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

Do Winners Win More from Transport Megaprojects?
Evidence from the Great Seto Bridges in Japan
Yoshifumi Konishi, Akari Ono
20 February, 2024
DP2024-003
https://ies.keio.ac.jp/en/publications/23409/

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

Do Winners Win More from Transport Megaprojects? Evidence from the Great Seto
Bridges in Japan
Yoshifumi Konishi, Akari Ono
Keio-IES DP2024-003
20 February, 2024
JEL Classification: O18, R4, R11, R12
Keywords: Market Access, Transportation Investment, Core-Periphery Model, Economic
Geography, Quantitative Spatial Model
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 large transportation projects. We empiricallyinvestigate whether such an effect manifests in the case of the Great Seto Bridges inJapan, a 70-billion-dollar project implemented as part of the “Building-a-new-Japan”
initiative in the 1980s-1990s. We employ the recently developed recenteredinstrumental variable approach in the difference-in-differences design, exploiting the
sharp decline in transport costs and its differential impacts on market access levels
across cities of different economic prosperity as exogenous sources of variation. We findthat, contrary to the straw effect, large peripheral cities gain more than core cities,
rather than lose, from the megaproject. We also demonstrate that the distribution of
winners and losers from the megaproject depends on where the associated cost
reductions occur in the existing network structures.
Yoshifumi Konishi
Department of Economics, Keio University〒108-8345 2-15-45 Mita, Minato-ku, Tokyoykonishi@econ.keio.ac.jp
Akari Ono
Graduate School of Economics, Keio University〒108-8345 2-15-45 Mita, Minato-ku, Tokyo
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, 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 Bridges 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 and discussions
throughout the development of this paper. We also thank Jevan Cherniwchan, Shota Fujishima, Kentaro
Fukumoto, 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).

1

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, andperipheral cities may lose, from large transportation projects. We empirically investigate
whether such an effect manifests in the case of the Great Seto Bridges in Japan, a 70-billiondollar project implemented as part of the “Building-a-new-Japan” initiative in the 1980s1990s. We employ the recently developed recentered instrumental variable approach in the
difference-in-differences design, exploiting the sharp decline in transport costs and its
differential impacts on market access levels across cities of different economic prosperity as
exogenous sources of variation. We find that, contrary to the straw effect, large peripheral
cities gain more than core cities, rather than lose, from the megaproject. We also demonstratethat the distribution of winners and losers from the megaproject depends on where theassociated cost reductions occur 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 Network2

I. Introduction
Public transportation infrastructure is essential for economic growth and for 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,the peripheral 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 from a plastic straw drawing water (“economic resources”)
away from a cup (“periphery”).
Indeed, the Straw-effect phenomenon has a strong foundation in economic theory. Since
Krugman (1991), economists have incorporated various economic mechanisms (e.g.,
agglomeration economies, endogenous labor migration, increasing returns to scale) into thetheory of trade and economic geography, virtually all of which generally predict heterogeneous
general equilibrium impacts of public transportation investments (Krugman, 1991; Krugman
and Venables, 1995; Helpman, 1998; Ottaviano et al., 2002; Hanson, 2005). Therefore, theeconomic abundance some cities enjoy today may be the direct consequence of economic
advantages they had for some historical, political, or geographic reasons rather than public
transportation investments per se. This line of arguments can be easily adapted to recent
advances in the quantitative spatial model (QSM) to demonstrate that under a variety of initialconditions, there are generally winners and losers from any given transportation investment
in a given network of cities (see Section II).
This manuscript empirically investigates whether the Straw-effect phenomenon manifests
in the case of the Great Seto Bridges in Japan during the 1980-1990s, a period marked byaccelerated economic growth (See Figure 3, Section III). 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 project was the construction of thethree bridges that connect the isolated island region (Shikoku) to the main island of Japan(Honshu). The Bridges’ construction lasted 21 years from 1978 to 1999, and its financial costis estimated to be roughly 70 billion dollars. It is one of the most expensive transport
megaprojects in Japan and worldwide (Table 1, Section III). There were concerns, even before
construction began, regarding the economic impacts of the Bridges on the surrounding
regions. While the project’s intention was to promote the economic development of peripheral
regions (Chugoku and Shikoku), the opponents of the project expressed serious concerns that3the economic activities might be simply drawn from Shikoku to core cities in the main island
such as Osaka (in the Kinki region) and Fukuoka (in the Kyushu region) or that the peripheral
regions may simply end up with a large financial debt without much economic gain (Asahi

Newspaper, 1987; 1998). For example, it is estimated that it would take at least 42 years,
counting from 2008, to fully repay the debt for the project (Mainichi Newspaper, 2008).
Our primary objectives are, first, to quantify the heterogeneous causal effects of thistransport megaproject on peripheral cities’ economic outcomes (population, economic
income, and employment), and second, to evaluate the distribution of winners and losers
relative to their initial conditions (do winners tend to win while losers lose more?). By doing
so, we also empirically evaluate the extent to which the Straw-effect phenomenon has (or hasnot) manifested in the Western Japan. Addressing these questions is highly policy-relevant,for a large sum of public investments are still being made on transportation infrastructures inlow- and middle-income countries.2
Accomplishing these goals, however, is empirically quite challenging. Over the last few
decades, a number of empirical studies has attempted to estimate the economic effect ofpublic transportation investments on a variety of city-level economic variables (e.g., Chandra
and Thompson, 2000; Baum-Snow, 2007; Duranton and Turner, 2011; Duranton and Turner,
2012; Duranton et al., 2014; Faber, 2014; Storeygard, 2016; Donaldson and Hornbeck, 2016;
Donaldson, 2018; Asher and Novosad, 2020; Banerjee et al., 2020; Baum-Snow et al., 2020).
Three important challenges are identified in this line of literature. First, either the location or
the timing or both of public transportation projects are likely endogenous, and causality may
even run in the reverse direction: Transportation investments may occur in areas where either
high or low economic growth is expected. To address this type of empirical challenges, theearlier literature often relies on quasi-experimental variation or the sharp discontinuity ineither the location or the timing of the investment. For example, Donaldson and Hornbeck(2016) exploit the expansion of U.S. railroad networks during the 1870s-1890s as the sourceof exogenous variation for changes in market access. Faber (2014) uses the optimal least cost
path of highways as an instrument for the non-random highway route placements while
Baum-Snow (2007), Baum-Snow et al. (2020) and Duranton and Turner (2012) use planned
highway or historical road network as an instrument for the interstate highway assignment.
Furthermore, in a regression-discontinuity design, Asher and Novosad (2020) exploit thediscrete cutoffs in village population size used to prioritize rural road construction in India.

2

The World Bank reports transport investment had $62.1 billion, which is 68% of total investments in 2022

(World Bank, 2022).

4

Second, the common concern in the earlier literature is that the general equilibriummechanism may plausibly “contaminate” otherwise credible quasi-experimental identificationand estimation strategies. Cities are connected, either directly or indirectly, through theconstruction of any given infrastructure in the transportation network of cities may (i) affectcities that are far apart and thus are not directly impacted (“spillover effect”), (ii) haveheterogenous impacts on cities that depend, in a subtle way, on pre-treatment levels of
economic development and transportation infrastructures (“stock effect”), and (iii) have“feedback effects” in the sense that its economic impact on one city affects other cities, which
in turn affect the original city. All of these are known to create identification failures in thequasi-experimental research design. To address this type of problem, the literature often
makes use of market access (MA) approach, in which the quasi-experimental variation intransport costs by a given transportation network is translated into the changes in the MA
variable, a summary statistic grounded in the spatial general equilibrium model that embodies
all of these nuisance effects (Donaldson and Hornbeck, 2016; Jaworski and Kitchens, 2019;
Mori and Takeda, 2019; Baum-Snow et al., 2020; Jedwab and Storeygard, 2022).
Third, there is an emerging literature highlighting another source of identification threat
to both types of empirical strategies (Borusyak and Hull, 2023; Borusyak, Jaravel, and Hull,2023). In essence, the MA approach requires strictly exogenous (or “random”) variation in
the MA variable for identification of its causal impact because there is no “untreated” groupin its general equilibrium framework. Borusyak and Hull (2023) have shown that even if
public transportation projects are randomly assigned, either in location or in timing, theresulting change in the MA for a city depends on its intrinsic location in the structure of thegiven economic network. To see this, imagine a city located in the center of the economic
network versus a city located at the edge of the network. Construction of a transportation
project at any point in the network would have different effects on the MA levels of thesecities. To address this fundamental problem, Borusyak and Hull (2023) proposes a recenteredinstrumental variable approach, which we also take in this paper and thus is outlined in detailbelow.
Our empirical strategy takes advantage of all these advances in this extremely rich empiricalliterature. First, we exploit the sharp decline in transport costs, induced by the constructionof the Great Seto Bridges and other highways during the 1980s-1990s. Second, we constructand calculate the market access variable based on a version of the QSM (Allen and Arkolakis,
2014; Redding and Turner, 2015; Redding and Rossi-Hansberg, 2017; Redding, 2020). We
then use the differential impacts of the Great Seto Bridges and other highway constructions
on market access levels across cities of different economic prosperity as the source ofexogenous variation. Third, we use this variation in the “recentered market access” (RMA)5instrumental variable method recently developed by Borusyak and Hull (2023). Intuitively,
the RMA extracts the purely exogenous variation from the observed MA, by purging out the
influence of endogenous variation that comes from its geographic network characteristics --some cities receive more transport projects and enjoy higher MAs precisely because they arelocated in geographically advantageous positions in 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 constructed from the MA variable.
Our IV estimates imply that a one-percentage increase in a city’s measured market accessleads to an increase in manufacturing output by 0.197%, in the city’s population by 0.039%,
and in employment (measured as the population in labor force) by 0.056%. These IV
estimates are not only statistically significant at the conventional levels, but also larger inmagnitude than OLS estimates. To put these numbers in context, we also calculate thepredicated impact on the cities’ economic growth in the region. Using the simulated QSM,
we predict the highway expansion between 1985 and 1995, which includes the Central Seto
Bridge resulted in the measured market access by 13.2% on average for cities in the WesternJapan region. Combined with the IV estimates, this increase in market access translates, on
average, into a 2.60% increase in the manufacturing output, a 0.51% increase in population,
and a 0.74% increase in employment during the subsequent 10-year period. These relatively
small economic gains, however, mask its highly heterogeneous impacts.
The heterogeneous economic impacts of the highway expansion are quantified in two ways.
Our first approach is to use the IV estimates and predict the growth of economic outcomes(manufacturing output, population, and employment) during the 1995-2005 period for all
cities in the study sample, and compare these outcome growths against the initial population
size as of 1980 (i.e., prior to the highway expansion). The result indicates that on average,
cities that had larger populations gained more, in all three outcomes, than those that had
smaller populations, implying that winners tend to win more from the public transport
projects while losers do not necessarily lose. We should not take this as a support for the Straweffect hypothesis, however. Within each population quintile, there is substantial heterogeneityin economic gains, which with a closer examination, can be taken to refute the Straw effect atleast in our empirical context. For example, large peripheral cities such as Hiroshima andOkayama gained more in all three outcomes than core cities such as Fukuoka and Osaka.
Furthermore, Takamatsu and Matsuyama, relatively large peripheral cities in the Shikoku
region, are also estimated to gain rather than lose from the transport projects.

6Our second approach is to calculate the back-of-envelope estimates of the net benefits of
the Central Seto bridge for all cities and compare them against the initial population size asof 1980. The problem with our first approach is that it does not account for the cost of highway
construction. Hence, to account for the cost, we use the publicly available information on thecost expenditures for the Central Seto Bridge. To estimate the economic benefit, we take thedifference in the predicted growth of manufacturing output between the two conditions: theobserved condition with the Central Seto Bridge versus the counterfactual condition without
the Central Seto Bridge. The results indicate that core cities are indeed estimated to lose fromthis transport megaproject while large peripheral cities are estimated to be the winners, withlarge gains in net benefit. Smaller peripheral cities are also estimated to lose from the project,
but their loss is much smaller than the core cities. With these results, we conclude that theStraw effect did not quite occur at least in the way that the critics expected to be in the caseof the Great Seto Bridge.
In our empirical framework, heterogeneous impacts occur in two pathways: the first is theheterogeneous impacts of the highway expansion on cities’ market access levels, and thesecond is the heterogeneous impacts of the changes in market access levels on economic
outcomes. Our market access approach naturally incorporates the first type of heterogeneity,
since the MA variable is constructed through the QSM. In contrast, the second type ofheterogeneous effects is not salient in our framework. In our base empirical specification, we
assume a homogenous effect of MA on economic outcomes. This is consistent with the model
prediction from the QSM. However, our MA variable excludes each city’s own population size
to avoid the endogeneity problem, and thus, some of the heterogenous effects are excluded
by construction. We also estimate alternative specifications allowing for the heterogeneous
effects of MA on economic outcomes. The results are reported in the Appendix E.
Our work closely complements the five strands of literature. To economize space, we only
touch on each and defer more thorough discussions to Appendix A. First, it is most closely
related to the studies that empirically investigate the impacts of transportation investmentson disadvantaged communities (Chandra and Thompson, 2000; Storeygard, 2016; Asher andNovosad, 2020; Jedwab and Storeygard, 2022). Second, related to the first literature, there isan extremely 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, 2018;
Mori and Takeda, 2019; Asher and Novosad, 2020; Baum-Snow et al., 2020; Banerjee et al.,2020; Jedwab and Storeygard, 2022). Third, ours builds on a large number of empirical studies
that use the market-access approach (Davis and Weinstein, 2003; Hanson, 2005; Breinlich,7

2006; Redding and Sturm, 2008; Nakajima, 2008; Head and Mayer, 2011; Donaldson andHornbeck, 2016; Jaworski and Kitchens, 2018; Mori and Takeda, 2019; Jedwab andStoreygard, 2022). Fourth, ours is also related to the theoretical literature on quantitative
spatial models (Redding and Turner, 2015; Redding and Rossi-Hansberg, 2017; Redding,2020). Lastly, our paper has an important implication for the public finance literature
studying how the burden of large public transport transportation investments should beshared among stakeholders (Anguera, 2006; Boardman et al., 2018).
This paper is organized as follows. Section II provides an intuitive explanation of the MA
approach as well as the recentered instrumental variable approach using a stylized quantitative
spatial model. It also demonstrates why conventional quasi-experimental identification
strategies fail and why the recentered instrumental variable succeeds in such a setup. Section
III discusses the institutional background and the data on highway constructions in Japan.
Section IV discusses the estimation and identification strategies, and Section V presents theestimation results. Section VI conducts a counterfactual policy simulation and presents theresults of our evaluation. Finally, Section VII concludes.

II. The Essence of the Empirical Problem and Its Solution
Before discussing our data and empirical setup in detail, we explain the essence of theempirical challenges and how our recentered market access approach might overcome thechallenges in a canonical quantitative spatial model (QSM) (Redding and Turner, 2015;
Redding and Rossi-Hansberg, 2017; Redding, 2020).
II-A. Market Access in a Stylized Quantitative Spatial Model
The QSM is a general equilibrium model of trade and migration across cities (with orwithout international trade) that explicitly accounts for economic geography over space. Here,we consider the model in a canonical form without international trade. We only describe themodel in the nutshell. Those unfamiliar with QSM are encouraged to read the details of themodel offered in Appendix B.
The model starts with a set of cities N with a structure of transportation, with bilateral
iceberg trade costs 𝜏𝑖𝑗 for each pair of cities (𝑖, 𝑗). Consumers in each city 𝑖 chooses where tolive and work, and how much to consume each variety of goods, from which city. Firms areheterogeneous in productivity and produce a unique variety. Firms decide where to produce
and how much (equivalently set the price) in monopolistic competition. The model is closed
8

by assuming exogenous amenity, untradable good (e.g., floor space), and (aggregate)
productivity for each city. The equilibrium quantities are solved by assuming the market
clearing conditions for trade, migration, and the utility equalization across cities.
The key driver of the market equilibrium in virtually all QSMs is the equation that defines
the market access (MA) for each city:−𝜃𝑀𝐴𝑖 = 𝜌 ∑ 𝜏𝑖𝑗
𝑀𝐴𝑗−1𝑌𝑗 .

(1)

𝑗∈𝑁

where 𝜏𝑖𝑗 is the iceberg trade cost between cities 𝑖-𝑗 (𝜏𝑖𝑗 ≥ 1), and theoretically it is the trade
costs taking into account natural and social geographical conditions. 𝑌𝑗 is the market size (wediscuss this below), 𝜌 is a constant, and 𝜃 is the trade elasticity (𝜃 > 1) (see Appendix B forderivation).
Intuitively speaking, 𝑀𝐴𝑖 represents how accessible the market is to the consumers and thefirms in a city 𝑖. For consumers, MA indicates how many types of goods consumers canpurchase and how reasonably priced they can purchase them. The larger the MA, the more
attractive it is to consumers, which increases the labor supply (population) in that city throughmigration. On the other hand, a firm‘s MA indicates how much price competition a firm faces
and how large a market it could be able to sell into. The larger the MA, the more attractivethat city is to firms and the higher the demand for labor through increased production of
goods in that city. MA for consumers is related to MA for firms in such a way that the former
is determined by the latter for all cities and the latter is determined by the former for all cities.
In other words, the MA is adjusted until the feedback loop of MAs for both consumers andfirms in all cities is balanced. Thus, each city's market access is a measure that is determinedby the spatial interactions among all cities.
II-B. An Illustrative Example: How Does the Empirical Problem Arise?
We now use a stylized version of this 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 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
9are meant to capture the intrinsic structure of network arising from the natural or social
geographic conditions that are purely exogenous. Cities are homogenous 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
investment. 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 1 describes the resulting
distribution of populations in terms of log changes in populations (top panels) and MAs(bottom panels).
Figure 1. 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 andB show the (log) changes in population. Panel A is for the case that trade costs decrease between cities A and
B whereas panel B is the case that that trade costs decrease between cities A and D. Panels C and D show the(log) changes in MA. Panel C is for the case that trade costs decrease between cities A and B whereas panel
D is the case that that trade costs decrease between cities A and D.

This illustrative example demonstrates the empirical challenges we wish to address in ourempirical study. Note, first, that by construction, the public transportation investment, the10

treatment, is related to any economic variables of the model, and thus, is endogenous in theeconometric sense. Hence, in this example, we assume away the kind of empirical challenges
researchers often face in a real empirical context (which we do address later), allowing us tofocus on other 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.3 In thissetup, the “untreated” cities would be cities C, D, and E in Panel A (B, C, E in Panel B).
Hence, our estimand and sample analogue of interest would be:

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

.

(2)

where we abuse the notation and define 𝑁𝑇 as both the number and the set of treated cities,
and 𝑁𝑈 as both the number and the set of “untreated” cities.
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 (2)is that there are winners and losers in both the treated and the comparison groups, as seen in
Figure 1. In Panel A, city B (“treated”) and cities D and E (“untreated”) experience gains in
population whereas city A (“treated”) and C (“untreated”) lose. Similar comments apply toPanel B. The problem here is that there seems no systematic way in which to account for such
heterogeneity in the difference-in-differences framework since it is theoretically impossible
to know a priori which cities are affected and by how much, and whether the effect is negativeor positive when trade costs change.
In contrast, the MA approach accounts for all the general equilibrium effects that arise fromthe construction of a transportation infrastructure. In Panel C, 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 valueof the gradient:∑𝑖∈𝑁 ∆𝑌𝑖
𝜕𝑌
𝐸[]=∑𝑖∈𝑁 ∆𝑀𝐴𝑖
𝜕𝑀𝐴
3

(3)

Note that the public transportation investment, the treatment, is often related to all economic variables ofthe model, and thus, is endogenous in the econometric sense. In this example, we assume away the kind of
empirical challenges researchers often face in a real empirical context (which we do address later), allowing
us to focus on other kinds of challenges.

11We estimate this gradient typically by regressing changes in the outcome of interest against
the changes in market access. This estimation strategy should work, in principle, as long as
the changes in MA are purely exogenous (i.e., random or as good as random). Hence, we are
tempted to make an argument that this strategy should also work if the changes in trade costs,
which cause the changes in MA, are random.
Borusyak and Hull (2023) clarifies, unfortunately, that this argument is not correct. Thatis, even if an assignment of transportation investments is random, changes in MA may not be
random. Figure 1 indeed demonstrates why this is the case. The changes in MA for each citydepend clearly on not only where the cost shocks occur but also where each city is located ina given network. In other words, the same (random) cost shock can generate different 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 that the conventional IV approach that relies on historical, planned, orinconsequential transportation routes is unlikely to work well because all these variables areplausibly correlated with the intrinsic network structure. We elaborate on this point further
in the next subsection.
II-C. 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 {𝝉𝑠 }𝑠∈𝑆 . Second, we calculate a sample analogue of the expected value of MA(EMA) that follow from this sequence of random draws: 𝜇𝑖 ≡ 𝐸𝑠 [𝑀𝐴𝑖 |𝝉𝑠 ] ≈1#𝑆∑𝑠∈𝑆 𝑀𝐴𝑖 (𝝉𝑠 ).

Third, we create a recentered instrument by calculating the difference between the observed
and the expected MA for each realized vector of 𝝉𝑠 : 𝑅𝑀𝐴𝑖 (𝝉𝑠 ) ≡ 𝑀𝐴𝑖 (𝝉𝑠 ) − 𝜇𝑖 .
By construction, this 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
to 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 arises in a complex
manner from the non-random exposure of cities in a given network structure to the “as-goodas-random” assignment.

12

Figure 2 illustrates the intuition for why and how this method works. Using the same
stylized transportation network in Figure 1, we take 300 random draws of transport costs 𝝉𝑠 .
To operationalize the randomization, we assume each draw represents a reduction in thetransport cost on the single connected route, or the pair of cities, (𝑖, 𝑗). There are a total ofeight possible route connections. Hence, we randomly select a city pair and take a random𝑠draw 𝜏𝑖𝑗
from a uniform distribution on (1.5, 9.5).

Figure 2 plots the results of this exercise. For each city, the realized changes in MA are
shown as dots while the expected change in MA in logs are shown as triangles. All values arein logged changes relative to the status quo. The figure demonstrates that each city has aunique distribution of the realized MA changes and the distributions are not centered aroundzero, despite that we take fully randomized draws of transport costs. Cities A and C tend toreceive positive MA growths 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 negative for cities 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 point isparticularly important to secure identification in our later empirical setup. In the meantime,by “re-centering” (i.e., subtracting the expected MA growth), we obtain the distributions ofpure MA shocks that are orthogonal to the non-random component with zero expected means.
purging out this structural dependency.
Figure 2. 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 1. For each city, the circles represent the realized MA changes andthe triangle denotes the expected MA change in logs.

13III. Background and Data
III-A. BackgroundOur study area covers the western Japan region, which consists of 23 prefectures with 615
municipalities. The western Japan is separated by the Seto Inland Sea into three major islands
the main island (called Honshu), the Shikoku Island, and the Kyushu Island (see Figure 3).
The region has two well-known metropolitan areas: the Kinki area and the Kitakyushu area.
The Kinki area consists of three major cities: Kyoto, Osaka and Kobe. The Kitakyushu area
consists of two major cities: Kitakyushu and Fukuoka. These five cities are labeled “Core”
cities (blue triangles) in Figure 3. The region also hosts several large cities along the Seto
Inland Sea, which served as a waterway for transporting large industrial materials such as
petroleum and iron ore. These relatively large regional cities are labeled “Large Peripheral
Cities” (blue circles) in Figure 3. 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 by 1973. Our study focuseson the period of public transportation investments that are designed to connect peripheral
cities to core cities in the late 1980s-1990s. 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,
the Shikoku region was separated from the main island by the Inland Sea. Before theconstruction of 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 (see Figure 3). The Central-Seto Bridge, completed in 1988,
connects Kagawa in the Shikoku region to Okayama in the Chugoku region. The East-Seto
Bridge, finished in 1998, and links Tokushima in the Shikoku, the Awaji Island, and Hyogo in
the Kinki region. Lastly, the West-Seto Bridge, completed in 1999, joins Ehime in the Shikoku
to Hiroshima in the Chugoku.

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. In the official language, cities with populations exceeding
500,000 are called ordinance-designated cities, and cities with populations exceeding 200,000 are called
central cities in Japan (The Ministry of Internal Affairs and Communications (MIC), 2021; 2023).

14

Figure 3. Major Highway Routes and the Great Seto Bridges in Western Japan

Notes: Area surrounded by blue is the Kinki region, green is the Chugoku region, red is the Shikoku Island,
and light brown is the Kyushu Island. The Kinki and Chugoku regions lie on Honshu Island. The prefecturesin red are those that directly bear the construction costs of the Great Seto Bridges. Five transport
megaprojects are also labeled: (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 4 shows the estimated impacts of bridges have on transport costs. Here we define
transport costs as the sum of travel time costs and transport user fees between all cities (amore detailed explanation of the costs is offered in Subsection III-B).5 The figure plots thechanges between 1985 and 1995 in logged values of transport costs. As shown, the largest
reduction in transport cost occurred in the Shikoku region, and the next largest was in the
inner main island, implying that the construction of the Great Seto Bridges is critical in
explaining the changes in transport costs during this period.
From its inception, the construction of the Bridges was highly contentious for several
reasons. First, the construction of the Bridges was one of the largest transport megaprojects
in Japan and worldwide (see Table 1). The total construction cost was estimated to be around70 billion dollars, and the construction of the three bridges took roughly 20 years. The cost
per kilometer is the highest compared to other large-scale public investments in the world,

5

We use the term "transport costs" instead of "trade costs" in our analysis because transport costs are often

the dominant factor in determining inter-city trade and migration (e.g., Duranton and Turner, 2012; Redding,2020).

15

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).
Figure 4. Transport Cost Reductions during the 1985-1995

Notes: This figure shows the change (in logarithm) in transport costs from 1985 to 1995. The thin line
represents highways built before 1985, and the thick line represents highways built between 1985 and 1995.
Transport costs are defined as the sum of time costs and transport user fees required to travel between cities,
added together across all cities.

Second, critics raised concerns about the Straw effect in the project. The Straw effect is thephenomenon that economic activity in peripheral cities in the Shikoku region would beswallowed by the core cities such as Osaka (Ihara et al., 2015). Such a concern is notunrealistic, and is indeed rooted in economic theory. Since Krugman (1991), economists havelong developed the theory of how cities grow. It is easy to construct an example in the stylized
spatial model where the core cities simply gain more, and the peripheral cities lose from atransportation project (as illustrated in Panel B, Figure 1).
The realization, and economic gains from the project and its distribution over space arequite important from the public finance standpoint. Currently, eight prefectures unevenly
bear the burden of the construction costs, but after 30 years of construction, these prefectures
have not been able to fully pay the debt. The cost shares of eight prefectures are also shownin Figure 3 as shaded areas, with darker red colors indicating higher cost shares. Comparing
Figure 3 and Figure 4, we see that the geographic distribution of the cost shares does not16

match that of the transport cost reductions. This highlights the importance of our question:
Does it match the distribution of economic gains?
Table 1. Construction Costs of Transportation Mega-Projects

Source: Board of Audit of Japan, 1998; Veditz, Leslie Allen, 1993; Thomas Müller and Isidor Baumann, 2016;
Al Neuharth, 2006; Faber, 2014; Bundesministerium für Verkehr, Bau und Stadtentwicklung, 2012; Ian
Mount, 2014; Richard Hollingham, 2015.

III-B. Data
We make use of three sets of data for our empirical analysis.
Transport Cost and Market Access Variables: We define the transport costs as the sum of thetravel time cost and the user fee between cities. Prior to the construction of the Bridges,
transportation in our study area and period heavily relied on local roads and ferries for thetransport 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 the travel time on theroad for road widths and road types (local vs. highway), accounting for differences in travel
speeds. For this, we use the General Traffic Volume Survey Results of the National Road andStreet Traffic Situation Survey by MLIT (2015). We then multiply the travel time by theofficial 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.
As a measure of the market size in equation (1), we use the population size of each city,following Donaldson and Hornbeck (2016), and Borusyak and Hull (2023). Our population
data come from the Statistics Bureau of the Ministry of Internal Affairs and Communications.
As we explain in the next section, we use 5-year lagged population sizes to avoid theendogeneity problem.
17

Outcome Variables: As our outcome variables of interest, we consider three variables:
manufacturing output, population, and employment. We use these as our outcomes not only
because all of these variables have been used in previous studies (e.g., Duranton and Turner,
2012; Faber, 2014; Baum-Snow et al., 2020; Jedwab and Storeygard, 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 is obtained the value of manufactured
product shipment from Industrial Statistics Survey (METI, 1980-2010). For both populationand employment, the data come from the Statistics Bureau of the Ministry of Internal Affairs
and Communications. We use the population in the labor force as a proxy for the employment
level of each city. Arguably, this is an inaccurate measure of employment in each city because
people might commute across cities and some people may be unemployed. Nonetheless, we
believe it is still a good proxy because the residential choices of people in the labor force arehighly correlated with employment demand in cities.
Control Variables: Our control variables include both the physical geographic characteristics(“first nature”) and the sociodemographic characteristics (“second nature”) aggregated at thecity level as of 1980 as our controls. These are known as the primary drivers of economic
growth (see Krugman (1993) and the quantitative spatial model in Appendix B). As thevariables that capture the first nature, we follow Faber (2014), and use the average slope, 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 the
National Land Numerical Information Download Service (MLIT, 1981; 1976). For thesociodemographic characteristics, we use the percentage of elderly residents, the percentageof employees in the manufacturing sector, and the manufactured product shipment value asof 1980. The data for these variables come from the Census Statistics (MIC, 1980) or
Industrial Statistics Survey (METI, 1980).
III-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 all samples
whereas the remaining columns report these for by city size as of 1980: small, large peripheral,and core cities (see Figure 3 and Section Ⅲ-A).

18

Table 2. Descriptive Statistics
The Size of Economy
Small Periphery
Overall
Mean

Lower

S.D.

Mean

Middle

S.D.

Mean

Upper

S.D.

Mean

Large Periphery

S.D.

Mean

S.D.

Core
Mean

S.D.

Panel A: Transport Cost and MA⊿ln Transport Cost

-0.47

(0.28)-0.48(0.31)-0.46(0.28)-0.47(0.26)-0.48(0.25)-0.38(0.01)⊿ln MA

0.13(0.23)

0.10(0.20)

0.11(0.18)

0.19(0.29)

0.19(0.25)

0.18(0.12)⊿EMA

0.08(0.14)

0.08(0.15)

0.08(0.14)

0.10(0.15)

0.07(0.09)

0.14(0.13)

Manufacturing Output (Bn.)

176

(441)

22.3

(45.7)

72.2

(108)

208

(204)

939

(733)

3110 (2139)

Population (K)

76.8

(178)

9.5

(5.0)

29.5

(10.3)

86.6(41.2)

418

(181)

1556

(607)

Employment (K)

37.8

(124)

4.2

(2.4)

12.7

(4.8)

38.9

(20.6)

198

(101)

1058

(800)

Manufacturing Output (Bn.)

173

(397)

29.1

(77.8)

80.3

(150)

210

(242)

910

(855)

2262 (1241)

Population (K)

76.8

(182)

9.1

(5.3)

29.2

(12.4)

85.7(42.9)

421

(186)

1591

(598)

Employment (K)

35.8

(111)

4.0

(2.6)

12.3

(5.5)

37.3

(20.0)

189(98.0)

960(649)

Average Elevation

238

(191)

317

(242)

225

(167)

196

(139)

125

(83.4)

158(136)

Average Land Steepness

12.8

(7.0)

15.4

(7.9)

12.7

(6.6)

11.4

(5.7)

8.3

(4.7)

8.8

(6.3)

Share of Water Area

0.10

(0.09)

0.08(0.09)

0.09(0.08)

0.11(0.10)

0.13(0.07)

0.11(0.07)

Share of Wetland Area

0.010

(0.05)

0.008 (0.040)

0.007 (0.027)

0.017 (0.067)

0.009 (0.035)

0.005 (0.008)

Share of Building Area

0.22

(0.19)

0.15(0.15)

0.20(0.16)

0.26

0.41(0.24)

0.43

Panel B: Outcpme Variablesi) 1995ii) 2005

Panel C: Control Variablesi) Geographical Conditions(0.20)(0.24)

ii) Socioeconomic Conditions
Manufacturing Output (Bn.)

131

(418)

12.6

(38.4)

39.0(67.4)

140

(156)

797

(767)

2884 (2315)

Share of Manuf. Labor

0.08(0.05)

0.07(0.07)

0.08(0.05)

0.09(0.05)

0.08(0.04)

0.09(0.03)

Share of Elderly

0.12(0.03)

0.14(0.03)

0.13(0.03)

0.11(0.03)

0.08(0.02)

0.09(0.01)

Observations

615

191

191

191

37

5

Notes: This table shows the means and variances of the variables by city size. This classification is defined
based on the 1980 population and the urban hierarchy criteria in Japan: Core is over 500,000 or more, Large
Periphery is below 500,000 and over 200,000, and Small Periphery is below 200,000. The small periphery is
subdivided into three groups: Lower is below the 33rd quantile, Middle is over the 33rd and below the 66th
quantile, and Upper is over the 66th quantile. Panel A shows the change in the MA variable from 1985 to
1995. Panel C shows the control variables as of 1980.

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
our empirical 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
19

impact. In 1995, our sample had the mean manufacturing output of approximately 176 billion
yen, the population size of roughly 77,000, and the population in labor force of 36,000 percity. Overall, cities in the Western Japan region are 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 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.

IV. Empirical Strategy
IV-A. The Recentered Market Access Approach
Our goal is to quantify the causal effect of the Great Seto Bridges on the economic growthof cities in the western Japan. As discussed in the previous sections, we are particularlyinterested in its heterogeneous impacts: which cities gain (or lose) more, and why? The
essential problem, as demonstrated in Section II, is that not just cities directly connected by
the Bridges but all cities are treated via the general equilibrium effects by this transportation
investment. Hence, the usual quasi-experimental methods, such as difference-in-differences
or regression-discontinuity methods, are unlikely to work well in our context. Instead, we startby the structural market access equation. This type of structural equation can be derived fromthe quantitative spatial model (Donaldson and Hornbeck, 2016; Redding, 2020; Jedwab andStoreygard, 2022):
ln 𝑌𝑖,𝑡 = 𝛽 ln 𝑀𝐴𝑖,𝑡 + 𝑊𝑖,𝑡 𝛾 + 𝜉𝑖 + 𝜂𝑖,𝑡 + 𝜖𝑖,𝑡 .

(4)where 𝑌𝑖,𝑡 is a variable that measures the economic size of city 𝑖 in year 𝑡, 𝑀𝐴𝑖,𝑡 is a market
access variable, 𝑊𝑖,𝑡 denotes a vector of controls, 𝜉𝑖 is time-invariant unobservables, 𝜂𝑖,𝑡 is
time-varying observables, and 𝜖𝑖,𝑡 is pure i.i.d. errors.
The literature has identified a number of empirical issues in estimating this equation, but
the bottom line is that to obtain an unbiased estimate of 𝛽, we need exogenous variation in20

the MA variable that is uncorrelated with uncontrolled unobservables. To ensure it, we
combine the following set of strategies.
The first strategy is to apply the conventional wisdom from the panel-data methods. First,
we follow Donaldson and Hornbeck (2016) and Jedwab and Storeygard (2022), and define
the MA variable for city 𝑖 at time 𝑡 by taking the first-order approximation to equation (1):−𝜃𝑀𝐴𝑖,𝑡 ≈ ∑ 𝜏𝑖𝑗,𝑡𝑃𝑂𝑃𝑗,𝑡−1 .

(5)

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 . Second, we take the first-differenceof equation (4) to eliminate the time-invariant unobservable term 𝜉𝑖 . Third, we run the
regression in lags, i.e., ∆(ln 𝑌𝑖,𝑡 ) are regressed on ∆(ln 𝑀𝐴𝑖,𝑡−1 ). This ensures that the MA
variable does not depend on the contemporaneous values of the outcome. In general, theeconomic benefits of a transportation megaproject take quite some time to materialize after
its construction. 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). Forth, regarding the MA variable in equation (5), we
take a time delay between transport cost and the size of the economy, i.e., 𝑃𝑂𝑃𝑗,𝑡−1 relative to𝜏𝑖𝑗,𝑡 , to avoid endogeneity problems coming from simultaneous changes in these. Fifth, we
include controls to absorb the remaining terms, ∆𝑊𝑖,𝑡 𝛾 and ∆𝜂𝑖,𝑡 . We explain the final
estimating equation in detail below.
Our second strategy is to exploit the quasi-experimental variation in transport costs inducedby the construction of the Great Seto Bridges (and other highway constructions during thesame period). As shown in Figure 4, the construction of the Central-Seto Bridge dramaticallyreduced the transport costs for not only the connected but all cities. This gives us theexogenous source of variation in the MA variable. Furthermore, much of this transport cost
reduction occurred in the late 1980s and in the Shikoku region, bringing the region’s transport
costs down to the same level as the other areas (Figure 5-A). Recall that only the Central-Seto
Bridge was constructed in 1988, and the other two bridges were not completed until 1998.
Figure 5-A reveals that the construction of the Central Seto bridge caused the sharp transport

6

We also report the results of our estimation using alternative trade elasticities in Appendix D. Intuitively

speaking, smaller values of 𝜃 assign more weights to transport costs while higher values assign more weights
to the population size. The trade elasticity of 𝜃 = 4.0 appears to hit the right balance as discussed in the
appendix as well as in Simonovska and Waugh (2014).

21

cost decline in cities surrounding the Seto bridges in the Chugoku and the Shikoku regions in
the late 1980s while the other two bridges in the late 1990s led to relatively small transport
cost reductions and their magnitudes were similar across regions.
Figure 5. Transport Cost Reductions and Changes in MA over Time

Notes: Panel A shows that transport cost reduction. Each line represents the median for the region. The Kinki
and Kyushu regions use averages excluding the core cities. Small dots are the medians for the prefectures.
Panel B shows that changes in the MA variable. Gray shaded areas represent the 1st to 99th percentile, 5th
to 95th percentile, and 25the to 75th percentile of all observers, in order from lightest to darkest color. Eachline represents the median for each region. Dots represent the raw values for cities.

In Panel B, we visually present the changes in logged MA relative to the values in 1985 asa result 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 percentileof all observations, respectively. Lines with markers represent the median values for five
representative regions while dots represent the values for individual cities. The figure delivers
several key messages that echo the points we made in Section II. First, there is no one-to-one
relationship between the changes in transport costs and those in the MA variable. Although
the magnitude of the transport costs reduction is far more pronounced in the Shikoku region,
there are winners and losers of MA in all regions. The correlation between the decline intransport costs and the growth in MA variable is not uniform, particularly in the Shikoku
region, where we see both increase and decrease in MA. Second, the transport cost reductions
have persistent impacts on the MA variable. We see a substantial increase in MA not only in1990 following the sharp decline in transport costs in 1988, but also in 1995 in the Chugoku
and Shikoku regions. The MA growth levels off only after 2000, with some changes during
22

1995-2005. Third, the median values indicate that core cities have a higher growth of the MA
possibly due to their original advantages of geographic and economic networks. This suggeststhat pre-existing network dynamics play a significant role in determining the endogenous
impacts of transportation cost reductions, as discussed in Section II-C.
This leads us to use the following estimation equation.
∆(ln 𝑌𝑖 )95−05 = 𝛽∆(ln 𝑀𝐴𝑖 )85−95 + 𝑔(𝑋𝑖,80 ) + Δ𝜖𝑖,95−05 .

(6)where 𝑔(𝑋𝑖,80 ) is the term that controls for the influence of the observables ∆𝑊𝑖,𝑡 and theunobserbales ∆𝜂𝑖,𝑡 . We use socioeconomic and geographic variables in 1980 as pre-treatment
controls. Note that this specification is similar to that of Duranton and Turner (2012) or Faber(2014), but differs from them in that we don’t use the difference-in-difference(DID) design
directly in the estimating equation and that we instead use the DID design only as the sourceof variation in our MA variable. In the nutshell, this equation compares the outcome trendsof cities that experience larger (or positive) shocks in MA against those of cities with smaller(or negative) shocks, controlling for other economic factors.
Our third strategy is to apply the recentered instrumental variable approach (Borusyak and
Hull, 2023) in equation (6) to address the fundamental endogeneity problem. Economists are
increasingly concerned with the endogeneity of the MA variable, even after taking all the steps
and the quasi-experimental variation above, that may arise from reverse causality or selection
bias. That is, public infrastructure investments are made in cities where higher (or lower)
economic growth is expected. This kind of endogeneity is hard to address because theselection hinges on the future expectation, and as a result, the reverse causality might still
exist even if we take long lags in regression analysis. In the earlier literature, economists haveused an instrumental variable (IV) approach, relying on three types of instruments: plannedroute IV (e.g., Baum-Snow, 2007), historical route IV (e.g., Duranton and Turner, 2012), andoptimal least cost path IV (Faber, 2014). However, economists have long been concerned withthe validity of such instruments.
Fortunately, Borusyak and Hull (2023) also offered a possible solution, called therecentered instrumental variable approach. The fundamental problem for the earlier literatureis that a city’s location in the intrinsic structure of economic network is inherently endogenous,
so any variable constructed from the network structure, whether it is based on the far past,
the first-nature, or the optimization algorithm, cannot be plausibly exogenous. Borusyak andHull (2023) convincingly shows why this is the case, clarifying the nature source of bias.
We operationalize Borusyak-Hull’s RMA approach discussed in Section II-C as follows. We
first randomly select the location and timing of transportation investments in the western
23

Japan, and calculate the corresponding changes in transport costs and MA variable.7 We thencalculate the expected MA growth for each city as the average of the 10-year growth of realized
pre-treatment MAs in log:

∆𝐸𝑀𝐴𝑖,85−95 ≡ 𝐸[∆(ln 𝑀𝐴𝑖 )85−95 ] =1𝑠̃ 𝑖,95∑[ln 𝑀𝐴− ln 𝑀𝐴𝑖,85 ]
#𝑆

(7)

𝑠∈𝑆

𝑠
̃ 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 (RMA) growth as the difference between the observed changes in MA and EMA
growth:∆𝑅𝑀𝐴𝑖,85−95 ≡ ∆(ln 𝑀𝐴𝑖 )85−95 − ∆𝐸𝑀𝐴𝑖,85−95.

(8)

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 variable as a valid instrument for MA because it must
be correlated with MA (relevance) and is uncorrelated with the unobserved error (exogeneity).
On the latter, note that the RMA growth instrument has an intuitive appeal for empiricaleconomists who are concerned concerns about bias arising from reverse causality or selection--- the government agencies may decide when and where to make public transportation
investments forecasting the excepted growth of cities in different areas, but ∆𝑅𝑀𝐴𝑖 purges
out that expected growth component from the MA variable. Some may wonder if thisargument holds, then we may actually use ∆𝐸𝑀𝐴𝑖 as a control in the OLS regression so as toabsorb the expected growth component. 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 to similar estimates. In Section V, we show that this is indeed thecase with ours too.

7

We use the population size of each city as of 1980 in calculating the MA for each draw. In each draw, we

randomly select a highway segment from the set of highway segments that were built between 1985 and 2015
with a 50% probability. Due to high computational burden, we only repeat 100 times of random draws.

24

IV-B. The Instrument Validity
This section discusses the spatial as well as temporal distribution of both the MA growth andthe RMA growth variables in our study’s context. Figure 6 shows the changes in logged valuesof MA (Panel A) and in logged values of RMA (Panel B) over the period of 1985-1995.
First, the spatial distribution of changes in market access (Figure 6-A) does not necessarily
coincide with that of transport costs (Figure 4). There is an indication that MA variable
increase significantly in cities where highways were constructed. For instance, we see
substantial increases in the MA variable in cities near the Great Seto Bridges. However, somecities (e.g., northeastern Kyushu) increase their market access levels even though no highways
were built during this period while some other cities (e.g., cities near Osaka) decrease theirmarket access even though highways were built. Furthermore, cities in the southern Shikoku
region lost market access despite that transport costs for these cities substantially declined.
This reinforces the importance of accounting for the general equilibrium effects of public
infrastructure investments discussed in Section II.
Figure 6. Spatial Variation in Raw and Recentered Market Access

Notes: Panel A shows the logarithm changes in MA variable over 1985-1995: we use lagged values of
population to construct the MA variable based on equation (5). For Panel B, we construct the logarithm
changes in RMA by using the population as of 1980 for the changes in EMA based on equations (7) and (8).

Second, Figure 6-B indicates that the spatial distribution of changes in MA matches that ofchanges in RMA, implying these are spatially highly correlated and that RMA growth is indeed
a relevant instrument for changes in MA variable. However, the figure also illustrates thatsome of these changes in market access are unexpected, in the sense that the changes exceed25

(or fall short of) the expected growth of market access for some cities. For example, thenorthern Shikoku and the southern Chugoku regions experienced larger changes in RMA
growth than others, implying that the market access growth due to the construction of theGreat Seto Bridges is not something we expect from the intrinsic structure of the economic
geography. On the other hand, the core cities, such as Osaka, received the negative shock inmarket access after netting out the expected growth. These cities, with large economic size,
tended to grow faster than others, however, the actual growth arising from the constructionof the Bridges and other highways during the period fell short of that expectation. This implies
that the observed variation in MA due to the construction of the Great Seto Bridges and otherhighways during the period indeed has unexpected, exogenous variation in MA, which we can
exploit in the identification of its economic impact. We report the results of the first stage in
two-stage least squares regression in Appendix C.

V. Estimation Results
V-A. OLS Regression
We first present the results on the OLS regression of equation (6) in Table 3. We use threeoutcome variables, manufacturing output (the first column), population size (the second
column), and employment (the third column), to gauge the extent of the impact on city-level
economic growth. We also use the following city-level variables as of 1980 as controls: theaverage land steepness, average elevation, percentage of building areas, percentage of water
areas, percentage of wetlands, and percentage of elderly and manufacturing labors, andmeasures of economic size as of 1980. These are meant to capture the influence of city-levelunobservables on the economic growth trends. To allow for the possibility that the impulse
responses may vary over time, we also run the same regressions varying the time periods tomeasure the economic impact: ∆(ln 𝑌𝑖 )90−00 (Panel A), ∆(ln 𝑌𝑖 )95−05 (Panel B), and∆(ln 𝑌𝑖 )00−10 (Panel C). Our preferred specification is the one with ∆(ln 𝑌𝑖 )95−05 in Panel B,
for reasons we outlined in Section IV. All regressions use full controls (see the table footnotes
for details).

26

Table 3. OLS Regression Results

Notes: All regressions use the following city-level variables as of 1980 as controls: average steepness, average
elevation, percentage of building areas, percentage of water areas, percentage of wetlands, percentage ofelderly, percentage of manufacturing employees, manufacturing output and population size as of 1980. ***1%, ** 5%, * 10% significance levels and parentheses denote cluster robust standard errors, clustered at thecity level.

We have two important take-aways from Table 3. First, the changes in MA during the 198595 period have statistically significant, yet economically small impacts on economic outcomes.
With our preferred specifications using 1995-2005 as the outcome period (Panel B), the point
estimates are 0.154, 0.032, and a 0.036, respectively, for manufacturing output, population,and employment. Standard errors are larger for regressions on manufacturing output than on
population or employment. Using the mean values reported in Table 2, these estimates imply
that the highway construction during this period lead to, on average, 2.03%, 0.42%, and
0.48% changes in manufacturing output, population, and employment during the same period.
Second, the estimated impulse responses are roughly consistent across different periods,
except for the manufacturing output using 1990-2000 as the outcome period. As shown in
Figure 5-B, the changes in MA are serially correlated in somewhat unpredictable ways due topersistency and feedback loops of the transport-cost impacts. The outcomes in the 1990-2000
period may be responding to the changes in MA in the periods other than 1985-1995 that are27not used as our treatment variable. Even with our clean identification strategy, we cannot
completely eliminate such intertemporal correlations. This supports our rational for using
1995-2005 as our preferred outcome period since it is the period when the influence of otherperiods’ MA changes is expected to be the smallest.
V-B. Recentered Market Access Approach
Next, we present the results of the RMA approach in Table 4, using the same set of outcome
variables and controls as in Table 3. We make use of the RMA approach in two ways, a la
Borusyak and Hull (2023): (a) using ∆𝐸𝑀𝐴 as an additional control and (b) using ∆𝑅𝑀𝐴 as
an instrument for ∆ ln 𝑀𝐴. If the approach works, we expect the estimates would be similar.
For each outcome, the first column displays the OLS results with a full set of controls and∆𝐸𝑀𝐴, and the second column shows with ones and ∆𝑅𝑀𝐴 as an instrument. As shown, ourresults are consistent with Borusyak and Hull (2023) in that our IV results with RMA growth
are quantitatively quite similar to the OLS results with EMA growth as an additional control.
Hence, we discuss our results mainly using the estimates from the RMA-IV regression.
Our results indicate that the use of the RMA approach tends to increase the magnitude ofthe estimates, without changing their signs, reinforcing the impacts of transportation
investments. With our preferred specifications using 1995-2005 as the outcome period, ourpoint estimates are 0.197, 0.039, and 0.056, respectively, for manufacturing output,
population, and employment. Using the mean values reported in Table 2, these estimates
imply that the highway construction during this period lead to, on average, 2.60%, 0.51%,
and 0.74% changes in manufacturing output, population, and employment, respectively,
during the 1995-2005 period. For all regressions, the IV estimates are larger in magnitude
than the OLS estimates, implying the existence of a downward bias on the OLS estimates.
This is somewhat unexpected, but is indeed consistent with econometric theory.8 The sign ofthe coefficient on EMA growth is negative. This implies that the non-random exposure in thecurrent setup induces non-random structural errors in equation (6) that are negatively
correlated with EMA growth. Hence, if uncontrolled, this causes a downward bias in theestimates.

8

Borusyak and Hull (2023) use Chinese high-speed rail expansion as an application. Their IV estimates on

the market access variable are generally smaller in magnitude than the OLS estimates, but their coefficientson EMA growth are positive.

28

Table 4. The Results of the Recentered Market Access Approach

Notes: For each outcome, the first column presents the results of OLS regression with ∆𝐸𝑀𝐴, and the second
column reports the results of 2SLS with ∆𝑅𝑀𝐴 as the instrument. We use the same set of controls as in Table
3. *** 1%, ** 5%, * 10% significance levels and parentheses denote cluster robust standard errors, clusteredat the city level.

V-C. Heterogeneity and Robustness
The regression equation (6) estimates the average elasticity of a city’s economic size withrespect to the market access over cities. This is fine if the elasticity is homogenous or theheterogeneity is uncorrelated with changes in MA. However, there is another source ofconcern. To calculate our MA variable in estimating equation (6), we use equation (5), which
excludes city 𝑖’s own population whereas the true MA variable in equation (1) is definedrecursively using 𝑖’s own population. This is done to avoid the direct influence of the city’s

29

own economic outcome on the MA variable, which would cause another endogeneity issue.9
Yet, such omission may create unobserved heterogeneity in economic impacts that may becorrelated with the changes in MA. To address such a concern, we estimate a version ofequation (6) 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 .

(9)𝑟where 𝕀[∙] is an indicator, which equals one if city 𝑖 belongs to a group 𝐺𝑟 . We classify cities
into three groups (heterogeneity dummies) using the following three alternatives: the tertiles
of manufacturing output as of 1980, and population size as of 1980. Estimating these
regressions using the IV approach requires the same number of instruments as the number ofdummies accounting for the heterogeneity. We could potentially interact the RMA with thesedummies. Such a strategy is known to result in implausible estimates in practice. Therefore,
we instead use the EMA as a control in equation (9).
Table E2 presents the results of estimating equation (9) using the RMA approach. The
table also reports the p-values of the joint hypothesis test of the null that there is no interaction
effect. We have several take-aways from the results. First, the p-values are sufficiently small
for population and employment, leading to rejection of the null hypothesis. This suggests thepossibility of heterogeneous effects on these two outcomes. On the other hand, formanufacturing output, our concerns about interaction effects are small. Second, the second
and the third tertiles tend to have larger, statistically more significant effects than the firsttertile for all outcomes. This implies that cities that enjoy economic abundance prior to thetransportation investment tend to gain more from a given increase in MA. Interestingly, insome specifications, the second tertile has larger effects than the third tertile on some
economic outcomes. The cities in the second tertile tend to be large peripheral cities. Hence,
the results are consistent with the analysis in the next section, which reveals that large
peripheral cities gain more from the megaproject. With these, we conclude that although there

9

Donaldson and Hornbeck (2016) investigate the extent of such bias by estimating the same regressions

using equation (1) versus equation (5). In Table E1 of Appendix E, we present the OLS regression results
using equation (1), but using the lagged values of population, following Donaldson and Hornbeck (2016).
All estimates become positive, larger in magnitude, and more statistically significant, most likely capturing
the spurious correlation via each city’s own population size between the recursively calculated MA and theoutcome variables.

30

may be a concern for existence of heterogenous treatment effects, the concern about
manufacturing output, which is utilized in the subsequent analysis, is not too serious, andhence, we continue to use the estimates from regression (6).
VI. The Economic Impact of the Great Seto Bridges 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 ways.
The first approach is to simply plot the predicted changes in the cities’ economic outcomesover the 1995-2005 period against the cities’ population sizes as of 1980. For this, we simply
make use of the RMA-IV regression results from columns 2, 4, and 6 in Table 4. This approach
simply 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 questions
like “Do larger cities tend to grow more with transport projects?”. This approach is arguably
too simplistic for three reasons: the estimated changes include the effects of all highways
constructed during the 1985-1995 period, are not in comparison with the counterfactual in
the absence of the megaproject, and do not account for the costs of construction.
Our second approach thus attempts to estimate the net benefit of the transport megaproject
for each city relative to the counterfactual in the absence of the Great Seto Bridge. To do so,
we proceed in four steps. First, we re-calculate the transport costs, removing the Central Seto
Bridge, which is the only bridge constructed during the 1985-1995 period. Second, we makeuse of the properties of the QSM and simulate the resulting changes in the market accesslevels for all cities in our sample that would occur during the post-construction (1995-2005)
period. Third, we make use of the estimates from the RMA-IV regression in Table 4 andestimate the outcome changes in response to changes in market access in two states of theworld: the observed state with the construction of the Central Seto Bridge and thecounterfactual state without the Bridge. Lastly, we apply our estimates of the cost shares of
the Central Seto Bridge to arrive at the estimates of the net benefit for each city.
VI-A. Distribution of Economic Impacts of Highway Construction
Figure 7 displays the results of the first approach, for each of the three outcomes.10 The
figure shows a clear sign of the agglomeration benefits: larger cities, prior to the transportation

10

In calculating the market access levels, we use the observed values of population in 1980 and 1990 in Figure

7. We provide the results using the pre-treatment values as of 1980 in Appendix F.

31

projects, tend to attract more economic activity than smaller cities. The magnitude of thiseffect seems economically large. At the median, the largest cities (the fifth category) gainroughly 9.5 billion yen in manufacturing output, 9,400 persons in population size, and 9,000
persons in labor force over the 1995-2005 period. The magnitude of these impacts graduallydiminishes for smaller cities, yet even the smallest cities show gains, albeit modest: at themedian, these cities gain 63 million yen in manufacturing output, 15 persons in population,and 9 persons in labor force. Hence, from the first exercise, we conclude that the highway
construction during the 1985-1995 period had positive impacts for all cities, yet the impacts
are distributed more toward larger cities than smaller cities.
Figure 7. Distribution of Economic Impacts across Cities of Different Population Sizes

Notes: The figure classifies cities based on their population sizes as of 1980 as follows: Cities with population
size of 500,000 or more (size 5), cities with 200,000 or more (size 4), and tertiles of small peripheral cities(size 1-3). This classification follows the MIC guideline. Each point represents the estimated impacts overthe 1995-2005 period. The black line shows the median of each the size of the economy.

There is another important take-away from this exercise. In the figure, the impacts on fourlarge peripheral cities (Hiroshima, Okayama, Matsuyama, and Takamatsu) as well as on threecore cities (Osaka, Kobe, and Fukuoka) are labeled with triangles. The large peripheral cities
are the cities that are of critical importance to the regional economy, and the critics of theGreat Seto Bridge are particularly concerned with the straw effect on these cities: theeconomic activity may be drawn from these central peripheral cities to the core cities. The
32

figure demonstrates that this concern did not materialize. In fact, these peripheral cities areamong the largest gainers from highway construction during this period. The impacts on thesecities manufacturing output are 290 billion, 240 billion, 19 billion and 11 billion yen,respectively, for Hiroshima, Okayama, Takamatsu, and Matsuyama. Several reasons explain
this. First, these peripheral cities are sufficiently large prior to the highway construction, andhence, they enjoy the gains from agglomeration forces just like other large cities. Second,
these cities are located near the Central Seto Bridge, and thus, are likely to be the direct
beneficiaries of the Bridge. Arguably, this approach is overly simplistic as discussed above.
VI-B. Distribution of Net Benefits of the Central Seto Bridge
In this section, we attempt to estimate the net benefits of the Central-Seto Bridge for all
cities in the sample, and compare the distribution of the net benefits against the initial
population sizes of these cities. Recall that only the Central Seto Bridge was completed by
1988, the other two bridges were not completed until the late 1990s, and hence, we assume
away the effect of the other bridges in this exercise.
As the first step, Figure 8 plots the estimated impacts of the Central Seto Bridge alone on
three outcomes: (a) manufacturing output, (b) population, and (c) employment, for each
population size as of 1980, in a manner analogous to Figure 7. To recap, as explained above,these impacts are obtained by re-calculating the transport costs without the construction of
the Central Seto Bridge, simulating the market access using the QSM, applying the RMA-IV
estimates from Table 4, and taking the difference between the outcomes predicted at thiscounterfactual state versus those predicted at the observed state for the period of 1995-2005.
Three important observations arise from Figure 8. First, the magnitudes of the estimatedimpacts are much smaller in Figure 8 than in Figure 7, implying that the pure effect of theCentral Seto Bridge is small and that much of the effects in Figure 7 come from the combined
effect of the other highways and the Central Seto Bridge. This also signifies the importanceof our approach in accounting for the general equilibrium impacts. Second, the estimatedimpacts are not necessarily monotonically increasing in initial population size (see Panel A.
manufacturing output), though there is a tendency for the impacts are larger for larger cities.
Third, major beneficiaries of the Central Seto Bridge are the cities located near the Bridge in
the Seto Inland Sea region, in particular, the Awaji Island and major peripheral cities within
the Shikoku region. The difference between Figure 7 and 8 suggests that the construction of
the Central Seto Bridge has a relatively large impact on these areas, and that its construction
substantially contributes to their economic growth.

33

Figure 8. Counterfactual Impacts of the Central Seto Bridge
Across Cities of Different Population Sizes

Notes: The figure uses the same classification of population size as in Figure 7. Each point represents thecounterfactual impact of the construction of the Central Seto Bridge over 1995-2005. The black line shows
the median of each the size of the economy.

Our next step is to convert these counterfactual impacts into monetary values in net benefits.
To do so, we apply the following simplifying accounting rules. First, on the cost side, we makeuse of the publicly available information on the construction cost of the Central Seto Bridge
and its cost shares by prefectures from MLIT (2010). It is estimated that the Central Seto
Bridge alone costs about 1.13 trillion yen in 1988 JPY. Unfortunately, we do not have the costburden at the city level. However, the construction cost burden born by each prefecture is
eventually collected through taxes from each resident within the prefecture. Hence, forsimplicity, we assume the cost burden is shared in proportion to each city's population size asof 1985. Second, on the benefit side, we assume that the primary benefit of the project arisesin 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 (non-pecuniary) benefits. Thisis arguably a simplifying assumption. But when discussing the benefit of this kind of transport
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.
34

There is one subtle issue, in making use of our regression/simulation results, however. On
one hand, the Bridge has a useful life of 100 years (MLIT, 2004). Hence, ideally, we would
like to do the cost-benefit analysis, estimating the benefit flows over the entire life years of
the Bridge. On the other hand, our preferred estimates get at the counterfactual impacts of
the Central Seto Bridge over the period of 1995-2005 only. Extending the estimated effectbeyond this period is spurious, for reasons similar to why we chose this period as the impulse
response window in our main regression. As a compromise, we estimate the net benefit forthe period of 1995-2005, assuming that construction costs should be paid equally over the 100
years of useful life,11 and applying the social discount rate of 4% per year following Circular4 guideline (which is the same as the social discount rate used in Japan). To be precise, thenet present value of the Bridge for city 𝑖 prorated for the 1995-2005 period is calculated as:

2005

𝑁𝐵𝑖,88−05 = ∑ (𝑡=19881 𝑡−1988)[𝐵𝑖,𝑡 − 𝐶𝑖,𝑡 ]
1+𝜌

(𝑌̂
)/10
𝐵𝑖,𝑡 ≅ { 𝑖,95−05
0𝐶𝑖,𝑡 ≅ 𝐶̂𝑖 /100

for 𝑡 > 1995
for 𝑡 ≤ 1995
for ∀ 𝑡

(10𝑎)
(10𝑏)
(10𝑐)

where 𝜌 is the social discount rate, 𝐶̂𝑖 is the estimated cost burden for city 𝑖 in 1988 JPY, and∆𝑌̂𝑖,95−05 is the estimated impact of the Bridge on city 𝑖’s manufacturing output over the periodof 1995-2005. In principle, this accounting method should yield roughly the same result asthat of applying the same benefit flow over the entire life of the Bridge against the one-time
construction cost.
Figure 9 plots the estimated net benefits for cities against their population size as of 1980,
in a manner analogous to Figures 7 and 8. There are three striking messages from the figure.
First, surprisingly, core cities become net losers despite that they were expected to be winners
from the Bridge. This occurs because these core cities incur larger cost burdens compared toother peripheral cities due to their disproportionately large population sizes. Second, large
peripheral cities along the Seto Inland Sea region, namely, Awaji, Hiroshima, Matsuyama,
Okayama, and Takamatsu, stand out as clear winners. While these cities also bear significant
cost burdens, the economic benefits that they reap from the Bridge are substantially greaterthan other cities (see Figure 8). Finally, smaller peripheral cities face low-cost burdens but
also experience small economic gains, leading to slightly negative net benefits on average.
Hence, our findings suggest that contrary to some critics’ expectations, the Straw-effect11This is equivalent to applying the depreciation rate of 1% per year.

35phenomenon did not quite occur, and instead, the large peripheral cities were net winners
when cost burdens are accounted for. Our results thus signify a more complex picture of the
distribution of economic benefits arising from a transport megaproject.
Figure 9. Distribution of Net Benefits Across Cities of Different Population Sizes

Notes: The figure uses the same classification of population size as in Figure 7. Each point represents thecounterfactual impact of the construction of the Central Seto Bridge over 1995-2005. The black line shows
the median of each the size of the economy.

To elaborate on this point, we also provide three additional maps in Appendix G showing
the geographic distribution of net benefits (Panel A, Figure G1), total costs (Panel B), andtotal benefits (Panel C) across cities in the Western region. The maps indicate that thebenefits mostly fall on cities around the Inland Sea region while the costs are distributed acrossall cities. As a result, cities enjoying positive net benefits are geographically concentrated in
the Inland Sea region. The geographic distribution of benefits is closely linked to the network
structures reflecting both natural and socio-geographic factors prior to the construction of theCentral-Seto Bridge: pre-existing transport costs, agglomeration forces due to city size, andproximity to the Bridge.
Lastly, let us touch on the impact on the aggregate welfare. Our back-of-envelope estimatesindicate that the Central-Seto Bridge generates an aggregate net benefit of approximately
12.5 billion yen for the Western Japan region over the analysis period of 1988-2005. Thus,
the winners’ net gains (such as Awaji, Matsuyama, Okayama, and Takamatsu) outweigh thelosers’ net losses (such as for Fukuoka, Hiroshima, Kobe, and Osaka) despite the facts that it36

had prohibitively large construction costs and that many cities are net losers of the project.
However, there are two things to note about our results. First, the benefits may be underestimated because we only used the estimated impact on manufacturing output over the 19952005 period. Arguably, the Bridge may stimulate growth of cities over a much longer time
horizon and may also bring other pecuniary and non-pecuniary benefits to the cities. On theother hand, the net benefits may be over-estimated because we only consider the construction
costs, ignoring other kinds of costs such as a potential increase in transport-related air
pollution. Nonetheless, our findings cast an important question for public finance economists:
How should design and finance a transport megaproject when there are clear winners andlosers from the project. This question is important not only for low- and middle-income
economies but also maturing economies expected to face a rapid demographic transition.

VII. Conclusion
We empirically examine the phenomenon known as the Straw effect, in the unique historicalcontext during the rapid motorization period in Japan. That is, core cities may gain more fromlarge transportation investments than peripheral cities do, or even worse, the peripheral cities
may even lose from such investments. As a first step, we estimate the heterogenous causal
effects of a large public transportation project, using the construction of the Great Seto Bridge,
one of the largest transport megaprojects around the world, as the source of exogenous
variation in transport costs and also employing the newly developed recentered instrumental
variable method in the market access approach. The estimated impacts are used to evaluate
the empirical distribution of winners and losers from the Bridge construction and to gauge
the extent of the Straw effect.
Our results indicate that virtually all cities, nor just large cities or cities nearby the Bridge,
experience positive growth in manufacturing output, population, and employment. But thegrowth rate is higher for cities that were larger (in population size) before the construction of
the Bridge and other highways, suggesting the existence of agglomeration bonus. This result
is consistent with the economy theory, and also in some sense, the Straw-effect hypothesis.
We do not find, however, that the Straw effect occurs in the original form claimed by somecritics. Some of the large peripheral cities are estimated to experience higher economic growth
purely attributable to the Bridge construction than core cities. More importantly, when thecost burdens of the Bridge were considered, core cities are estimated to lose from the Bridge
construction while these large peripheral cities are estimated to gain substantially. Although
the aggregate net benefit for the Western Japan region is positive, a large number of cities are37

estimated to be net losers, with large parts of net benefits concentrated in a much smaller
number of cities. This implies that the financing scheme should have been more carefully
designed. How to optimize a public transportation project and its cost sharing rule when theireconomic benefits are heterogenous is an important public finance question. Due to spaceand data limitations, we do not explore this question and it is hence left for future research.

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42

Appendix A. Related LiteratureThis paper is related to five major strands of literature.
The first studies focus on the heterogeneous effects of transportation constructions on
economic activity. It is most closely related to investigating impact on disadvantaged
communities (Chandra and Thompson, 2000, Faber, 2014; Storeygard, 2016; Asher andNovosad, 2020; Baum-Snow et al., 2020; Jedwab and Storeygard, 2022). Theoretically,
starting with Krugman's Core-Periphery model (1991), it is shown that lower trade costs havea positive impact on core cities, while hurting peripheral cities. Empirically, based on thistheoretical background, Jadwab and Storeygard (2022), which analyze peripheral cities inAfrica, finds a larger positive impact for peripheral cities, while Asher and Novosad (2020),
who analyze peripheral cities in India, indicates that road construction has little effect on
population growth. Contrary to these, Faber (2014) and Baum-Snow et al. (2020) show alarger positive impact on core cities through transportation constructions. In western Japan,
we focus on, we can use the structure of the core cities and the peripheral cities, at the sametime, the timing of highway construction connecting them, and the non-marginal quasiexperimental variation of bridge construction. We investigate whether the straw-effectphenomenon, which is a heterogeneous effect on economic activity, appears or not.
Second, it is related to studies, which is extremely rich literature, that empirically evaluate
the construction of transportation infrastructure on economic activity (Baum-Snow, 2007;
Duranton and Turner, 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, 2018; Mori and Takeda, 2019; Asher and Novosad, 2020; BaumSnow et al., 2020; Banerjee et al., 2020; Jedwab and Storeygard, 2022). Baum-Snow (2007)
investigates the effects of inter-state highway on suburbanization in the U.S., Duranton et al.
(2014) indicates inter-state highway has a large effect on the weight of city exports in the U.S.,
and Donaldson (2018) shows the effects of railroad construction on inter-city trade andmarket integration in India. Duranton and Turner (2012), Ghani et al. (2015), Banerjee et al.
(2020), and other literature above analyze the impact on city economic growth. These papers
use continental countries such as the U.S., China, and India as their study area, unlike Japan.
Since we use a quasi-experimental variation that takes advantage of the geographicaluniqueness of land division by inland seas, it could lead to a substantial reduction in transport
costs.
Third, it relates to literature that use the market access (MA) approach (Davis andWeinstein, 2003; Hanson, 2005; Breinlich, 2006; Redding and Sturm, 2008; Nakajima, 2008;
Head and Mayer, 2011; Donaldson and Hornbeck, 2016; Jaworski and Kitchens, 2018; Mori
43and Takeda, 2019; Jedwab and Storeygard, 2022). For example, Redding and Sturm (2008)
uses an exogenous change in the unification of East and West Germany, and Nakajima (2008)
uses an exogenous change in the economic separation of Japan and Korea to show theimportance of market access. Donaldson and Hornbeck (2016), and Jedwab and Storeygard(2022) analyze the impact of changes in market access through transportation network
construction. However, these papers may not address endogenous bias due to MA variable.
We address this issue by estimating unbiased causal effects by using the instrumental variable
proposed in Borusyak and Hull (2023).
Fourth, it is also related to the theoretical literature on quantitative spatial models (QSE)(Allen and Arkolakis, 2014; Redding and Turner, 2015; Redding and Rossi-Hansberg, 2017;
Redding, 2020). The model is based on gravity equations for goods and population flows,
allowing for empirical analysis of general equilibrium effects. Theoretical studies of new
economic geography (e.g., Fujita et al. 1999; Fujita and Thisse, 2002; Baldwin et al. 2003)
show that the love of variety, increasing return to scale, and transport costs lead toheterogeneous geographic distribution of economic activity. However, this theoretical model
is complex and thus diverges from empirical estimation. QSM is an approach that solves such
empirical problems. Redding and Turner (2015) show that the general equilibrium effects oftransportation infrastructure development on wages, population, trade, and industry
composition based on Krugman (1991). Allen and Arkolakis (2014), on the other hand, baseits model on a perfect competition Armington with differentiated varieties rather than thehomogeneous tradable goods and monopolistically competitive market assumed in Krugman(1991). Redding and Rossi-Hansberg (2017), and Redding (2020) review these series ofstudies.
Lastly, it relates to studies that investigate how the burden of large public transporttransportation investments should be shared among stakeholders (Anguera, 2006; Boardman
et al., 2018). To the best of our knowledge, little research exists that describes which parties
should bear the burden of public investment costs and debt, and to what extent. Moreover,many of these studies show the validity of ex-post evaluation of public megaprojects by using
engineering methods, not economic methods. For example, Anguera (2006) provides a costbenefit evaluation of the construction of the Channel Tunnel connecting the U.K. and France.
However, its total funding burden in the U.K. was less than the estimated total benefits,suggesting that the overall U.K. economy would be better off without its construction.
Boardman et al. (2018) provide a case study of a cost-benefit analysis of a mining development
project in British Columbia, Canada. The primary beneficiaries of the project are theCanadian National Railway and the federal government.

44

Appendix B. Quantitative Spatial Model and Market AccessB.1. A Brief Summary of 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 terrain and climate, while second-nature geography contains human-made elements
like politics and policy. These aspects play an important role in shaping the spatial interactionof economic activities across cities. In the QSM, economic activities are influenced by both
agglomeration forces and dispersion forces. This interaction determines the spatial
distribution of economic activities across locations.
In the model, an economy consists of locations, 𝑖, 𝑛 ∈ 𝑁 , all locations connected bytransportation networks. Each location has a worker, and there are 𝐿 workers (consumers) in
the overall economy. These consumers have a "love of variety" preference and consume bothtradable and non-tradable goods. They are perfectly geographically mobile. Producers
produce tradable goods under conditions of monopolistic competition with one industry andincreasing returns to scale. Productivity, amenity, bilateral trade costs, and supply of floor
space (non-tradable goods) are given exogenously.
A consumer has a utility function:

𝑈𝑛 = (𝐶𝑛 𝛼 𝐻𝑛 1−𝛼
) (
)
𝐵𝑛 ,
𝛼
1−𝛼

𝜎

𝜎−1
𝜎−1
𝑀
where 𝐶𝑛 = [∑𝑖∈𝑁 ∫0 𝑖 𝑐𝑛𝑖 (𝜓) 𝜎 𝑑𝜓] is the consumption of tradable goods in location 𝑛, 𝑀𝑖

is the number of variety produced in location 𝑖, 𝐻𝑛 is the consumption of non-tradable good,and 𝐵𝑛 is amenity (e.g., quality, safety). The indirect utility function is derived as follows12:

𝑉𝑛 =
𝑀

1
1−𝜎

where 𝑃𝑛 = [∑𝑖∈𝑁 ∫0 𝑖 𝑝𝑛𝑖 (𝜓)1−𝜎 𝑑𝜓]

𝐵𝑛 𝑣𝑛,
𝑃𝑛𝛼 𝑄𝑛1−𝛼1≡ 𝐶𝑀𝐴1−𝜎is price index, 𝜓 is a variety and 𝜎 is an𝑛elasticity of substitution, and 𝑄𝑛 is the price of non-tradable good.

12(B. 1)The budget constraint for consumer is given by 𝑣𝑛 = 𝑃𝑛 𝐶𝑛 + 𝑄𝑛 𝐻𝑛 .

45

Next, based on the consumer's partial utility maximization problem, a consumer consumes
the following amount of tradable goods:

𝑐𝑛𝑖 (𝜓) =𝑝𝑛𝑖 (𝜓)−𝜎 𝛼𝑣𝑛
𝑝𝑛𝑖 (𝜓)
=(
)
−𝜎
𝑃𝑛
𝑃𝑛
𝑃𝑛

−𝜎

𝐶𝑛 .

(B. 2)

Consumers increase (decrease) their consumption of a variety 𝜓 when the relative price ofvariety 𝜓 is low (higher). Based on equation (B.2), we can get the optimal price by solving the
producer's profit maximization problem:

𝜎 𝑤𝑖 𝜏𝑛𝑖.
𝜎 − 1 𝐴𝑖𝑝𝑛𝑖 (𝜓̅) = 𝑝𝑛𝑖 =(B. 3)

We can derive the equilibrium wage from the equilibrium condition of tradable goods13:

𝜎−11𝑤𝑖 = 𝜉𝐴𝑖 𝜎 (𝐹𝑀𝐴𝑖 )𝜎 ,(B. 4)

1

1−𝜎 𝜎−1
where 𝐹𝑀𝐴𝑖 ≡ ∑𝑛∈𝑁(𝑤𝑛 𝐿𝑛 )𝜏𝑛𝑖
𝑃𝑛 , and 𝜉 is constant defined as 𝜉 ≡ 𝜎 −1 (𝜎 − 1)(𝐹(𝜎 − 1) )−𝜎.

The general equilibrium of QSM can be obtained by solving for three endogenous variables:
bilateral trade flows, population share, and wages. First, the gravity equation is shown as
follows:Γ𝑛𝑖 =𝐿𝑖 (𝜏𝑛𝑖 𝑤𝑖 ⁄𝐴𝑖 )1−𝜎.
∑𝑘∈𝑁 𝐿𝑘 (𝜏𝑛𝑘 𝑤𝑘 ⁄𝐴𝑘 )1−𝜎

(B. 5)

It implies the share of expenditures in location 𝑛 on goods exported from location 𝑖. Second,
we can obtain the population share from the population mobility condition14:

13

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

tradable goods in supply location 𝑖 can be shown as 𝑥𝑖 = 𝐴𝑖 𝐹(𝜎 − 1). The location 𝑛’s demand of the tradable
goods produced in location 𝑖 is 𝑥𝑛𝑖 = (14

𝜎 𝜏𝑛𝑖 𝑤𝑖 −𝜎 (𝛼𝑣𝑛 𝐿𝑛))

𝜎−1 𝐴𝑖

The population mobility condition is 𝐿𝑛 = [. Then, the equilibrium condition is 𝑥𝑖 = ∑𝑛∈𝑁 𝑥𝑛𝑖 𝜏𝑛𝑖 .

𝑃𝑛1−𝜎

−

σ−1
σ(1−𝛼)−1𝛼

1−𝛼 (Γ ) σ−1𝐴𝛼𝑛 𝐵𝑛 𝐻𝑛
𝑛𝑛
𝛼

𝛼

1−𝛼

̃𝛼( 𝜎 ) ( 1 )1−σ(1−𝛼)
𝑉
𝜎−1
𝐹𝜎
𝛼

46

]

derived by using equations (B.1) to (B.5).

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 is:

𝑤𝑖 𝜆𝑖 = ∑ Γ𝑛𝑖 𝑤𝑛 𝜆𝑛 .
𝑛∈𝑁

We can obtain the endogenously determined population by solving a system of equations.
B.2. What is Market Access?
There are two indicators of market access: firm (FMA) and consumer (CMA). FMA
increases where price competition is less intense and where firms trade with larger economies.
CMA, on the other hand, increases when firms face strict price competition. From theconsumer's perspective, this competition leads to lower prices, which makes them access to agreater variety of goods.
We can derive the MA defined in equation (1) by using the FMA and CMA. First, we canrewrite the FMA by using the relationship between the price index 𝑃𝑛 and 𝐶𝑀𝐴𝑛 :1−𝜎 (𝐶𝑀𝐴 )−1
𝐹𝑀𝐴𝑖 = ∑(𝑤𝑛 𝐿𝑛 )𝜏𝑛𝑖.
𝑛𝑛∈𝑁Second, since the price index 𝑃𝑛 can be rewritten in equilibrium,

𝑃𝑛1−𝜎 = 𝐶𝑀𝐴𝑛 = ∑𝑖∈𝑁𝐿𝑖𝜎 𝜏𝑛𝑖 𝑤𝑖 1−𝜎()
𝐹𝜎 𝜎 − 1 𝐴𝑖

(B. 6), we can derive the trade flow from location 𝑛 to location 𝑖:
𝑋𝑛𝑖 = (𝑤𝑛 𝐿𝑛 ) × Γ𝑛𝑖=

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

47

(B. 7)

In equilibrium, labor income 𝑤𝑖 𝐿𝑖 and total expenditures on trade goods are equal, then thefollowing relationship holds:

𝑤𝑖 𝐿𝑖 = ∑ 𝑋𝑛𝑖 .

(B. 8)𝑛∈𝑁

We can 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 (𝑤−1()= ∑ 𝜏𝑛𝑖𝑖 𝐿𝑖 )𝐹𝑀𝐴𝑖 .
𝐹𝜎 𝜎 − 1 𝐴𝑖
𝑖∈𝑁

Since it is known that 𝑀𝐴𝑖 ≡ 𝐹𝑀𝐴𝑖 = 𝜌𝐶𝑀𝐴𝑖 when trade costs are symmetric, 𝜏𝑖𝑗 = 𝜏𝑗𝑖 (e.g.,
Donaldson and Hornbeck, 2016; Redding, 2020), We can define MA as follows:−𝜃
𝑀𝐴𝑖 = 𝜌 ∑ 𝑌𝑛 𝜏𝑛𝑖
𝑀𝐴−1
𝑛 ,
𝑛∈𝑁

where 𝑌𝑛 = 𝑤𝑛 𝐿𝑛 , 𝜃 = 1 − 𝜎. The MA, derived by integrating both FMA and CMA, measuresthe market potential in each location by considering both the potential opportunity to sellgoods for firms and an availability of variety under their budget constraint for consumers.

48

Appendix C.
The Results of First Stage in Two-Stage Least Squares Regression
We show the results of the first-stage estimation in 2SLS regression in Table C1. This stage
is crucial as it tests the validity of our chosen instrumental variable, the recentered market
access (RMA) growth. The main result in Column (1) indicates a statistically significant andpositive impact on the change in the MA variable, with a coefficient of 1.05. This finding is
not only statistically significant but also aligns with the patterns we observed in Figure 5, thus
satisfying the "relevance condition" for our instrumental variable.
To further validate these results, we varied the trade elasticity parameter 𝜃, as detailed inColumns (2) to (5). Across these different specifications, we consistently observed a strong
positive relationship between the instrumental variable and the change in the MA variable.
This consistency supports the robustness of our first-stage estimation and reinforces thereliability of the RMA growth as an instrumental variable in our 2SLS framework.
Table C1. 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)

1.05 ***

0.72 ***

0.70 ***

0.88 ***

1.08 ***(0.09)(0.08)(0.11)(0.13)(0.10)

R-squared

0.66

0.31

0.46

0.60

0.66

Controls

✓

✓

✓

✓

✓

615

615

615

615

615

Observations

Notes: This table shows the results of the first stage in two-stage least squares regression. We calculate∆ ln 𝑀𝐴, ∆𝑅𝑀𝐴 with each trade elasticity. We use the same set of controls as in Table 3. *** 1%, ** 5%, * 10%
significance levels and parentheses denote cluster robust standard errors, clustered at the city level.

49

Appendix D. Sensitivity Analysis of Trade Elasticity
In this section, we report the results of our estimation using alternative trade elasticities. We
construct MA variables using trade elasticities for 𝜃 = 1.1, 𝜃 = 2.0, 𝜃 = 3.0, and 𝜃 = 5.0.
D.1. Changes in Market Access by Each Trade Elasticity
Figure D1 shows the geographic distribution of the MA variables using these tradeelasticities 𝜃 for the period 1985-1995. The red legend indicates a positively large(logarithmic) change in the MA variable, while the blue legend indicates a negatively largechange. As 𝜃 decreases, the changes in the MA variable become more closely aligned with thechanges in transport costs. This suggests that a smaller 𝜃 may not adequately reflect shifts inthe size of the economy or capture the full extent of the general equilibrium effect.
Furthermore, the figures reveal that when the trade elasticity is set to less than 3, the MA
variables within the Shikoku region display a uniform pattern. This indicates a potential
limitation in capturing the heterogeneous economic interactions among cities in this region.
Figure D1. The Geographic Distribution of the MA Variables by Each Trade Elasticity

Notes: These figures show the log change in MA variables 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.

50

Figure D2 illustrates the changes in the MA variable under varying trade elasticity 𝜃. Grayshaded areas represent the 1st to 99th percentile, 5th to 95th percentile, and 25the to 75th
percentile of all observers, in order from lightest to darkest color. Each line represents themedian for each region. Dots represent the raw values for cities. Our analysis reveals that asmaller 𝜃 results in a quicker capture of the increase in the MA variable, particularly from1985 to 1990. Additionally, when 𝜃 is small, changes in the MA variable closely mirror thechanges in transport costs. The MA variable with 𝜃 = 5.0 greatly increases between 1990 and
2000. However, it fails to reflect the substantial decline in trade costs in the Shikoku region,
as indicated by the negative change at the median value from 1985 to 1995.
Figure D2. Changes in MA Variable over Time by Each Trade Elasticity

Notes: Each figure is drawn in the same format as Panel B in Figure 5. Panel A shows the result of the tradeelasticity 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.

51

D.2. The Estimation Results with Each Trade Elasticity
We demonstrate how the use of different trade elasticities 𝜃 in the MA variable change
affect the estimation results. Table D1 presents the results of the EMA-OLS and RMA-IV
regressions. In Panel A, results for 𝜃 = 1.1 are not statistically significant. Panel B shows
results for 𝜃 = 2.0, the effect on population is statistically significant and robust across bothmodels, but not for the other two outcomes. The results for 𝜃 = 3.0 (Panel C) resemble thoseof 𝜃 = 4.0 for all economic outcomes. However, the results for manufacturing output andemployment are not robust across models and have a slightly small the first-stage F-value of
2SLS. In the case of 𝜃 = 5.0 (Panel D), we don’t observe statistically significant effect on
manufacturing output due to the larger variance of the change in the MA variable, which may
underestimate the effect for all outcomes. Therefore, the trade elasticity 𝜃 = 4.0, supported
by Simonovska and Waugh (2014), is the most preferred value in our context.

52

Table D1. The Estimation Results for Each Trade Elasticity
Manuf. Output

Population

Employment

EMA-OLS

RMA-IVEMA-OLS

RMA-IVEMA-OLS

RMA-IV

(1)

(2)

(3)

(4)

(5)

(6)

0.059

0.119

0.019

0.016

0.015

0.024

(0.116)(0.159)(0.015)(0.020)(0.021)(0.028)Panel A: θ =1.1
⊿lnMA
⊿EMA

-0.1580.009-0.024(0.131)(0.016)(0.022)First-Stage F-stat.
R-squared

78
0.103

0.090

78
0.455

0.454

78
0.373

0.369

Panel B: θ =2.0
⊿lnMA

0.161
(0.109)⊿EMA

0.031 **
(0.014)0.032 *
(0.019)

0.008

0.025

(0.022)(0.028)-0.234 *-0.003-0.040(0.132)(0.015)(0.025)First-Stage F-stat.
R-squared

0.260 *
(0.153)

44

44

44

0.104

0.092

0.453

0.453

0.374

0.369

0.269 ***

0.291 **

0.040 ***

0.042 ***

0.039 *

0.047 *

Panel C: θ =3.0
⊿lnMA

(0.098)⊿EMA(0.014)(0.015)(0.023)-0.019-0.056(0.186)(0.018)(0.040)First-Stage F-stat.
R-squared(0.119)-0.169

48
0.107

0.105

48
0.454

0.453

(0.025)

48
0.375

0.371

Panel D: θ =5.0
⊿lnMA
⊿EMA

0.120

0.117

(0.092)(0.080)Controls
Observations

0.033 ***
(0.010)0.052 ***
(0.019)-0.038-0.018-0.043(0.216)(0.016)(0.040)First-Stage F-stat.
R-squared

0.035 ***
(0.010)129129

0.049 ***
(0.017)

129

0.103

0.103

0.457

0.456

0.380

0.377

✓

✓

✓

✓

✓

✓

615

615

615

615

615

615

Notes: For each outcome, the first column presents the results of OLS regression with ∆𝐸𝑀𝐴, and the second
column reports the results of 2SLS with ∆𝑅𝑀𝐴 as the instrument. We use the same set of controls as in Table
3 and 4. *** 1%, ** 5%, * 10% significance levels and parentheses denote cluster robust standard errors,
clustered at the city level.

53

Appendix E. Additional Heterogeneity and Robustness Analysis
Figure E1. Spatial Variation in Recursive Market Access

Notes: This figure shows the log changes in MA variable over 1985-1995: we use lagged values of population
to construct the MA variable based on equation (1). This figure shows the difference in logs between the twoyears. For Panel B, we construct the changes in RMA by using the population as of 1980 for the changes in
EMA based on equations (7) and (8).

Table E1. The Estimation Results of Recursive Market Access

Notes: We use the same set of controls as in Table 3. *** 1%, ** 5%, * 10% significance levels and parentheses
denote cluster robust standard errors, clustered at the city level.

54

Table E2. The Estimation Results with Interaction Term
Interaction Terms

Manuf. Output as of 1980

Population as of 1980

Manuf. Output

Population

Employment

Manuf. Output

Population

Employment

(1)

(2)

(3)

(4)

(5)

(6)

Tertile
1st
2nd

0.139

0.030

0.027

0.161

(0.203)(0.026)(0.037)(0.177)0.186(0.120)3rd

0.266 ***
(0.102)0.054 **
(0.025)0.037 ***
(0.013)0.081 **
(0.039)0.072 ***
(0.020)0.327 **
(0.156)0.202 **
(0.094)0.043 *
(0.026)0.049 **
(0.020)0.038 ***
(0.014)0.045(0.040)0.101 ***
(0.032)0.055 **
(0.022)-0.105-0.024-0.057-0.124-0.023-0.059(0.216)(0.019)(0.040)(0.204)(0.021)(0.043)R-squared

0.092

0.465

0.393

0.116

0.458

0.389

Controls

✓

✓

✓

✓

✓

✓

Joint Test

0.24

0.04

0.01

0.40

0.03

0.14

Observations

615

615

615

615

615

615

⊿EMA

Notes: This table shows the results of the interaction term with manufacturing output as of 1980, andpopulation size as of 1980. Each column presents the results with ∆𝐸𝑀𝐴. For each interaction term, the firstcolumn presents manufacturing output, the second column reports population, and the third column shows
employment. We use the same set of controls as in Table 3 and 4. *** 1%, ** 5%, * 10% significance levels
and parentheses denote cluster robust standard errors, clustered at the city level.

55

Appendix F. Additional Counterfactual Analysis
In this section, we investigate additional counterfactual analysis. Our main estimation
calculates the change in the MA variable by using market size data from one period earlier toavoid endogeneity problem. We test other approach in this section. First, we use the 1985
population for the 1985 MA variable calculation and the 1995 population for the 1995
calculation. This approach yielded results very similar to those in Figures 7 and 8, so we do
not report them. Second, we use the 1980 population for both the 1985 and 1995 MA variablecalculation.
We show the results of this second approach in Figure F1. This figure, like Figure 7, plots
the predicted changes in the cities’ economic outcomes during 1995-2005. It shows that thecore cities and large peripheral cities, that are winners in Figure 7, are also clearly winners.
However, two notable differences arise: first, Fukuoka, a core city in the Kyushu region andphysically distant from the bridge, experience smaller economic growth. Second, no cities
experience negative growth, indicating that all cities have the potential to benefit from the
project. These differences stem from the fact that the MA variable of this approach does notreflect changes in market size. Figure F2, similar to Figure 8, plots the impact of the Central
Seto Bridges on the economic outcomes during 1995-2005. The results in Figure F2 arealmost identical to those seen in Figure 8. Both analyses indicate Awaji Island and the larger
peripheral cities in the Seto Inland Sea area are the main winners of the transportation
megaproject.

56

Figure F1. The Economic Impacts across Cities of Different Population Sizes in the Case of
Population as of 1980

Notes: The figure uses the same classification of population size as in Figure 7. Each point represents theestimated impacts over the 1995-2005 period. The black line shows the median of each the size of theeconomy.

Figures F1 and F2 differ from Figures 7 and 8, because they cannot consider changes in themarket size (i.e., we set the 1980 market size of the MA variable in Figures F1 and F2). Thisdifference reveals an important insight. Figures 8 and F2 evaluate the impact of theconstruction of the Central Seto Bridge. Since these two results are almost the same, theimpact of local construction, such as the bridges, appears to be relatively independent ofchanges in market size. On the other hand, we examine the broader impacts of highway
construction in Figures 7 and F1. These two figures show slightly different results, suggestingthat the impact of broader highway construction is largely dependent on changes in the market
size. This emphasizes the importance of our approach, which considers changes in market
size, in capturing the general equilibrium impacts of highway construction.

57

Figure F2. Economic Impacts Across Cities relative to the Counterfactual without theCentral Seto Bridge in the Case of Population as of 1980

Notes: The figure uses the same classification of population size as in Figure 7. Each point represents thecounterfactual impact of the construction of the Central Seto Bridge over 1995-2005. The black line shows
the median of each the size of the economy.

58

Appendix G.
Geographical Distribution of Counterfactual Predictions
Figure G1. Geographical Distribution of Net Benefit, Cost Burden, and Total Benefit

Notes: These figures show spatial distributions of net benefit (Panel A), cost burden (Panel B), total benefit
manufacturing output (Panel C) over 10-years during 1995-2005. Each panel shows values in units of JPY as
of 1988.

59