the level of the MA variable
as of 1985, the estimate on population is a statistically significant 0.157% increase in thehighest tertile, while the estimate on employment is a marginally significant 0.146% increasein the second tertile. In contrast to manufacturing output, these two economic outcomes mostly
follow the order of the tertiles. These results suggest that cities that enjoy economic abundance
prior to the transportation investment tend to gain more from an increase in the MA variable.

61

62

(9)

(8)

(7)

(6)

(5)

(4)

(3)

(2)

(1)

0.061
0.450
614

0.089
0.490
614

0.325
0.094
614

0.630
0.411
614

0.638
0.468
614

0.114
0.084
614

0.862
0.402
614

0.774
0.474
614

0.055

0.084

614

Joint Test

R-squared

Notes: Asterisks represent 1%, 5%, 10% significance levels. In parentheses are the cluster robust standard errors clustered at the city level. This table shows theresults of the interaction term with changes in transport costs from 1985 to 1995, population size as of 1980, and level of market access as of 1985. All regressions
use EMA growth and the same set of controls as in Table 3: average land steepness, average elevation, percentage of building areas, percentage of water areas,
percentage of wetlands, percentage of elderly, percentage of manufacturing employees, manufacturing output and the taxable income per capita. For each interaction
term, the first column presents manufacturing output, the second column reports population, and the third column shows employment.

Observations(0.048)(0.026)0.138 ***
(0.241)

0.048 *

(0.050)0.108(0.029)0.136 ***

(0.080)(0.233)0.055 *

(0.070)(0.050)0.034(0.094)0.195 **

(0.030)0.001

0.124 **

(0.351)(0.023)(0.015)(0.047)(0.031)(0.193)0.057 *

-0.102

0.004

0.018

0.010

0.007

(0.158)0.146 *

(0.024)(0.015)(0.101)(0.035)(0.022)(0.050)(0.039)(0.299)0.157 **

0.012
0.024

0.167

0.016

0.015

-0.0060.040-0.0130.351 *

(0.026)(0.014)(0.133)(0.040)(0.026)(0.198)(0.022)(0.013)(0.093)0.293 *

-0.037
0.005

0.124

-0.044

0.008

0.020

-0.017-0.0010.204 **

Employment

Population

Manuf. Output

Employment

Population

Manuf. Output

Employment

Market Access as of 1985

Population

Population as of 1980

Manuf. Output

Log Change in Transport Cost

(0.221)

⊿EMA

3rd

2nd

1st

TertileInteraction Terms

Table E3: The Estimation Results with Interaction Term

E.4. Fixed Market Size in MA
There are several ways to address endogeneity arising from the market size that constitutes
the MA variable. Our main strategy is to use a lagged market size against transport costs (e.g.,
Donaldson and Hornbeck, 2016). Other prior studies take the strategy of fixing the market sizeat the pre-transportation project level with respect to changes in transport costs (e.g., Borusyak
and Hull, 2023; Jedwab and Storeygard, 2022). In this section, we construct the MA variable
by using the population as of 1980, before the project, as the market size.
Table E4 shows results in a manner analogous to Table 3. Most estimates in Table E4 closely
resemble those in Table 3, ensuring that the results are robust to our main specification.
Transport megaprojects during the period 1985-1995 have statistically significant and positive
impacts on manufacturing output in all specifications, and on population in OLS estimates and
RMA-IV estimates. The RMA-IV estimates show that transport megaprojects lead to anincrease in manufacturing output by 2.01% and population by 0.21% over the period 19952005, using the fact that the MA variable with a fixed market size increased by 10.4% during
1985-1995. However, we note an important difference between Table E4 and Table 3: themagnitudes of the EMA-OLS and RMA-IV estimates are almost the same as those of the OLS
estimates. In contrast to previous studies, the EMA estimates in Column 2 for each economic
outcome are not statistically significant, and their magnitude is small.

63

Table E4. The Estimation Results for Utilizing Market Access with Fixed the Market Size

Panel A: Manuf. Output⊿ ln MAOLS(1)

EMA-OLS(2)

RMA-IV
(3)

0.173 **
(0.068)0.203 **
(0.091)-0.088(0.244)0.193 ***
(0.074)

0.079

0.079

0.079

0.021 **
(0.009)0.019(0.012)0.006(0.024)0.020 **
(0.010)

0.459

0.459

0.459

0.006
(0.015)0.004(0.019)0.008(0.043)0.005(0.016)

0.389

0.389

0.389

614

202
614

⊿ EMA
R-squared
Panel B: Population⊿ ln MA⊿ EMA
R-squared
Panel C: Employment⊿ ln MA⊿ EMA
R-squared
First-Stage F-Stat.
Observations

614

Notes: Asterisks represent 1%, 5%, 10% significance levels. In parentheses are the cluster robust standard
errors clustered at the city level. All regressions use the same set of controls as in Table 3.: average land
steepness, average elevation, percentage of building areas, percentage of water areas, percentage of wetlands,
percentage of elderly, percentage of manufacturing employees, manufacturing output and the taxable income
per capita.

64Appendix F.
Economic Impacts of the Transport Megaproject on Population and Employment
This appendix provides the evaluation of the economic impacts on population andemployment of two scenarios: (a) the Central Seto Bridge and other highways and (b) theCentral Seto Bridge, against the counterfactual with no such investment, as described in Section
V-A. Figure F1 shows the economic impacts of transport megaprojects on population, andFigure F2 shows those on employment. The left side of each figure plots economic impacts of
the Central Seto Bridge and other highways on the growth rates over the period 1995-2005
against the 1980 population size. In the same manner, the right side of each figure plots theimpacts of the Central Seto Bridge only. In the figures, the cities directly connected by thetransport projects are marked as diamonds while the cities not directly connected are indicatedas dots. The cities in Shikoku are marked in red and those in other regions are in blue.
Figure F1 shows that cities gain population by 0.26% (Panel A) and 0.06% (Panel B) relative
to no transport investment, on average. At the median, the largest cities (the fifth category) gain
population by 0.32% (Panel A) and 0.11% (Panel B), whereas the smallest cities (the firstcategory) gain population by 0.09% and 0.006%, respectively. Figure F2 shows that cities gain
employment by 0.04% (Panel A) and 0.01% (Panel B) relative to no transport investment, on
average. At the median, the largest cities (the fifth category) gain employment by 0.06% (PanelA) and 0.02% (Panel B) whereas the smallest cities (the first category) gain employment by
0.02% and 0.001%, respectively.
These results show essentially the same pattern as the results presented in Section V-A: (1)a majority of cities gain economic benefits from the transport megaproject, regardless of theirinitial economic abundance, (2) there is a sign of agglomeration economies, (3) there issubstantial heterogeneity in these economic impacts across cities within each category, withsome cities losing substantially from the megaproject, (4) the Straw effect phenomenon did notmaterialize, at least not in the form originally claimed by the critics, and (5) the magnitudes ofthe estimated impacts are smaller in Panel B than in Panel A, implying that the pure effect of
the Central Seto Bridge is relatively small and much of the effect of the projects comes fromthe combined effect of the other highways and the Bridge.

65

Figure F1. Economic Impacts of the Transport Megaproject on Population
across Cities of Different Population Sizes

Notes: The size of economy on the horizontal axis is based on the population size as of 1980, following thesame classification as in Table 2. As in Figure 5, In the figures, the cities directly connected by the transport
projects are marked as diamonds while the cities not directly connected are indicated as dots. The cities in
Shikoku are marked in red and those in other regions are in blue. The black lines indicate the median foreach category.

66

Figure F2. Economic Impacts of the Transport Megaproject on Employment
across Cities of Different Population Sizes

Notes: The size of economy on the horizontal axis is based on the population size as of 1980, following thesame classification as in Table 2. As in Figure 5, In the figures, the cities directly connected by the transport
projects are marked as diamonds while the cities not directly connected are indicated as dots. The cities in
Shikoku are marked in red and those in other regions are in blue. The black lines indicate the median foreach category.

67

Appendix G.
Geographical Distribution of Net Benefit, Cost Burden, and Total Benefit
This appendix presents the geographic distributions of economic benefits estimated in
Section V-A, construction cost burden, and the net benefits per capita estimated in Section VB. Figure G1 shows the geographical distribution of the Central Seto Bridge and otherhighways, while Figure G2 shows that of the Central Seto Bridge only. In both figures, Panel
A shows city-level economic benefits per year estimated from the IV estimates ofmanufacturing output in Table 3, Panel B shows city-level cost burden per year estimated fromthe publicly available information on construction costs, and Panel C shows the net benefits
per capita per year estimated by equation (10a).
Figure G1 shows that cities newly connected by highways during 1985-1995 tend to havehigher economic benefits and net benefits. Both economic and net benefits are particularly highin the inland sea coastal areas of the Chugoku and Shikoku regions. In large peripheral cities
such as Okayama and Hiroshima, the economic benefits are even higher, and the benefits arecommensurate with the heavy cost burden. In cities without highways (e.g., southern Shikoku
and southern Kinki regions), the economic benefits tend to be negative, and consequently, thenet benefits are also negative.
Panel A of Figure G2 shows that larger economic benefits are enjoyed in cities located in
Okayama and Kagawa prefectures, which are directly connected by the Central Seto Bridge,
and cities that were connected by highways as of 1985. This indicates that a local investment,such as connecting two points in Okayama and Kagawa prefectures, generates economic
benefits across western Japan. This figure also shows negative economic gains in cities locatedin the northern Kinki, the southwestern Shikoku, the eastern Kyushu, and parts of the inlandsea coast regions, while Figure G1 shows positive economic benefits for these cities. Thisimplies that the effect of the Bridge is smaller than that of the other highways and the Bridge.
Panel C of Figure G2 shows that the cities in the inland sea coast areas of Okayama and Kagawa
prefectures and in the eastern Kinki region enjoy larger net benefits. In addition, many cities
where highways existed before 1985 gain positive net benefits. Although the pure economic
effect in Figure G2 is smaller than the combined effects shown in Figure G1, the city-level net
benefit loss is reduced because the cost burden is much lower. Thus, these geographicaldistributions support the importance of accounting for the complex general-equilibrium effects
in evaluating public transportation projects.

68Figure G1. Geographical Distribution of Net Benefit, Cost Burden, and Total Benefit
from the Central Seto Bridge and Highway

69

Notes: Panel A shows that the city-level economic benefit from manufacturing output per year, Panel B
shows the city-level cost burden per year, and Panel C shows the net benefit per capita per year. Each panel
shows values in units of JPY as of 1988. As in Figure 1, the thin black lines represent highways built before
1985, and the thick black lines represent highways built between 1985 and 1995.

70

Figure G2. Geographical Distribution of Net Benefit, Cost Burden, and Total Benefit
from the Central Seto Bridge

71Notes: Panel A shows that the city-level economic benefit from manufacturing output per year, Panel B
shows the city-level cost burden per year, and Panel C shows the net benefit per capita per year. Each panel
shows values in units of JPY as of 1988. As in Figure 1, the thin black lines represent highways built before
1985, but the thick black line represents the Central Seto Bridge.

72