Discussion Paper No. 1000

RAILROADS, TECHNOLOGY ADOPTION,
AND
MODERN ECONOMIC DEVELOPMENT:EVIDENCE FROM JAPAN

Junichi Yamasaki

The 19th ISER-Moriguchi Prize (2016)
Awarded Paper

April 2017

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

Railroads, Technology Adoption, and Modern Economic
Development: Evidence from Japan
Junichi Yamasaki∗
Department of Economics, London School of Economics and Political Science
April 27, 2017

Abstract
Railroad access can accelerate the technological progress in the industrial sector and therefore induce
structural change and urbanization, the two common features of modern economic growth. I examine
this particular mechanism in the context of Japanese railroad network expansion and modern economic
growth in the late 19th century and early 20th centuries. By digitizing a novel data set that measures theuse of steam engines at the factory level, allowing me to directly observe the diﬀusion of steam power, I
analyze the eﬀect of railroad access on the adoption of steam power. To overcome the endogeneity problem, I determine the cost-minimizing path between destinations, and use this to construct an instrument
for railroad access. I find that railroad access led to an increased adoption of steam power by factories,
which in turn reallocated labor from the agricultural to the industrial sector, thereby inducing structuralchange. Railroad network also broke mean reversion in population growth, eventually leading to urbanization. My results support the view that railroad network construction was key to the modern economic
growth in pre-First World War Japan.

Keywords: Railroads, Transportation Infrastructure, Technology Adoption, General Purpose Technology, Structural Change, Urbanization JEL Codes: N65, N75, O14, O18, O33∗
j.yamasaki@lse.ac.uk. I am grateful to my supervisors, Robin Burgess and Greg Fischer for their constant support. I also
thank Gianmarco Ottaviano, Vernon Henderson, David Atkin, Jeremiah Dittmar, Janet Hunter, Tetsuya Okazaki, Takashi Kurosaki,
Yukinobu Kitamura, Kazuki Yokoyama, Hiu (Eddy) Tam, Yatang Li, Hanwei Huang, Thomas Carr and also seminar and conference
participants at LSE, Kyoto, Kindai, Osaka, Tsukuba, EUDN, and NEUDC for comments and suggestions. I also thank Takubo
Junpei, Tomohito Honda, Shouta Tojima, and Yoshimasa Katayama for their research assistance. This research was supported by
the Joint Research Center, Institute of Economic Research, Hitotsubashi University. Also, I greatly appreciate Yutaka Arimoto,
Kaori Ito, Asuka Imaizumi, Tetsuji Okazaki, Kentaro Nakajima, and Tomohiro Machikita, who digitized a part of data I am using inthis paper with support from the JSPS KAKENHI Grant-in-Aid for Scientific Research (B) 21330064, for providing me permission
to use the data. This paper is the 19th ISER-Moriguchi Prize awarded paper. All errors are my own.

11

Introduction

Transportation infrastructure is considered one of the most important public goods in developing countries.
In 2015, the World Bank was financing 197 transportation projects, accounting for 21 percent of its total
lending.1 The bank’s annual report for 2015 shows that the transportation sector was consistently one of
the three largest recipients of lending from 2011 to 2015. In the United States (U.S.) and Western Europe,railroad network construction was associated with an increase in economic growth. Rostow (1960) argues
that through increased market integration and higher demand for industrial goods, the railroad was key to thetake-oﬀ from pre-industrialized economies.2
This paper investigates the eﬀect of railroads on technology adoption in the industrial sector,3 structuralchange, and urbanization. The result of Bustos (2011) suggests that railroad network construction should increase the adoption of new technology due to increased economies of scale. Alvarez-Cuadrado and Poschke(2011) presents evidence that the productivity growth in the manufacturing sector relative to the agricultural
sector is responsible for structural change in the countries that transitioned between 1840 and 1920. However, the cause of this productivity growth, its eﬀect on structural change, and even its consequences for theoverall economy are not well understood. Apart from structural change, the other common feature of modern economic development is urbanization, which is only possible if population dynamics diverge from thepattern of mean reversion seen in agricultural economies. Desmet and Rossi-Hansberg (2009) and Michaels
et al. (2012) show that the adoption of new technologies and structural change broke the mean reversion in
the U.S.
I now consider the case of Japan in the late 19th and early 20th centuries. During this time, Japan
expanded its railroad network, adopted many Western technologies, and experienced modern economic development. The Japanese Government, following its modernization and centralization through the Meiji
Restoration in 1868, commenced constructing a railroad network in the 1870s. By 1900, this network
reached around 60 percent of the counties, as shown in Figure 1. Figure 2 shows that during this time,
the adoption of steam power proceeded at a faster pace than the growth in the use of fertilizers, which was1

http://www.worldbank.org/en/topic/transport/overview
This claim is tested by Fogel (1964). He uses sub-national price and trade flow data for the U.S., and concludes that the impact
of railroads was not as large as expected. Donaldson (2017) tested whether the price of agricultural land increased around theconstruction of railroads, as the trade theory predicts. They conclude that structural market access, as derived from a model a laEaton and Kortum (2002), increased the land price, and that without railroad access the price would be 64 percent lower. If thiseﬀect could be mitigated by the feasible extension of waterways and road improvement, then the loss would be in the range of 13 to20 percent.
3
In this paper, I abstract from the service industry and use “industrial sector” and “non-agricultural sector” interchangeably.
22the primary new technology in the agricultural sector. Similarly, Figure 3 illustrates that the price of industrial goods decreased relative to that of agricultural goods. Figure 4 shows that during this time Japan also
experienced structural change and Figure 5 urbanization. These changes resulted in the rapid growth of GDP.
Figure 6 illustrates that in 1868, Japanese GDP was far lower than that of the U.S. or the United Kingdom(U.K.), but began to increase thereafter.
To facilitate my analysis, I digitize several novel data sets. I construct geographical data showing therailroad network expansion from the 1870s to 1910s based on current network data and documents to identify
the opening year of each section. To measure technology adoption, I digitize repeated cross-sectional factorydata, which contain addresses, types of products manufactured, and information on the number of engines
and total horsepower for each power source, steam and water. This allows me to construct a county-level
panel data set with very concrete measures of technology adoption and productivity. In addition, I digitizecounty-level population and sector-wise worker data for my sample period to analyze population dynamics
and structural change.
Railroad construction is an endogenous choice by the government, and this endogeneity could bias theOrdinary Least Squares (OLS) estimates. Moreover, the direction of the bias is unclear ex ante. The government may choose to favor areas where the economic benefits would be the greatest. Alternatively, thegovernment may choose to build railroads in areas with low economic potential as a form of redistribution. I
use an instrumental variable to overcome this endogeneity problem. I construct the cost-minimizing route between destinations, using slope information to determine the construction costs incurred by the government.
This is a standard type of identification strategy in this literature as used in Atack et al. (2008b), Banerjee
et al. (2012), Hornung (2012), and Faber (2014). It exploits that the government’s motivation to connect thedestinations and make the network is exogenous to local economy. I then use the distance from the costminimizing path as my instrument for growth in railroad access, while conditioning on local geography andother variables in order to satisfy the assumption of quasi-random assignment. My Diﬀerence-in-Diﬀerencewith IV estimates implies that having railroad access by 1892 led to an increase in steam power of about 300
horsepower at the county level between 1888 and 1902.4 This means that railroad access growth between
1888 and 1892 accounted for 67 percent of steam power growth between 1888 and 1902.
I also analyze the impact of gaining access to the railroad network earlier on structural change and4

In 1902, the average steam power in factories was about 32 horsepower. According to the instruction manual of the Toyoda power loom, which was invented in 1898 as the first Japanese power loom ever, the factories could handle 20 power looms
by one horsepower (https://www.toyota.co.jp/jpn/company/history/75years/text/taking_on_the_automotive_
business/chapter1/section1/item6.html.

3

urbanization. Using census data from 1920 and the agricultural worker data from 1885, I estimate the longterm impact of having railroad access by 1892 on the share of workers in the primary sector. Conditionalon the share of population working in the agricultural sector in 1885, I find that early adopters have a 6.7
percent lower share of workers in the primary sector. To analyze the link between railroad access andpopulation dynamics, I estimate the impact of railroad access on population growth, allowing for dependence
on initial density. I find that without railroad access by 1892, counties with log initial population one standard
deviation above the average, have a 7.2 percent lower growth between 1885 and 1902 than that of counties
at the mean. This pattern of mean reversion is broken by railroad access. If a county gained railroad accessby 1892, this 7.2 percent decline becomes a 1.9 percent decline. When I analyze population growth between
1885 and 1920, the estimates for early adopters even becomes positive. The estimated eﬀect is 11.3 percent,compared to minus 16.9 percent for counties with no railroad access by 1892. This is consistent with apositive feedback mechanism generating urbanization.
When combined, these results imply that railroad construction was key to both structural change and urbanization. My results provide further implications for the more general question on why the industry sector
in Japan adopted Western technology so quickly and developed so rapidly after the Meiji Restoration. This
paper suggests one explanation; a centralized government receptive to Western technology, which enabledthese changes by rapidly constructing a railroad network.
This paper contributes to the literature on infrastructure, technology adoption, structural change, population dynamics and urbanization, as well as the economic history of Japan and the industrial revolution. In
terms of the eﬀects of railroads,5 recent studies such as Atack et al. (2008b), Atack et al. (2010), Burgess
and Donaldson (2010), Herrendorf et al. (2012), Duranton and Turner (2012), Jedwab and Moradi (2013),
and Fajgelbaum and Redding (2014), Donaldson and Hornbeck (2016), Donaldson (2017) investigate theeﬀect of railroads on the number of factories,6 price equalization, famine, the price of agriculture land, specialization, structural change, and the urban population.7 Authors such as Baum-Snow (2007), Duflo andPande (2007), Jensen (2007), Michaels (2008), Goyal (2010), Dinkelman (2011), Rud (2012), Duranton et5
Atkin and Donaldson (2015) finds that in Ethiopia and Nigeria, trade costs are four to five times larger per unit of distance thanthose in developed countries.
6
Factory production may include technology adoption as well, although the former is defined as an establishment with morethan 15 workers, and therefore does not measure the change in management methods. In addition, even among factories, the steampowered ones had higher labor productivity than that of the non-steam-powered ones, as also shown by Atack et al. (2008a). Thus,
it is worth considering this margin of steam power adoption.
7
Kim (2005) and Herrendorf et al. (2012) use a rural/urban classification in their analysis of urbanization, without discussing
Gibrat’s Law. However, such a classification depends on the country or context, so a discussion based on population dynamics will
be more general.

4al. (2014)f, Lipscomb et al. (2013), Kline and Moretti (2014), Faber (2014), and Agrawal et al. (2016) study
other types of infrastructure, such as highways, dams, electricity, regional development plans and mobile
phones.8 On the other hand, my paper focuses on a particular mechanism why railroad construction can
induce modern economic development by looking at the eﬀect on technology adoption.
A research study on international economics also examines technology adoption, and since railroads
reduce transportation costs, gaining access to a railroad network will be conceptually similar to the reductionof tariﬀs or trade liberalization. The paper most similar to mine is Bustos (2011), which studies the impact
of MERCOSUR on the investment in technology by Argentinian firms.9 Authors such as Pavcnik (2002)
and Fernandes (2007) study the relationship between trade and productivity growth at the plant level. Thesepapers typically estimate plant-level productivity using structural methods such as Olley and Pakes (1996)
or Levinsohn and Petrin (2003), whereas I directly observe each factory’s use of steam engines.10
Turning to the structural change literature in macroeconomics, as noted above, Alvarez-Cuadrado andPoschke (2011) shows empirically that the nature of structural change modified between 1920 and 1960. Before 1920, structural change was driven by increase in the relative productivity of non-agricultural industries,
which led to a labor pull eﬀect. Since 1960, structural change has been driven by productivity improvements
in agriculture, which led to a labor push eﬀect. This can be formulated as operating through an income
eﬀect, as in Gollin et al. (2002) and Kongsamut et al. (2001), or through a relative price eﬀect as in Ngai
and Pissarides (2007). On the other hand, recent empirical studies that use within-country exogenous variation find that growth in agricultural productivity is the cause of structural change. Foster and Rosenzweig(2008), Marden (2015), Bustos et al. (2016) and Kitamura (2016) find this to be the case for post-Second
World War India, China, Brazil, and Japan, respectively. All these papers study eras and countries where
Alvarez-Cuadrado and Poschke (2011) find that technological progress in agriculture is driving structural
8

Faber (2014) uses an identification strategy similar to this paper in the context of highway construction in China. The Chinese
Government aimed to connect all local capitals and cities with a single highway network. As an instrument, he uses geographicalinformation to find the cost-minimizing path between each possible combination of cities, and then chooses routes to connect all
cities at the lowest cost. In my case, the Japanese Government merely wanted to connect specific destinations. In particular, they
aimed to connect Tokyo with the northern and western ends of Honshu, Aomori, and Niigata. I find no anecdotal evidence of theirintention to directly connect every destination to every other destination.
9
To be precise, her measure of investment in technology is R&D spending, whereas mine is total horsepower, which is an
outcome of investment in new technology.
10
There also exist relevant literature regarding technology adoption in development economics. Munshi (2004), Foster andRosenzweig (2008), Bandiera and Rasul (2006), and Conley and Udry (2010) find evidence of network eﬀects and learning. Suri
(2011) shows evidence of heterogeneity in the eﬀects of new technology. (Duflo et al., 2011) finds that behavioral biases exist. Gine
and Yang (2009) and Dercon and Christiaensen (2011) show that the risk of return and its consequences for consumption due tocredit market imperfections, are key barriers to technology adoption. This study is diﬀerent because it focuses on macroeconomic
barriers to technology adoption, rather than individual-level barriers, and it studies the industrial sector, rather than the agricultural
sector.

5

change, or are out of the sample of Alvarez-Cuadrado and Poschke (2011) entirely. This paper examines
why the relative productivity of non-agricultural industries was rising before 1920, and whether this induced
any structural change.
The fourth type of literature this paper is linked with are studies on population dynamics and urbanization. In these studies, Gibrat’s Law, which asserts that population growth rates are constant irrespective of
diﬀerences in initial population density, is one of the stylized facts11 . However, Glaeser et al. (2014) findsthat it does not hold in the U.S. in some sample periods, and Dittmar (2011) finds the same result for Europe.
Studies such as Giesen and Südekum (2014) and Desmet and Rappaport (2015) investigate which factors,
such as city age, explain these diﬀerences in population growth rate. Desmet and Rossi-Hansberg (2009)
and Michaels et al. (2012) argue that industry-specific technological progress is the key; suﬃciently fast
technological progress will break the pattern of mean reversion in population as better technology attracts
migration. My paper tests this idea by investigating whether gaining access to a railroad network, and thesubsequent adoption of new technology breaks mean reversion in population dynamics.
Finally, this paper is linked to the literature on the economic history surrounding steam power and theindustrial revolution. The diﬀusion pattern of steam power, and its eﬀect on factory-level productivity andurbanization in the U.S. are studied by Atack et al. (1980), Kim (2005), and Atack et al. (2008a). Other
related papers consider the industrial revolution in Japan. The paper most similar to mine is Tang (2014),
which analyzes the impact of railroad access on the number of firms and their capital stocks. This paper
diﬀers from Tang (2014) in that I use the various county-level outcomes, and employs instrumental variables.
Tang (2014) uses prefecture-level data and Diﬀerence-in-Diﬀerence analysis. Minami (1977) and Minami(1979) describe the diﬀusion of new power sources using prefecture-level data, and estimate the impact
on productivity through growth accounting. However, the link between the diﬀusion of steam power andrailroad access is not studied. Kiyokawa (1995) provides an overview of technology adoption in pre-Second
World War Japan, covering both agricultural and non-agricultural sectors.
The remainder of the paper proceeds as follows. Section 2 discusses why the construction of a railroad
network could accelerate technology adoption, structural change, and urbanization. Section 3 provides an
overview of the history of railroads and steam technology in Japan, describes the data sources used in the
study, and explains the construction of the instrument. Sections 4 and 5 present my estimation results, andSection 6 concludes.
11
Gibrat’s Law is linked theoretically to Zipf’s Law, another stylized fact concerning the size distribution of cities. Gabaix and
Ioannides (2004) describes the related theory, and Giesen and Südekum (2011) provides the empirical evidence.

6

2

Conceptual Framework

In this section, I first explain the market access channel, which is the main channel through which railroad
access could have increased technology adoption in the industrial sector. I then describe how this could
facilitate structural change and urbanization. I also discuss two other potential channels, the migration andadoption cost channels, and explain how it is possible to distinguish them from the main channel in my data.
In addition, I describe how financial frictions, and whether domestic or foreign market integration is driving
technology adoption, could lead to heterogeneous outcomes. I use the intuition developed in this section as
the basis for formal statistical tests in Section 4. The discussion in this section is informal, but I include aformal model for the main channel in the Appendix.
To understand the market access channel, consider a firm in a monopolistically competitive industry,
which exports goods to many regions. To export its goods, the firm has to incur a trade cost of the iceberg
cost. When railroads are constructed, these trade costs would decrease. The firm would then reduce its
price,12 and facing constant elasticity of substitution (CES) consumer demand, it increases its sales. Suppose
the firm can pay a fixed cost to adopt a new technology that lowers its marginal cost. As in Bustos (2011),
firms that have access to a railroad network and produce more output will be more likely to adopt this newtechnology.13
Alvarez-Cuadrado and Poschke (2011) show that in the presence of an income eﬀect in the industrial
sector, the adoption of new technology by industrial firms will increase the share of total workers employedin the industrial sector, thereby implying structural change. This eﬀect is known as labor pull. Most current empirical literature examine labor push, where productivity growth in the agricultural sector leads toindustrialization. Alvarez-Cuadrado and Poschke (2011) show that the labor pull eﬀect was dominant indeveloped countries before 1920. As seen above, this is also true in the case of Japan.14
Technology adoption would also change the population dynamics. In an agricultural economy where
technology progress is stagnant, the more populated areas have lower population growth due to mean reversion of agricultural productivity. In an industrial economy, the population dynamics of the more populatedareas are determined by the productivity of the industrial sector, which is more persistent due to technolog12

We have the same implication when firms can use input with lower price. I include this as a part of this market access channel.
David (1975) discusses similar mechanism to explain why railroad will lead to the gain in productivity at factory level, though
he explain it by learning by doing rather than technology adoption. Also, he points out that this gain is not accounted in social
saving estimated in Fogel (1964).
14
In the Appendix, I formally modeled both labor push and pull by combining Bustos (2011) and Alvarez-Cuadrado and Poschke(2011).
137

ical progress. More populated areas may also have additional advantages in technology adoption, such as
easier access to credit, which could lead to rapid population growth.
There can be three other channels by which access to a railroad network could aﬀect technology adoption.
The migration channel may exist because railroad construction could aﬀect migration costs. If railroads
reduce the migration costs, labor mobility may consequently increase. If areas with railroad access are more
attractive to workers than those without, this could increase the aggregated demand in the former areas.
Sales of firms in these areas could increase as their home market will be larger. In addition, a higher labor
supply would lead to lower wages, which will be passed through to lower prices, further increasing sales.
As mentioned above, higher sales for firms with access to the railroad network would mean that they are
more likely to adopt new technologies. Note that the market access channel will also lead to an increasein migration through the labor pull eﬀect, therefore the market access and migration channels have broadly
similar implications. However, the two diﬀer in the timing of events. The market access channel implies
that firms first adopt new technology, which is followed by an increase in population. Under the migration
channel, the order is reversed. It is thus possible to distinguish between the two channels by examining
the timing of technology adoption relative to population growth.15 The information channel may also exist
because railroad construction will lower the communication cost and it may induce technology adoption by
sharing information such as the existence of steam power or its expected return. I will test this channel bylooking at the number of expositions, an important place to share information.
The final channel is the adoption cost channel. To adopt steam power, firms had to buy machines fromtrading companies, which were mainly based in Tokyo or Osaka. Asajima (2001) documents that the Mitsui
Trading Company, the dominant trading company in the late 19th centuries, imported and sold machines
through their headquarters in Tokyo and a branch in Osaka. The introduction of a railroad network may
allow firms based outside Tokyo and Osaka to access these trading companies more easily. In addition,
Pomeranz (2001) shows that the gain in productivity by adopting new technology depends on the price ofcoal. By reducing transportation costs for coal, railroad network construction could increase the productivity
gain from new technology for firms in areas far from coal producers.16
Other than these channels, Rostow (1960) suggests that railroad construction could aﬀect the economy

15

It is possible that firms adopt new technology in anticipation of future migration, which would render timing under the migrationeﬀect ambiguous. However, the outcome variable that I examine is horsepower, a result of investment, and firms will have no
incentive to increase horsepower before migration occurs.
16
Coal was an export industry at that time, so the distance from the areas of coal production, and not ports, will be more appropriate.

8

by increasing demand. The construction requires industrial goods such as rails and locomotives. However,in this case most goods were imported from abroad, so the eﬀect on domestic demand should be minimal.
In the presence of credit market frictions, the eﬀect of access to a railroad network on technology adoption may be heterogeneous over areas. Suppose only urban areas have credit markets, then firms in only
these areas could invest in new technology.17 Gaining access to a railroad network will then have a greater
impact for more populated areas.18
Heterogeneity of eﬀects over industries are also important to distinguish which of domestic integrationand external integration is the key mechanism behind. Although the conceptual framework abstracts from
this, it would still aﬀect the external validity of the results. For example, if the eﬀect is found only in theexporting sector, then this suggests that country-level comparative advantage will fully determine the sector
aﬀected by railroad access.

3

Background and Data

In this section, I first provide an overview of the history of railroad network expansion, and explain how I
exploit these historical facts in constructing my instrument. I then describe modern economic development
in Japan. I also briefly discuss my data sources, and explain the construction of my control and outcome
variables. The Appendix contains a more detailed description of each of the original data sources. Note thatmost of the data set comes from hundreds of newly digitized documents through archival work. A uniquefeature of this data set is that I observe variables at a more disaggregated level (county level) than in otherexisting data set (prefecture level). This enables me to analyze important economic issues such as heterogeneous impacts, externalities, and urbanization. Prefecture-level data will mask county-level variation foranalysis on heterogeneous impacts and externalities. Also, it will not be appropriate to analyze urbanization
if we consider within prefecture migration. Finally, I will show the descriptive statistics of my data set.

17

Nakamura (2010) shows that in Japan, the credit market for firms was based on face-to-face communication and networks
among elites established during the Edo period. Ohnuki (2007) finds that the interest rates were higher in rural than urban areas in
the early 1880s and the 1890s, although the gap narrowed after the establishment of the Bank of Japan.
18
When exporting has a fixed cost, only larger firms will choose to export. Bustos (2011) labels this as the firm selection eﬀect.
If larger firms tend to be located in areas with large populations, then this mechanism implies that the eﬀect of railroad accesson technology adoption will be stronger in urban areas. However, in the data I use, there is no significant relationship between
population density and the average number of workers per factory. Therefore, this eﬀect should be absent.

9

3.1

Development of the Japanese Rail Network

3.1.1

Background

Both railroads and steam power originated in the U.K. The diﬀusion of these technologies to Japan was slow
due to political circumstances. Consequently, the adoption started in the late 19th century. From 1612 to1853, the Tokugawa Shogunate severely restricted interactions with foreign countries in order to preserve
political stability. In 1612, Christianity was outlawed, and in 1635 Nagasaki was designated as the only
port open to Western ships19 . In 1639, the Netherlands became the only Western country permitted to tradewith Japan. Western knowledge could then diﬀuse to Japan only through the Netherlands. Throughout thisperiod, Japan gradually adopted Western technology.
In the 1850s, the U.S. used military force to pressurize Japan to open its ports to trade. Japan subsequently signed treaties with the U.S., the U.K., France, the Netherlands, and Russia. Political conflict over
how to handle this foreign pressure resulted in a civil war, the collapse of the Tokugawa Shogunate, and eventually, the establishment of the new Meiji Government in 1861. The civil war and the threat of colonization
by foreign powers led many political groups to attempt to adopt Western technology in the 1850s.
Railroad was one such technology. Japanese people first saw steam locomotives in the 1850s20 . In 1869,
the Meiji Government created a railroad construction plan in order to link Tokyo and Kyoto. The first railway
line opened in 1873. It was approximately 30 km in length, and linked the capital, Tokyo, with the largest
port open to foreign trade, Yokohama. In 1874, a line linking Osaka, the second-largest city, and Kobe,
another port open to foreign trade, was completed. Military expenditures related to civil war in 1873 (theSeinan Senso) led to budget problems, and there was no further expansion of the railroad network in the1870s.
In the 1880s, the railroad network began to expand. Following these fiscal problems, the Meiji Government allowed the establishment of a private railroad company, Nihon Tetsudo in 1881. This company wasissued a license to build a line from Tokyo to the northern end of Honshu, the largest island in the Japanesearchipelago. Despite being a private company, Nihon Tetsudo has close connections with the government,21
and received financial support from it. In 1887, to further increase private investment in railroads, the government enacted a law allowing the private sector to establish railroad businesses. Under this law, private
19

There were three other ports open to other Asian regions: Tsushima for Korea, Satsuma for Ryukyu, and Ezo for the Ainu
people20
In contrast, in the U.K., the first commercial railroad with steam locomotives had opened in 1825.
21
Iwakura Toshimi, who was appointed as Udaijin (literally “Minister of the Right”) for the Council of State of the Meiji Government in 1871, served on the board of Nihon Tetsudo.

10

firms had to submit a proposal for a rail line to the government, and obtain a license. These policies enabled the railroad network to expand. The top-right panel in Figure 1 shows the railroad network in 1892.
Accordingly, railway lines covered about 34 percent of the counties included in my sample.
The expansion continued even after the 1880s. In 1892, the central government enacted the Railway
Construction Act (Tetsudo Husetsuho) to plan the construction of additional railroad lines. The law specified
33 lines,22 and stated that a public railroad fund would finance their construction projects. Private sector
firms were also allowed to build these lines on behalf of the government, conditional on obtaining a license.
The routes were chosen based on predicted local economic benefits. A map of factors, which would predict
railroad revenues such as villages, industries, and other transportation, was constructed.23 The railroad
network then expanded to cover the entire nation. In 1895, 2,126 thousand Yen was spent on railways
through the public fund, constituting 2.49 percent of the total government expenditure. In comparison,
the total expenditure of the Ministry of Education was 1,153 thousand Yen, and that of the Ministry ofAgriculture and Commerce was 1,236 thousand Yen. The amount of freight traﬃc and the number of cars
used for freight services increased during 1890. Figure 7 shows that the growth rate of freight services
was higher than that of passenger services.24 The bottom-right panel of Figure 1 shows that by 1920, therail network covered almost the entire country. Accordingly, the rail network decrease domestic trade cost.
Tetsudo-In (1916) documents that railroads access decreased the trade cost by half between Tokyo and Fukui,
which is about 500 km distance in today’s highway network. Before railroad network construction, it cost
132 yen and 10 days per 10kan (37.5 kg). After railroad network construction, it became 62 yen and 4
days.25 Tetsudo-In (1916) also states that by railroad access, people increased consumption by importingfrom other regions, and domestic traders increased long-distance trade accordingly, which is consistent withthe market access channel.
To construct my instrument, I exploit the government’s policy objectives in the 1880s. Masaru Inoue, thedirector of the Ministry of Railways, was the driving force behind the government’s railroad policy. Whendesigning the line from Tokyo to Aomori, the northern end of Honshu, he stated that “We should not makeside trips to consider local benefits.” This statement implies that the government prioritized the connection from Tokyo to the end points of Honshu over the benefits to the local economy, suggesting exogeneitywith respect to the economic conditions of the local areas along the route. In addition, technological con22

This includes some lines that were already completed.
p. 123 in Vol. 3 of Nihon Kokuyu Tetsudo (1997)24
At that time, most railroads carried both freight and passengers, whereas modern high-speed trains carry only passengers.
25
Unfortunately, there is no comprehensive trade cost data before railroad network construction.
23

11

straints meant that the slope was an important consideration when planning routes. As pictured in Figure
8, construction was more diﬃcult and costly in steeper areas. For example, the line connecting Tokyo with
Western Honshu was initially planned to pass through steep terrain, but after re-evaluation in 1885, the route
was changed to pass through flatter areas as shown in Figure 9. A report comparing the two routes was
prepared after this re-evaluation. This report estimated that the construction cost per mile of the original
route was roughly double that of the final route. The slope was also significant for the operation of rail lines.
Locomotives would run more slowly on slopes, and were not powerful enough to climb some steep slopes.
The report estimated that a train on the original line would take one and half times as long as the revisedroute would, despite the length of the two routes being roughly the same.
Given this historical context, I construct cost-minimizing paths from Tokyo to the prioritized destinations
to use as an instrument for railroad network expansion from the 1880s to 1892. In 1881, Nihon Tetsudo
prioritized a few destinations in their statement. The first objective of Nihon Tetsudo was to connect Tokyo
and Aomori, the latter being historically regarded as both the northern end of Honshu and the gateway to
Hokkaido. The other two prioritized lines connected Tokyo and Niigata, and Moji and Nagasaki. Niigata andNagasaki were both ports open to Western trade, and Moji was the gate port of Kyushu facing Shimonoseki.26
To the West, Shimonoseki was prioritized by the government as the gate port for the island of Kyushu.27 In
constructing the cost-minimizing path, I use information on slopes together with an algorithm by Dijkstra(1959), which I explain below.
To be a valid instrument, the cost-minimizing path must satisfy the relevance, quasi-random assignment,
and exclusion restrictions. Since cost was considered when the route was planned, the cost-minimizing pathwill predict the actual expansion of the network. Furthermore, we can easily test this using the results ofthe first-stage regression. On the other hand, by construction, the cost-minimizing path will be correlatedwith local geography, which aﬀects its own outcomes, and this violates the assumption of quasi-random
assignment. Therefore, I use the distance from these paths conditional on local geography including slope
26

The original statement by Nihon Tetsudo states that the line to Niigata will continue to Ushu, a historical province to the northof Niigata, comprising the modern Yamagata and Akita prefectures. However, the statement does not specify a city in this province
as the intended destination. Therefore, I set Niigata as the destination as their priority. Niigata was the only foreign port open toWestern trade on the Sea of Japan, rendering it a natural choice as the final destination. In addition, the fact that no exact destinationin Ushu was specified indicates its subsidiary importance relative to Niigata. Similarly, the line between Moji and Nagasaki was
originally intended to continue to Higo province, a historical province corresponding to today’s Kumamoto prefecture. Assumingthat the final destination is Kumamoto city, the line to Higo would be only 70km in length and pass through five or six counties
along the cost-minimizing path, so excluding this branch will not aﬀect the final results.
27
In the statement by Nihon Tetsudo, Kyoto was specified as the destination of the line to the West from Tokyo. However, thegovernment recognized that Shimonoseki as the important destination to the West. In 1887, to consider the proposal from the private
sector to a line to Shimonoseki, Masaru Inoue stated in a document to the cabinet that the line to Shimonoseki is a part of prioritizednetwork. In Table A.10, I do robustness checks by excluding counties which are located between Kyoto and Shimonoseki.

12

information. Figure 10 illustrates this idea. As an example, I consider two potential paths from Tokyo to thedestination in question, although there is an infinite set of potential. In this case, path B will be chosen as it
passes through flatter terrain than path A does. If paths A and B are compared unconditionally, the assumption of quasi-random assignment will be violated as the flatter area may have better economic potential. By
conditioning on local geography, the comparison is between counties with similar slopes, but one lies onpath A and the other on path B. The variation of the instrument is now from the goverment’s networking
motivation and other counties’ geography, which will be exogenous to local economy. Moreover, before
the railroad era, there were no attempts to connect Tokyo with other destinations by public expenditure onland transportation due to the absence of a powerful centralized government and the low level of technology
in land transportation.28 Therefore, conditional on local geography, the cost-minimizing path will not be
associated with economic variables before railroad construction started.
To further ensure quasi-random assignment, I estimate specifications including the initial population
density and the existence of traditional roads as additional controls. I show that these controls do not aﬀect
the results, suggesting that conditional on local geography, the instrument is plausibly exogenous. There
may also be concerns related to the exclusion restriction. For example, the central government may allocate
other kinds of public goods together with railroad construction. This would cause the eﬀects of railroad
access to be overestimated, although the reduced form will not be biased. To eliminate this possibility, I
check for a relationship between the instrument and other kinds of public spending, such as investment inroads or irrigation. I will also discuss other important policies and technological progresses during this timeand argue that these polices and progresses will not aﬀect the main result.
3.1.2

Data

Using the current railroad network data, I construct historical data for the rail network. The National Land
Numerical Information (NLNI) download service29 oﬀers geographical data that includes all railroad network and station data from 1950 onwards. However, it does not include the opening years for all railroads
that opened before 1950, and therefore cannot be used to generate geographical data for the railroad networkfor each year of the sample period. Consequently, I use two additional oﬃcial documentary sources, theNihon Kokuyu Tetsudoshi (The History of Japan’s National Railroad) and the Nihon Tetsudoshi (The Historyof Japanese Railroads) to retrieve the railroad opening years. After constructing the historical network data,
28
29

Before railroads were constructed, marine transportation was the major transportation mode for long-distance freight.
http://nlftp.mlit.go.jp/ksj

13

I identify the year in which each country gained access to the railroad network.30
To calculate the cost-minimizing paths, I download 250 meter mesh data for average slopes from the
National Land Numerical Information Download Service,31 and calculate the cost-minimizing path using thealgorithm in Dijkstra (1959).32 My basic specification assumes that construction costs are a linear functionof average slope. Since the true cost function is unknown, I also try a specification, which includes a constantterm, and a specification, which does not penalize a slope of less than 25 per mill. The results under these
specifications are discussed in detail in the Appendix, but the form of the cost function did not aﬀect the main
results. After constructing the paths, I simply calculate the distance to each path from each county, which I
then use as the cross-sectional variation of the instrument to predict growth in railroad access by 1892.
To control for the time-variant eﬀect of traditional roads, I constructed network data for traditional roads
by connecting the towns that each route passed through with straight lines, referring to the Nihon no Kaido
Handbook (Handbook of Japanese Traditional Roads). Although this is an approximation, but since thetowns are fairly close to one another, the measurement bias will be minimal at the county level. I also
included a diﬀerent dummy for the Gokaido, which are five major routes specified and controlled by the
Shogunate. The Shogunate directly specified checkpoints, pre-modern post stations, and lodging on theroutes, and therefore these areas might have higher economic potential.

3.2

Steam Power and Japanese Economy

3.2.1

BackgroundThe diﬀusion of steam power to Japan was also gradual. In 1712, the British inventor Thomas Newcomen
was the first to develop a practical steam engine.33 James Watt, among others, subsequently attempted to
improve the engine’s thermal eﬃciency. In 1785, Edmund Cartwright used steam power as the basis forhis power loom. Robert Stephenson later used steam to power locomotives on the Stockton and Darlington
railway lines. Many industries thus began to use this new source of power.
In Japan, the Tokugawa Shogunate was the first to experiment with Western steam technology. In 1861,
the Shogunate established a steam-powered metal producing factory in Nagasaki. Throughout the 1860s, the30

Throughout this paper, the variable railroad access is a dummy, which takes a value of one for a county-year when that county
has any intersection with a 1 km buﬀer of railroad lines by the beginning of that year. I include the buﬀer since minor data issues
lead some lines to pass outside the coast.
31
For computational reasons, when rasterizing the mesh data, I set 0.002, which is around 1.7 km, as the mesh size.
32
The Arcgis “cost” commands use this algorithm.
33
The idea of steam power is not new. In the first century AD, Hero of Alexandria described a simple steam turbine known as the
Aeolipile (Hero’s engine).

14

Shogunate, local domains, and the Meiji Government established similar factories in other industries suchas textiles and mining (Minami, 1977), but it was not until the 1880s that the private sector began to utilize
steam engines.34
Adoption of steam power led to a significant increase in the aggregate horsepower, as can be seen in
Figure 2. In 1884, the total horsepower was 1,051, which increased to 21,825 by 1890. In addition, as
Figure 11 shows, there is regional variation in steam power adoption. On the other hand, Figure 2 also showsthat technological progress in the agricultural sector, often considered the key to structural change, did notoccur during this period. Fertilizer consumption did not increase as rapidly as horsepower did, and the next
major technological breakthrough, a new high-yield variety of rice35 did not occur until 1910.36 Consistent
with these developments, Figure 3 shows that the relative price of industrial goods declined before 1940,
particularly before 1900. Alvarez-Cuadrado and Poschke (2011) notes that this pattern can be observedfor 11 countries that experienced structural change between 1840 and 1920. This implies that productivity
growth is faster in the manufacturing sector than in the agricultural sector, consistent with the labor pulleﬀect emphasized in Lewis (1954), and not labor push.
A stylized pattern of structural change and economic growth can also be seen. Agriculture’s share oflabor and value added decreased, while GDP per capita increased. Figure 4 shows the declining importanceof the primary sector (agriculture and fishery) after the 1880s. Figure 6 shows growth in GDP per capita
for Japan, and three other countries included for comparison. Before 1868, both Japan and China lagged far
behind the U.S. and the U.K., Western countries that had already experienced industrial revolutions. After
1868, Japan began to grow while China’s growth was stagnant. Japan’s catch-up period ended during WorldWar II, but began to grow again afterward and surpassed the U.K. in 1980.37
Cross-sectional data also illustrates a regional pattern of technology adoption, structural change, andurbanization. Figure 12 plots the share of primary workers in a county against the log of total horsepower
34
The fixed cost of adopting steam technology was significant, as I assumed in the conceptual framework. In the pulp and paperindustry, in 1890 Fuji Seishi, the second-largest company at that time, estimated the cost of establishing a steam-powered factory as
32,919 Japanese Yen. Unfortunately, they did not report any revenue, but using the revenue of Ouij Seishi and the relative productionof the two companies, estimated that of Fuji Seishi to be around 230,000 Japanese Yen. The cost of this new factory would thus bearound 14.3 percent of the annual revenue.
35
The key scientific knowledge underlying this breakthrough, Mendelian inheritance, was re-discovered in 1900.
36
Kiyokawa (1995) states that there were two other adoptions of major technology in agriculture. The first was a new type of
cocoon that could grow after July. Its adoption rate was stagnant during the 1890s, although there is no data available before thisdecade. Additionally, this cocoon does not belong to the agricultural sector as defined in most growth models, because there will be
no subsistence level of consumption for this crop. Therefore, I interpret technology adoption in this industry similar to steam power
adoption in the industrial sector, although most data do not treat it separately from non-cash crops, such as rice. The second newtechnology was the non-human-powered agricultural machinery, although adoption of this technology started in the 1900s.
37
Hayashi and Prescott (2008) argues that pre-Second World War cultural factors explain this progress, while the war and theresulting destruction of the country acted as constraints on migration and hindered structural change.

15per factory worker and rice productivity per land. Both are negatively correlated with the primary industry’s
share of employment with the primary industry’s share of employment, with the slope being much steeper forlog horsepower (on a standardized X-axis). This is consistent with labor pull rather than labor push. Figure
13 shows that a negative correlation exists between the share of primary workers and the log of population
density.
To summarize, there is a positive relationship between technology adoption in the industry sector, structural change, urbanization, and economic growth from the 1880s onwards. This is true both in time series
and cross-sectional data. It is therefore important to examine the factors that induced technology adoption in
the industrial sector.
3.2.2

Data

To capture technology adoption and economic development, I newly digitize three diﬀerent publication series, and supplement this data with two other previously digitized series. I use two types of data: factorydata, which allows me to observe technology adoption, and population data, which allows me to observe
structural changes and urbanization.
To obtain factory data, I digitize two statistical series published by the central government, Noshomu
Tokeihyo (Agriculture, Commerce and Manufacturing Statistics) and Kojo Tsuran (the Factory Catalog).
These contain the total horsepower generated through steam power, the number of steam-powered factories,
and the number of workers in steam-powered factories from 1888 to 1891, 1902, and 1919. These series also
contain product information, which allows data to be classified by industry. However, there is no reliablecommon identifier for individual factories, so I aggregate all variables to the county level.
I digitize a similar local government publication, the Huken Tokeisho (Prefectural Statistics) from 1884
to 1902 to obtain the revenue, share capital, number of workers, product type, and location of each factory.
This publication has many missing volumes, so I do not use it in my main analysis. However, I use the profit
data to check if the use of steam power is associated with improvements in revenue productivity.
For the population variables, I digitize several central government population publications, the Nihon
Teikoku Minseki Koguchi Hyo (Table of Registered Population), the Nihon Teikoku Seitai Jinko Tokei (Tableof Population Statistics), and the Population Census, and obtain population data for 1885, 1888 to 1898,
1903, 1908, and 1920. Data is also available for 1879, 1880 and 1882, which is before IV aﬀected railroad
construction. However, this data uses a slightly diﬀerent definition for population, so I use it only in checking

16the exclusion restriction. The data for 1920 was collected by census, and contains the number of workers ineach sector. I use this data to test for the eﬀect of technology adoption on structural change. Before 1920,
no central government publication records the number of workers in the agricultural sector. I obtain thisdata from a local government publication, the Huken Tokeisho, which records the number of workers in the
agricultural sector around 1885. This is used as a control when testing for eﬀects on structural change.

3.3

Descriptive Statistics

Before moving to regression analyses, I show the descriptive statistics. Table 1 presents the descriptive
statistics for three data points: an initial stage at 1888, a middle stage at either 1902 or 1903, and an end
stage at either 1919 or 1920. Panel A shows that steam power was in limited use in 1888. The average total
horsepower per county was 12.4, and the average number of steam-powered factories per county was only
0.58 . There were two-thirds as many water-powered factories as there were steam-powered ones, but theformer’s total horsepower was 15 percent of that of the latter. Only 5.8 per cent of the counties had railroad
access. The average county population was 69,087 and the average population density was 245.84. Thislevel of population density is similar to that of Pakistan in 2014, which had a density of 240, and lower thanthat of Vietnam in 2014, where the density was 293.38 Most counties had traditional roads, as is also shownin Figure 14, while the Gokaido did not reach the end points as the railroad network did after 1868.
By the middle stage, the average number of steam-powered factories had increased to 3.51, and thehorsepower to 112.79, roughly 10 times that of the initial period. The number of water-powered factories
had also increased, but their contribution to the total horsepower was 22.62, much lesser than that of steampowered factories.39 Population and railroad access both have increased. About 64 percent of the counties
now have railroad access.
Toward the end stage, electricity and gas emerged as the new types of power source. These new powersources are more important than steam power, both in terms of horsepower (621.57 vs. 1207.96) and thenumber of factories (8.17 vs. 20.1). I aggregate these two power sources when analyzing the long-term
impacts later. Population has also increased, and 79 percent of the counties now have railroad access.40

38

http://data.worldbank.org/indicator/EN.POP.DNSTThe original data records the horsepower for “steam” and “other,” not specifically water. However, given the context, it isappropriate to read this as water power. I therefore assume this variable is consistent between the data in 1902 and before.
40
To show the extent to which steam power had diﬀused in more detail, I present the descriptive statistics at the industry andfactory levels in 1902, as in Table A.2.
39

17

4

Main Results

In this section, I first confirm that the introduction of steam power increases the factory size and laborproductivity by using the Huken Tokeisho. Then, I estimate the causal eﬀect of railroad access on steam
power adoption. I then perform robustness checks and heterogeneous impact analysis.

4.1

Preliminaries: Factory-level Analysis

To analyze the relationship between the introduction of steam power, and factory size and labor productivity,
I use the following specification
Outcomeit = α1 Steam Power Fty.it + County * Year FE + County * Industry FE + uit,(1)

where Steam Power Fty.it is a binary variable, which takes a value of one if firm i is using steam power attime t. Note that because the source data lacks a common identifier for individual factories, I cannot include
factory fixed eﬀects. As an alternative, I include interaction terms with country and industry dummies.41
Table 2 presents the results for factory size and share capital. Columns (1) to (3) show the relationshipbetween the adoption of steam power and the number of workers. The estimates in column (1) indicate thatfactories that use steam power are roughly 58 percent larger than those that do not.42 I find similar results
for share capital, which is consistent with factories’ issuing stock to fund the adoption of new technology.
Columns (4) shows that steam-powered factories have 78 percent more share capital than those without steam
power, and we have similar the point estimates in column (5) and (6).
Table 3 shows the impact on labor productivity, measured by the log revenue per worker. Columns (1) to(3) use diﬀerent combinations of fixed eﬀects, and columns (4) to (6) add share capital as additional control.
Panel A uses all available data. However, revenue is only observed for one year in some prefectures. In Panel
B, I estimate the model using the subset of prefectures where revenue data is available for multiple years.
Both panels show a positive relationship between labor productivity and the use of steam power, even after
controlling for share capital. In column (6), for example, steam-powered factories have 49.7 percent higher
productivity than non-steam-powered ones have.
Overall, although these results are not free from selection bias, the introduction of steam power is asso41

Only Factory Catalog has industry classification, so for other data sources I use the product type to classify factories.
Atack et al. (2008a) estimates a similar equation using US data from 1880, and finds a coeﬃcient of 1.281 for steam power and
0.760 for water power. When I include a water power dummy, both estimated coeﬃcients are smaller, but the diﬀerence between
the two is similar.
42

18

ciated with a larger number of workers, higher share capital, and higher labor productivity. All these results
are consistent with the assumptions of this paper.

4.2

Main Result: Eﬀect of Railroad on Technology Adoption in 1888-1902

Now, I will analyze the impact of railroad access on technology adoption using data from 1888 and 1902 or
1903.43 Since the government will construct railroads endogenously, I will rely on the instrumental variable
explained in the previous section to estimate the causal impact of railroad access.
First, I examine the relationship between the actual railroad network and my instrument. Figure 15 plots
both the railroad network and the cost-minimizing path, showing the relevance of the instrument graphically. To see how the relationship between the cost-minimizing and actual paths evolves over time, I nonparametrically estimate the relationship between distance from the cost-minimizing path and railroad accessin selected years. Figure 16 plots the resulting functions. In 1880, when only a small part of the railroad
network had been constructed, there was no relationship. In 1888 and 1892, counties closer to the costminimizing path were more likely to have railroad access. Subsequently, government support of railway
construction led to an increase in the access for counties far from the cost-minimizing path. The line for1902 shows that between 1892 and 1902, the same amount of railroad was constructed across all counties.
By 1919, due to catch-up in previously non-prioritized counties, the gap in railroad access has disappeared,
and the function is flat. In Table A.3, I show that this relationship is true even after including the controls.
I next estimate the impact of railroad access on various outcomes, using the cost-minimizing path as an
instrument, as seen in Figure 16. The main specification is
Outcomeit =α1 Railroad Access (-1892)it + Local Geographical Controlsi ∗ Year FE(2)+ (Local Other Controlsi ∗ Year FE) + Year FE + County FE + eit
Railroad Access (-1892)it =Distance to the Pathi ∗ Year FE + Local Geographical Controlsi ∗ Year FE+ (Local Other Controlsi ∗ Year FE) + Year FE + County FE + eit
where Railroad Access (-1892)it is a binary variable. When t ≤ 1892, it takes a value of one if county i has
railroad access in year t. When t > 1892, it takes the value it had at t = 1892. The treatment variable is43
I focus on the medium-term eﬀect here, as I expect the eﬀect of railroads to permeate gradually. This is because our outcome
variable is the result of investment, unlike R&D expenditure.

19

defined diﬀerently from the previous analysis. I construct it in this way since my instrument is related torailroad construction before 1892. This means that the treatment eﬀect should be interpreted as the eﬀect of
having access to the railroad network before 1892, which will be less than the simple unconditional eﬀect ofrailroad access.4445
Table 4 presents the estimates of the eﬀect of railroad access before 1892 on the adoption of steam power
in a county. Panel A contains the estimated eﬀect on steam power measured by its horsepower, with column(2) adding the initial population density and the presence of traditional or main traditional roads, both ofwhich are correlated with the instrument in the cross section as additional controls. In columns (3) and (4), I
added the distance to the destinations as an additional control to columns (1) and (2). The point estimate isaround 300 in column (1), and the result is robust by including an additional control in columns (2) to (4).
To understand the magnitude of the eﬀect, I multiply the eﬀect with the number of counties that obtainedrail access between 1888 and 1892, and find that the growth in rail access between 1888 and 1892 accounts
for 67 percent of the growth in steam power between 1888 and 1902. In Panel B, I use a slightly diﬀerent
measure of technology adoption, the number of steam power engines. In all specifications, the coeﬃcients are
significantly positive, and the point estimates do not change substantially over diﬀerent sets of controls. The
point estimates are around 21 and by similar calculation, the growth in rail access between 1888 and 1892
accounts for 97 percent of the growth in the number of steam engines between 1888 and 1902. Although
this calculation assumes that LATE is equal to ATT, but overall, a large amount of technology adoption is
explained by more widespread railroad access.

4.3

Robustness Checks

First, I analyze the direction of the bias of OLS. Table A.4 in the Appendix, I perform the same regression
analysis with Table 4 without the use of instrumental variables. I find that the OLS estimate is biased downwards, possibly due to the government favoring less developed areas. Agrawal et al. (2016) notes that thisis a typical result in transportation literature. Alternatively, the result may be due to the diﬀerence between
ATT and LATE. The instrument captures the main lines for long-distance transportation, not branches, andthese main lines may be better connected and oﬀer better service than branches do.
44
I choose distance from the cost-minimizing path as my instrument, rather than a binary variable taking a value of one forcounties on the path, as the fit of the first stage is better. Figure 16 shows that the relationship is close to linear in 1892.
45
This “hypothetical” path is a better instrument than government plans commonly used in the literature, because it is constructedusing only geographical information, whereas government plans may depend on other economic or political factors. After controlling for local geography, the only variation in my instrument is whether the county lies on the cost-minimizing path, so thequasi-random assignment and exclusion restrictions are likely to be satisfied.

20

In Table A.5, I investigate the eﬀect of railroad access on other outcomes, the number of (steam-powered)factories, the proportion of the population working in (steam-powered) factories, and the population with thesame sets of specifications with Table 4. The results indicate that railroad access increased the total numberof steam factories, population, and the proportion of workers employed by steam-powered factories, but not
the total number of factories or the proportion of workers employed in any factory, although some results
are not significant in some specifications. This is consistent with railroad access accelerating technology
adoption, but not the entry into factory-based production.46 47
We can use the result of the number of steam-powered factories to study the eﬀect of railroads on the
productivity of factories. The results in column (6) of Table 3 imply that steam power raises productivity by
49.7 percent.48 The estimates in column (1) of Panels A and B in Table A.5 depict that growth in railroad
access between 1888 and 1892 accounted for 29.5 percent point of the proportion of steam-powered factories
in 1902. Together, these estimates imply that access to a railroad network increases factory productivity by
16.2 percent. This figure does not correct for selection eﬀects, and therefore should be treated as an upperbound. However, it provides a rough estimate of the eﬀect of railroads on productivity.4950
I next perform a falsification test of the assumption of quasi-random assignment by examining population
growth. Between 1879 and 1880, no county gained access to the railroad network,51 and between 1879 and
1882, only six counties gained access. If the instrument is quasi-randomly assigned, it should have no
impact on the population in this period. As my headline specification includes county fixed eﬀects, the test
46
Although the proportion of people working in factories did not increase, this is not a measure of structural change. Matsumoto
and Okuda (1997) and Nakamura (1971) note that there is a non-factory-based sector connected with the factory-based sector.
47
As a robustness check, I include prefecture-level geographical controls and cluster standard errors at the prefecture level, as
county-level geographical controls may be too restrictive. Table A.6 shows the results. The general pattern is similar to the results
without prefecture-level controls.
48
This is a comparison between steam power and non-steam power. When I include a water power factory dummy in the analysis,
the diﬀerence between water power and steam power is 30 percent, and that between human power and steam power remains 50
percent, and both eﬀects are significant. I use a simple steam versus non-steam comparison because water power is only feasible
near steep rivers, and is not suitable for industries that require a constant amount of energy input. Atack et al. (2008a) finds that thediﬀerence between steam power and water power, in terms of log revenue, for establishments with more than 16 employees is 0.181
without controlling capital per worker, and 0.201 when controlling for capital per worker. The estimate with capital per worker issimilar to my result. However, capital in Atack et al. (2008a) is defined as the actual capital invested, whereas capital in this paper
refers to the share capital, so the two specifications are not identical.
49
Bias may also arise due to the use of revenue productivity rather than physical productivity. Productivity will increase following
the adoption of new technology, but a monopolistically competitive firm will reduce prices in response to the decrease in marginal
cost, and this will have a negative eﬀect on revenue productivity. However, separating these eﬀects is impossible in my data.
50
Minami (1979) examines the adoption of the power loom by the Japanese textile industry, and its eﬀect on industry productivity.
Using cross-sectional diﬀerences in the adoption rate in 1924, he finds that in the cotton industry, a one percentage point increasein the adoption of the power loom leads to an increase in labor productivity of 0.91 percent (0.59 percent conditional on capital per
worker), in the silk industry the estimated eﬀect is 0.57 percent (0.12 percent conditional on capital per worker). However, by 1920
the dominant source of power was electricity, so the eﬀect of the power loom will not be the same with the eﬀect of steam power.
51
At this point, there were already short lines in the Tokyo and Osaka areas.

21

is to determine whether railroad access aﬀects the trend.
Results are contained in Table 5. It estimates where the dependent variable is population growth in 1879–
1880 in columns (1) and (2), and 1879–1882 in columns (3) and (4). Similar to the main results, I estimate
all models with and without additional non-geographical controls.52 I find no significant correlation in any
columns. IV is not correlated with economic growth proxied by population growth before it could start toaﬀect thorough railroad construction, and there is no evidence of the violation of quasi-random assignment.
I now test for the exclusion restriction. This restriction would be violated if being on the cost-minimizing
path aﬀects the outcomes through some channel other than railroad access. I test it informally by checking
whether there is any eﬀect of the IV on a potential channel, which might aﬀect the outcomes. Specifically, I
use prefecture-level data on government infrastructure spending, as the government may increase spending
on other forms of public infrastructure such as roads or irrigation at the same time as constructing the railroad.
I digitize its data from the Home Ministry’s Statistical Yearbook and estimate the following specification.

(3)

Outcomeit= α1 Prop of Railroad Access County (-1892)it + Prefecture Geographical Controlsi ∗ Year FE+ Year FE + County FE + eit
Prop of Railroad Access County (-1892)it(4)= Ave. Distance from the Pathi ∗ Year Trend (-1892) + Prefecture Geographical Controlsi ∗ Year FE+ Year FE + County FE + eit
Outcomeit is public infrastructure spending in prefecture i for year t. For t ≤ 1892, Prop of Railroad Access
County (-1892)it is the proportion of counties with railroad access in prefecture i for year t, t > 1892 it
takes its value at t = 1892. Prefecture Geographical Controlsi and Prefecture Geographical Controlsi are
the same controls used in county-level analysis, recalculated at the prefecture level. Ave. Distance fromthe Pathi is the average of the distance from the cost-minimizing path, taken across counties in prefecturei. Instead of using year fixed eﬀects, I use Year Trend (-1892), which is linear trend for t ≤ 1892 and 1892
afterwards to be interacted with Ave. Distance from the Pathi to increase the F-value in the first stage.
Table 6 shows the results. Columns (1) and (3) do not use any instrument for the railroad variable, while
52

Since I control for log area, the level of log population is the same as log population density.

22

columns (2) and (4) do. Columns (1) and (2) use levels, while (3) and (4) use a log specification. The point estimates are negative, and insignificant at the 10 percent level. Furthermore, the mean of Public Expenditureit
is 514, 013, so the point estimates in columns (1) and (2) are not economically significant. It does not appear
that the exclusion restriction is violated by public spending.
Further robustness checks are presented in the Appendix. In Table A.7 and Table A.8, I check forrobustness to the choice of cost function used in calculating the cost-minimizing path. In Table A.9, I control
the distance to the destinations and exclude the counties adjacent to the destinations to exclude potential
spillover eﬀect from the destinations. In Table A.10, I see if dropping subsamples aﬀects the main results.
The main results are robust to these modifications. In addition, Redding and Turner (2015) points out thatthe estimated treatment eﬀect may just be a displacement eﬀect. In other words, the estimated eﬀect inthe main result is overestimating the eﬀect due to negative externalities. To test this possibility, I first use
a diﬀerent treatment variable, Railroad (-1892) within 50 kmit which takes one if Railroad (-1892)it takes
one or Railroad (-1892) jt of any of neighbor counties within 50 km takes one. Because out of counties
which switch on the new treatment variable between 1888 and 1902, 29 per cent counties switch on due
to their neighbor’s access, we will see a huge drop in the coeﬃcient when displacement eﬀect is large.
However, when comparing column (1) and (2) in Table A.11, we do not see a huge diﬀerence in the eﬀect on
steam power. The result is similar in column (4) and (5), using the number of steam engines, suggesting nodisplacement eﬀect. I also see the eﬀect of having railroad access only in their neighbor counties to capture
local externalities directly. In column (3) and (6) I use Neighbor’s Railroad within 50 kmit which takes one if
Railroad (-1892) within 50 kmit takes one and Railroad (-1892)it take zero. I instrumented this by Adjacentto the Pathi ∗ Year FEt where Adjacent to the Pathi takes one if Distance to the Pathi takes from 20 km to
50 km.53 If there is displacement eﬀect, its coeﬃcient should show negative sign, but column (3) and (6)shows no significant eﬀects. Another way to see displacement eﬀect will be to compare counties in Shikoku
island, the island in the south without any destination shown in Figure 15 with counties in the other threeislands far from the path. If displacement eﬀect existed and only occurred within islands, counties in Shikoku
island should have higher growth than counties far from the path in the other islands. I analyze this point inTable A.12, but I do not see significant diﬀerence between them. Of course, displacement may occur acrossislands, but this is diﬃcult to characterize without knowing the form of the entrepreneur’s entry decision
problem, and diﬃcult to observe in a small area like Japan. In Table A.13, I conduct analysis regarding53
During 1888 and 1892, counties in this region are railroad access three percent (insignificant) more likely to get railroad accessthan counties which is more than 50 km from the path are, so there is minimal bias from the direct impact of railroad access.

23

which variables are aﬀected in the short term. I find the eﬀect on steam power although the its magnitude ismuch smaller than in the main result as expected.
I now investigate whether channels other than market access are responsible for the main results54 . The
diﬀerence between migration and market access channels is in the timing of the eﬀects. Table A.13 shows
the short-term reduced form eﬀect of railroad access on technology adoption and population. In the shortterm, the only significant eﬀect is on technology adoption in Panel A, and not population in Panel B.5556
Table A.14 investigates whether the short-term impact is heterogeneous. The results imply that there wasno urban-rural migration by railroad. Overall, these results indicate that migration due to railroad accessdoes not occur before or at the same time as technology adoption, which is inconsistent with the migration
channel being the main channel. To examine whether the cost channel is driving my results, and also to
examine heterogeneity of the eﬀect of railroad access between rural and urban areas, I estimate a modelallowing for heterogeneous impacts. Table 7 shows the results. Columns (1) to (3) present the estimates ofshort-term eﬀects using annual data from 1888 to 1891, and columns (4) to (6) present estimates of mediumterm eﬀects using data from 1888 and 1902, as shown in the main results. In both cases, the estimatesindicate that the eﬀect was stronger in countries with a higher population density. This is consistent withthe presence of credit constraints in rural areas, and implies that the distance from Tokyo and Osaka is notimportant, which is not what we would observe if the cost channel was important. In Table A.15 of the
Appendix, I show that the distance from areas where coal is produced is not significant, so the coal price54
If the eﬀect of railroad access on trade costs can be observed, then this data can be used to test whether trade is a key mechanism.
However, there is no data on trade costs before the railroad era, and even if there were, it is not clear how these trade costs could besummarized into a single variable, as my framework does not allow for multiple or asymmetric regions. The impact could possibly
be greater for inland areas as they do not have access to marine transportation. I tested for heterogeneity in the impact of railroad
access using distance from the coast, but did not find any robust pattern. There are two potential reasons for this. First, the costminimizing path mostly follows the coast, so there is not enough variation to detect heterogeneity. This problem of insuﬃcientvariation also applies to the distance from ports open for foreign trade. Second, marine transportation may also be conducted using
rivers, so the distance from the coastline may not be an appropriate proxy for trade costs before railroad construction. Anotherpossibility is to construct market size explicitly as in Donaldson and Hornbeck (2016). This does not work in my context because
undulate geography and the lack of trade cost data before railroads came prevent us to make a meaningful measure of market size.
However, Figure 7 shows that the amount of freight traﬃc on the railways increased as the network expanded, and the growth of
freight was higher than that of passenger traﬃc. Hence, I believe that trade is the driving force, as explained in the conceptual
framework.
55
Hornung (2012) estimates the eﬀect of railroads on population growth for cities in Prussia from 1840 to 1864 by using a straightline between the cities as an instrument for the railroad. His estimates are similar to the ones presented here.
56
Kim (1995) estimates the impact of steam power adoption on the location of firms in the U.S. between 1850 and 1880, andfinds the eﬀect negligible. The analysis in this case is, however, limited to a simple cross-sectional regression, with no instrumental
variable or natural experiment. Atack et al. (2010) estimates the eﬀect of railroads on population density and the rural population in
the Midwest United States between 1850 and 1860, and finds a roughly 3 percent increase in both. In this case, the initial estimates
are obtained using a Diﬀerence-in-Diﬀerence estimator, and further estimates are obtained by using a congressional survey as an
instrument. The results of the IV estimation imply that Diﬀerence-in-Diﬀerence is biased downward, although the IV estimates are
imprecise.

24

channel will not be the main force.
Another possible channel is the information channel. The results in Conley and Udry (2010), Agrawal etal. (2016), and Catalini et al. (2016) suggest that introducing railroads to an area may transmit informationsuch as the existence of steam engines and the profitability of the firms using them. I test this channel bylooking at the impact of railroad on the number and total days of expositions held at prefecture. Expositions
played a major role to share in the industrial sector (Kiyokawa, 1995), railroad may increase expositions
by reducing transportation cost to invite speakers. Using the same specification with Table 6, Table A.16
shows no eﬀect of railroad on expositions. Also, there are many cases where factories adopted steam power
even without access to a railroad, which suggests that the eﬀect through this channel is minor. In 1888,
101 counties contained at least one steam-powered factory, and 82 of them did not have railroad access.
Therefore I do not think that the information channel is working in my context. Another potential channel iswithin-city commuting, as suggested by Duranton and Turner (2012). However, commuting by train would
not have been aﬀordable for most workers. The average daily wage in 1902 was between 0.3 and 0.7 Yen,57
while the cheapest ticket from Tokyo (Shimbashi) to Yokohama, a commute of 30 km and 45 minutes, was
0.3 Yen.58 Therefore, the eﬀect of within-region commuting is most likely small.
The results so far indicate that the market access channel is likely to be the main forces behind the eﬀectof railroad access. It is also possible to discern whether the incentive to adopt new technology is driven by
improved access to domestic or foreign markets. If domestic market integration is important, then railroad
access should aﬀect purely domestic industries. I focus on two domestic industries, brewing and printing,both of which have low export shares as they produce goods for the domestic market.59 I then used steam
power in these industries as an additional outcome. I also showed the results from the mining and textile
industries, both exporting industries, for comparison.60
Table A.17 shows the estimates of industry-specific eﬀects. The specification is the same as column (1)57This estimate is obtained from Umemura et al. (1965).
Nihon Kokuyu Tetsudo (1997).
59
I consider industries with more than 200 factories (2.6 percent of the total) in 1902, used estimates by Taguchi (1994) tocalculate the export share of each, and selected the industries with an export share of less than 10 percent. I construct the brewing
industry as an aggregate of miso, soy sauce, sake, and beer manufacturers. The export share of this aggregate is 2.5 percent. No
exports were recorded for printing. The tobacco industry also had an export share of 2.5 percent, but was monopolized by thegovernment in 1898, so I have excluded it. Explosion material, mining, ceramic, and textile industries had more than 200 factories,
but were not categorized as domestic.
60
In total, these two industries account for 50 to 60 percent of the total value of exports in the 1880s and 1900s. In the data from
1909, both industries also exhibit a fairly high export rate, defined as the value of exports divided by the value of total production.
The textile industry itself has a low export ratio, but this industry is diﬃcult to distinguish from the yarn industry in my data, so I
work with their aggregate, which I define as the textiles and related products industry.
58

25

of Table 4. Column (1) shows the result for the domestic industry, which I consider significant.61 Other
columns show the results for the mining and textile industries. The eﬀect on the mining industry is huge
and accounts for roughly two-thirds of the overall eﬀect. The estimated eﬀect for the textile industry, shownin column (4), is positive but insignificant. I also show the coeﬃcient divided by the standard deviation ofoutcomes in the row below the standard error. These results indicate that the eﬀect of railroad access aﬀects
both the domestic and exporting industries by a similar size.62 The eﬀect on the exporting industry will havean implication on external validity. If a country has a comparative advantage in the agricultural sector, thenits railroads may aﬀect the agricultural sector, and not the industrial sector.
I consider other important policies or technological progress in my sample period to discuss whether it
might aﬀect my main result.63 For the policies, I consider modern post oﬃces, telegraphs, and telephones
as important upgrades in communication infrastructure after the Meiji Restoration because communication
costs might aﬀect technology adoption and the government might have similar networking motivation forcommunication infrastructure as for transportation infrastructure. The modern post oﬃce was introducedin 1872 and covered the entire nation in 1873 in cooperation with village leaders, so it will not aﬀect ouridentification strategy. Similarly, the telegraph was introduced in 1869 between Tokyo and Yokohama, andstarted to expand in 1874. By 1878, almost all the capital cities of the prefectures had obtained telegraphstations, so this would also not aﬀect the main result. 64 The telephone network spread in 1898, so this might
be aﬀected by the cost-minimizing path. During that time, telephone service was restricted within the city
area, so I exclude seven counties that obtained telephone service by 1902, and estimate the same model withthe main result, but the result does not change (See Table A.18). As an important industrial policy, Meiji
Government established state-owned factories in 1870s. These factories tried to adopt Western technologies
to demonstrate them for the private sector. Though many of them are not profitable, it might aﬀect the main
result. Table A.19 shows the result without counties where state-owned factories are located,65 the result
61

Tanimoto (2006) finds that firms in the domestic industries are run by local elites.
Even without technology adoption in the exporting industry, structural change can occur by exposure to the foreign market,
and specialization in non-agricultural goods due to comparative advantage. However, this would imply that the relative price ofindustrial goods increases after a county gains access to the railroad network, whereas the trend in the aggregated data is the
opposite. Alvarez-Cuadrado and Poschke (2011) uses similar reasoning to argue that trade does not explain the decrease in the priceof industrial goods and the increase in the industrial sector’s share of workers. I also show more detailed export data in Figure A1.
It shows the growth in the openness and the trade volume with developed countries and of metal and textile industries, and no trade
growth in agriculture and machinery industries. Figure A2 shows industry-specific relative price data and confirms that the exporting
industry also experienced a drop in the price over the sample period.
63
The issue of specification error for IV is covered in Figure A3, Table A.7 and Table A.8.
64
Telegraph was available even without telegraph stations through post oﬃces covering the entire nation. In their public announcement, the Ministry of Technology instructed how to use telegraphs in the absence of telegraph stations. Source:http://dl.ndl.go.jp/info:ndljp/pid/796222
65
The list of factories are available by Kobayashi (1977)
62

26

do not change substantially. Also, the new government tried to improve credit market and it might aﬀect
technology adoption through credit constraint. However, they made an act to establish national bank system
in 1876 and private banks cover the nation uniformly by the end of 1870s. Accordingly, Table Table A.20
implies that the instrument is not correlated with interest rate in the initial period and railroad construction
does occur in area where interest rate was getting lower, so credit market story will not explain the main
result. Steamship is another concern, because it became popular during my sample period and decreased thetrade cost like the railroads do. However, it will aﬀect the coastal areas more, and the cost-minimizing path
is located inland on average. In addition, since the distance to the coast is controlled in all the specifications,
I do not think that the adoption of steamships drives the main result.66

5

The Eﬀect on Modern Economic Development

As I discuss earlier, technological progress in the industrial sector will induce structural change by laborpull eﬀect67 and urbanization by breaking the pattern of mean reversion in population. In this section, I
examine the eﬀect of earlier railroad access on structural change and urbanization, common features ofmodern economic development.

5.1

Structural Change

Here, I examine whether gaining railroad access induces structural change. I cannot analyze the short- andmedium-term eﬀects as the sectoral employment data is not available. However, by using 1880’s Huken
Tokeisho and the 1920 Population Census, I can analyze the long-term eﬀects. I use the following specifica-

66

As another technological change in the transportation infrastructure, Chandler (1977) documents that some firms producing
perishable products such as milk or meat adopted refrigerated railroad cars in the context of U.S.. However, refrigerated railroadcars will be interpreted as the outcome of railroad access if factories invest as in the case of U.S. or a part of the treatment if railroad
companies introduced. Also, it does not aﬀect the main result because it was introduced to Japan in 1908.
67
Railroads might aﬀect industrialization through regional specialization. However, there is no strong reason to believe thatrailroad access will induce industrialization on average. Also, it might induce structural change through the potential eﬀect on
agricultural productivity, but this will be less important as shown in Figure 2 and Figure 3 in my context. The potential reasons why
railroad access did not induce technology adoption in the agricultural sector are that farmers are credit constrained, new Western
technology was not available, Western technology was not directly applicable to Japanese crops, and the technological gap between
Japanese and Western technology in the agriculture was not as huge as one in the industrial sector. Hence, in this paper, I think laborpull eﬀect as the main channel.

27

tion68
Primary Worker/Total Workeri = α1 Railroad Access (-1892)i + α2 Local Controlsi(5)+ α3 Initial Agricultural Worker / Populationi + ei
Primary Worker/Total Workeri is the primary sector’s share of workers in county i in the 1920 census.
Railroad Access (-1892)i is a dummy variable, which takes the value of one if county i had gained railroad access by 1892. As before, Railroad Access (-1892)i is instrumented by Distance to the Pathi . Initial
Agricultural Worker/Populationi is the share of agriculture workers in the total population of county i, calculated from the population data of 1885 and the number of agricultural workers in either 1884, 1885, or1886.69 Although this control and outcome are not identically constructed, they are conceptually similar.
Since the outcome variable is only available for 1920, this is a cross-sectional analysis. However, I control
for the initial level of industrialization using Initial Agricultural Worker/Populationi .
Table 8 presents the results. In column (1), I find a significant negative relation between the two, as
expected. This finding is robust after controlling the time-variant eﬀect of traditional roads in column (2). In
column (3), I also include the time-variant eﬀect of main traditional roads and lose significance, but this isbecause of higher standard error rather than omitted variable bias. To confirm that railroad access aﬀects the
manufacturing sector rather than the service sector, I use the share of workers in the industrial sector as thedependent variable in columns (4) to (6), and estimate the eﬀect with the same sets of controls with columns(1) to (3). The size of the eﬀect is similar to that in columns (1) to (3), so the negative eﬀect on primary sector
employment is due to the industrial sector, rather than the service sector. In column (4), the magnitude is
5.7 percent, and this corresponds to 0.36 of a standard deviation. This estimate may be attenuated since after
1900 steam power had diﬀused widely and agricultural technology was upgraded, which induced widespread
structural change and narrowed the gap in structural change with the early adopters.

68

See Figure A4 and Table A.21 in Section A.9 for the long-term eﬀects on technology and population.
Since this variable is taken from the Huken Tokeisho, data availability varies by year and prefecture. To increase the sample
size, I used the earliest available data from these three years. Even so, no data was available for 87 of 487 counties used in the main
estimation.
69

28

5.2

Urbanization

To analyze the impact on urbanization, I estimate the following model for two data points, 1903 and 1920.
Log of Populationit − Log of Populationi,1885(6)= α0 + α1 Log of Population Densityi,1885 + α2 Railroad Growth 1885-1892i+ α3 Log of Population Densityi,1885 ∗ Railroad Growth 1885-1892i + α4 Local Controlsi + eit
where Railroad Growth 1885-1892i is Railroad Accessi,1892 - Railroad Accessi,1885 . As in the previous
analysis, this is instrumented by Distance to the Pathi . This variable captures the eﬀect of railroads constructed by priority, and will have a long-term eﬀect after 1982, when the new railroad law was enacted
and counties that were not prioritized began to catch up. Local Controlsi contains the set of geographical(and non-geographical) controls as in the other analysis. Due to the mean reversion eﬀect in pre-modern
agricultural economies, I expect α1 to be a negative coeﬃcient. I expect α2 and α3 are expected to bepositive coeﬃcients because of technology adoption in the industry sector.70 The parameter of interest is
α1 + α3 ∗ Railroad Growth 1885-1892i . This determines whether bigger cities could grow more, and if wehave a fatter tail on the right side in the distribution of the log of population density as we see in Figure 13,
then it also determines how it diﬀers between early and late adopters of railroads. If Gibrat’s Law, thebenchmark in the population dynamics of the modern economy, holds for any sample, α1 and α3 will have a
coeﬃcient of zero.
Table 9 contains the results. To simplify the interpretation of the eﬀects, I normalize all controls. For all
the years and specifications, α1 has a negative sign and the interaction terms, α2 and α3 have positive signs,
as expected. Moreover, the estimated coeﬃcient of Log of Population Densityi,1885 when Railroad Growth,
1885–1892i = 1 is less negative and even becomes positive in some specifications. In columns (1) and (3),the overall eﬀects are -0.0189 and 0.113, suggesting that the urbanization eﬀect is stronger in the long term. I
obtain similar estimates in columns (2) and (4), when access to (main) traditional roads is added as a control.
These results are consistent with the technology adoption and structural change caused by railroad accessinducing urbanization by breaking the pattern of mean reversion.71
70

Michaels et al. (2012) shows that conditional on productivity, manufacturing areas should have higher population density
because production is less land intensive.
71
If railroads aﬀect population dynamics by reducing migration costs, then the negative slope of Log of Population Densityi,1885
would become steeper when interacted with railroad access. This is because the mean reversion process will be accelerated bycostless migration. My estimate is precisely the opposite.

29

6Conclusion

The central contribution of this paper is to find a causal impact of gaining access to a railroad network ontechnology adoption in the industry sector, which is a key mechanism that explains why railroads would be amajor impetus for structural change and urbanization. To achieve this, I digitize a novel county-level data set
covering Japan from the 1880s to 1920s, a period that witnessed railroad network expansion, transformationof the industrial sector by steam power, and structural change and urbanization. I start the analysis byusing factory-level data and confirm that steam-powered factories are bigger and more productive, whichis consistent with the key assumption that steam power has a high fixed cost but low marginal cost. Then,
I perform DID-IV estimation by calculating the cost-minimizing path between destinations, and using the
distance from this path to predict the growth in railroad access by 1892. I find that gaining railroad accessby 1892 increased steam power in 1902. These estimates imply that railroad access growth from 1888 to1892 accounts for 67 percent of the growth of steam power from 1888 to 1902. The eﬀect is smaller for lesspopulated areas, suggesting its disadvantage for technology adoption such as credit constraints. We observe
the eﬀect for both the domestic and export industries, which highlights that railroads access integrated theregions internally and externally.
As additional results, I find that railroad access aﬀects structural change and urbanization. As in the laborpull story, gaining railroad access by 1892 increased the share of workers employed outside the agricultural
sector in 1920, and most of the eﬀect is explained by the increase in workers in the manufacturing sector.
It also breaks the pattern of mean reversion in population dynamics during 1885–1903 or 1920, which isan important change for big cities to emerge. Overall, these results are consistent with the picture thatrailroads induce the two features of modern economic development, structural change and urbanization,
through technological progress in the industrial sector.
The findings suggest a view on the history of take-oﬀ of the Japanese economy. Before the 1850s, thediﬀusion of Western technology was mostly blocked by the restrictive trade policy by Tokugawa Shogunate.
By the foreign pressure from Western countries, the Shogunate was forced to open several ports for foreign
trade in the late 1850s, but the fear of colonization resulted in the collapse of the Shogunate in 1868. The
new Meiji Government, a more centralized one than the Shogunate, tried to adopt Western technology tostrengthen their state capacity to avoid colonization. Railroad network was one of them, and this adoption
would not be possible without the Meiji Government because constructing a railroad network required significant government spending, coordination, and a positive attitude towards Western technology. If this is30the case, then the findings of this paper support the view that the centralization and modernization of thegovernment in 1868 aided Japan’s economic development through public goods provision, that is, railroad
construction.

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37

Figure 1: Railroad Network

The green lines represent those that existed in each year. See subsubsection 3.1.2 for the source

38

Figure 2: Adoption of New Technology in the Industrial and Agricultural Sectors

4

6

8

10

12

Technology Adoption in Industrial / Agricultural Sector

1880

1890

1900

1910

year
Engine Power [Log HP]
Fertilizer [Log Quantity, Nitrogen]

Steam Power [Log HP]

Source: Minami (1979) for steam power and Umemura et al. (1965) for fertilizers. These show aggregated level numbers.

39

Figure 3: Relative Price (Industry/Agriculture)

.2

.25

.3

.35

.4

Drop of Industrial Goods Price

1880

1890

1900
year

Industry Price / Agricuture Price
Fitted Values [1884-1900]

1910
Fitted Values

Source: Umemura et al. (1965) as used in Alvarez-Cuadrado and Poschke (2011)

40

1920

Figure 4: Decline of Primary Sector, 1885–1920.5

Agricultural Worker/All Worker
.55
.6
.65

.7

Decline of Primary Sector

1884

1900
year

1910

1920

Source: Meiji iko Hompo Shuyou Keizai Tokei (Major Economic Statistics After Meiji Period) published by Bank of Japan in 199941

0Kernel Density Estimation
.2
.4

.6

Figure 5: Population Density in 1885, 1902, and 1920

2

4

6
Log of Population Density
1885
1920

8

10

1903

Notes and Source: The data is based on the 1885 and 1902 population statistics and the 1920 census. Section 3.2.2 provides moredetailed information about the data source.

42

6

Log of GDP per capita in 1990 USD
7
8
9
10

Figure 6: Log of GDP per Capita, 1800–2000

1800

1880 1900
1868 1890 1910
year
USA
Japan

1945

2000

England/GB/UK
China

Source: The Maddison-Project, http://www.ggdc.net/maddison/maddison-project/home.htm, 2013 version.

43

Figure 7: Freight and Passenger Traﬃc by Railroad

Growth in Railroad Service from 1890
Quantity*Km

1

0

5000

1.2

10000

1.4

15000

20000

1.6

N of Cars

1890 1892 1894 1896 1898 1900 1902
1891 1893 1895 1897 1899 1901
year

Freight

1890 1892 1894 1896 1898 1900 1902
1891 1893 1895 1897 1899 1901
year

Passenger

Source: Meiji Iko Hompo Shuyo Keizai Tokei(Nihon Ginko Tokeikyoku, 1997). In the left panel, the Y-axis depicts the total numberof cars for freight (red and solid line) or passengers (blue and dashed line), divided by their level in 1890. In the right panel, theY-axis shows million ton * km for freight and million passenger * km for passenger, divided by their level in 1890.

44

Figure 8: Construction Work in Steeper Areas (The Usui Pass)

Source: Nihon Kokuyu Tetsudoshi

45

Figure 9: Abandoned Line due to Steepness

The green lines are those that existed in 1885. The red dotted lines depict the original plan to connect Tokyo with the West, but wasabandoned. The brown line depicts the line that subsequently connected Tokyo with the West.

46

Figure 10: IV Conditional on Local Geography

Path APath B

Unconditional IV

Conditional IV

47

Figure 11: Regional Pattern of Steam Power Usage between 1888 and 1902

48

Log (Primary Sector Worker / Total Worker)
-2.5
-2
-1.5
-1
-.5
0

Figure 12: Technology in Factories and Primary Sector Employment Share in 1920

-2

-1

0

Log of Rice Productivity
Log of Horsepower per Worker

12(Linear Projection)(Linear Projection)

Notes and Source: Steam power data is from the 1919 Factory catalog and primary sector employment share data is from the 1920
census. To calculate the total workers, I add agriculture, fishery, manufacturing, and mining industry workers, to divide the steam
power and primary sector workers (agriculture and fisheries). Section 3.2.2 provides more detailed information about the data
source. Rice productivity is the mean of mesh-level crop suitability of rain-fed with low-fertilizer type rice, obtained from the FAOGAEZ data. I used the same sample in the main analysis. For readability, the figure excludes samples with more than two standard
deviations from the mean in either of the x variables.

49

Figure 13: Population Density and Primary Sector Employment Share in 1920

0Share of Primary Sector Wokers In 1920
.2
.4
.6
.8
1

Local polynomial smooth2

4

6
Log of Population Density

8

10

kernel = epanechnikov, degree = 0, bandwidth = .35

Notes and Source: Primary sector employment share is from the 1920 census. To calculate total workers, I add the agriculture,
fishery, manufacturing, and mining industry workers, to divide the horsepower and primary sector workers (agriculture and fisheries).
Section 3.2.2 provides more detailed information about the data source. Log of population density is also based on the Population
Census.

50

Figure 14: Traditional Roads Built before 1868

Source: Takeuchi (2006). Blue lines indicate traditional roads and red lines are the Gokaido, five major roads directly controlled by
Tokugawa Shogunate during the Edo period (1615-1868).

51

Figure 15: Railroad Network in 1892 and Cost-Minimizing Paths

The green lines represent the railroad network in 1892 and the red lines depict the cost-minimizing path.

52

0

.2

Railroad Access
.4
.6

.8

1

Figure 16: Unconditional Relationship Between IV and Railroad Access

0

.2

.4
.6
Distance from the Path
1880
1892
1919

53

1888
1902

.8

1

Table 1: Descriptive Statistics: County-level Data

A. Initial Stage (1888)
1. Factory Data
Steam Power [horsepower]
Water Power [horsepower]
N of Steam Power Engines
N of Steam-Powered Factory
N of Workers in Steam-Powered Factory
2. Population Data
Population
Population Density (/km2)
3. Other Data
Railroad AccessTraditional Road
Main Traditional Road
B. Middle Stage (1902 or 1903)
1. Factory Data
Steam Power [horsepower]
Water Power [horsepower]
N of Steam Power Engines
N of Steam-Powered Factory
N of Workers in Steam-Powered Factory
2. Population Data
Population
Population Density (/km2)
3. Other Data
Railroad Access
C. End Stage (1919 or 1920)
1. Factory Data
Steam Power [horsepower]
N of Steam Power Engines
N of Steam-Powered Factory
Elec. or Gas Power [horsepower]
N of Elec. or Gas Power Engines
N of Elec. or Gas-Powered Factory
N of Workers in Steam-Powered Factory
2. Population Data
Population
Population Density (/km2)
3. Other Data
Railroad AccessObservations

mean

sd

min

max

12.23819
2.057495
1.209446.5770021
70.78029

63.26859
8.296014
7.158499
2.47076353.955

0
0
0
0
0

878
80
110
36
4917

68918.87
243.55436176.1
665.8755

13246
10.60407

2559859307.554.0616016.835729.137577.2406778.3709023.3448097

0
0
0

1
1
1

111.2263
22.26298
5.924025
3.478439
336.6858

425.1105
89.64335
14.451725.926075
740.0709

0
0
0
0
0

4107
971
155
57
7259

82831.4
293.3173

45262.23
815.558

16275
13.34293

43953111160.75.6406571.4803012

01

621.5736
11.7577
8.172485
1207.957
46.79466
20.05133
1088.122374.43
19.98597
14.89963
8418.538
130.7535
40.79971
2318.957

0
0
0
0
0
0
0

28129.5205
186174151.9
1565
396
26526

93255.39
342.6105

76286.78
1028.052

16364
15.79187

115697313599.35.7926078
487.4058558

01Railroad Access and (Main) Traditional Road are binary variables, which take the value of
one if there are railroads or (main) traditional roads, respectively. In Panels B and C, only
Population Data uses the data in 1903 and 1920, respectively, and Factory Data and Other
Data use data in 1902 and 1919, respectively. 54

Table 2: Factory-level Data: Steam Power and Log of Workers or Share Capital
Log Workers
Steam-Powered Fty.
County*Year FE
County*Industry (First Digit) FE
County*Industry (Second Digit) FE
Observations(1)
0.586∗∗∗(0.0697)
Yes
No
No
7194

(2)
0.593∗∗∗(0.0651)
Yes
Yes
No
6299

Log Share Capital(3)
0.624∗∗∗(0.0637)
Yes
No
Yes
6231

(4)
0.787∗∗∗(0.101)
Yes
No
No
7427

(5)
0.777∗∗∗(0.126)
Yes
Yes
No
6526

(6)
0.731∗∗∗(0.121)
Yes
No
Yes
6478

The county-level cluster robust SEs are in parentheses. + p < 0.1, * p < 0.05, ** p < 0.01, ***p < 0.001. Steam-Powered Fty. takes one if the factory is using steam power engines. Industries
are classified into Chemical, Machines, Tobacco, Gas and Electricity, Spinning and Weaving, Metal,
Mining, Food, and Miscellaneous as the first digit. There are 39 subcategories in the second digit.

55

Table 3: Factory-level Data: Steam Power and Log of Labor Productivity

Panel A: Full Available Prefectures
Steam-Powered Fty.

(1)

(2)

0.847∗∗∗(0.187)1.233∗∗∗(0.218)Log (Revenue / Labor)
(3)
(4)

(5)

(6)

0.221∗∗(0.0760)

0.577∗∗∗(0.108)0.410∗∗(0.143)0.538∗∗∗(0.0620)0.504∗∗∗(0.0771)

0.427∗∗∗(0.0843)0.778∗∗∗(0.163)0.417∗∗∗(0.108)0.652∗∗∗(0.0933)

0.497∗∗∗(0.112)

786
724
Yes
No
Yes

0.490∗∗∗(0.0764)
934
724
Yes
No
No

0.491∗∗∗(0.0844)
782
724
Yes
Yes
No

0.414∗∗∗(0.0880)
761
724
Yes
No
Yes

0.767∗∗∗(0.196)Log Share Capital/Workers
Panel B: Prefectures with Multiple Years
Steam-Powered Fty.
1.118∗∗∗(0.205)1.235∗∗∗(0.248)Log Share Capital/Workers
Observations in Panel A
Observations in Panel B
County*Year FE
County*Industry (First Digit) FE
County*Industry (Second Digit) FE

960
724
Yes
No
No

807
724
Yes
Yes
No

The county-level cluster robust SEs are in parentheses. + p < 0.1, * p < 0.05, ** p < 0.01, ***p < 0.001. Steam-Powered Fty. takes one if the factory is using steam power engines. Panel A uses all
the available sets of data, which uses the data in 1884–1893, while Panel B uses the data on prefectures
only, which I can obtain for more than one time period. As a result, Panel B uses Osaka (1886–1888),
Yamanashi (1885, 1887, 1888, and 1890), Hiroshima (1892–1893), Aichi (1886–1887), Shiga (1887–
1888), Fukuoka (1887–1888), Nagasaki (1887–1890), and Tottori (1889–1891). See Table 2 for thedescription of industry groups.

56

Table 4: Main Result: Eﬀect of Railroad on Technology Adoption in 1888-1902
DID-IV
Panel A: Steam Power [HP]
Railroad (-1892)
Panel B: Number of Steam Engines
Railroad (-1892)
County FE and Year FE
Sum of Slope * Year FE
Log Flat Area * Year FE
Log Area * Year FE
Distance to Coast * Year FE
Initial Population Density * Year FE(Main) Traditional Road * Year FE
Dist. to Destinations * Year FE
First F
N
Ng

(1)

(2)

(3)

(4)

299.0∗∗(102.0)304.3∗∗(110.6)256.3∗∗(89.07)255.5∗∗(93.49)21.16∗∗∗(6.015)
Yes
Yes
Yes
Yes
Yes
No
No
No
41.52
956
487

20.59∗∗(6.526)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
38.15
956
487

19.11∗∗∗(5.404)
Yes
Yes
Yes
Yes
Yes
No
No
Yes
40.91
956
487

18.25∗∗(5.766)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
36.48
956
487

N shows the sample size and N g shows the number of counties. The countylevel cluster robust SEs are in parentheses. + p < 0.1, * p < 0.05, ** p < 0.01,*** p < 0.001. First F shows the Kleibergen-Paap statistics to test for the
weak instrument. I use the data in 1888 and 1902.

57

Table 5: Falsification Test: IV and Population
Log Population
1879-1880
Distance from the Path * 1879
County FE and Year FE
Sum of Slope * Year FE
Log Flat Area * Year FE
Log Area * Year FE
Distance to Coast * Year FE(Main) Traditional Road * Year FE
Initial Log Population * Year FE
N
Ng

(1)
0.00179(0.00169)
Yes
Yes
Yes
Yes
Yes
No
No
956
478

(2)
0.00144(0.00165)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
956
478

1879-1882(3)
0.0116(0.0210)
Yes
Yes
Yes
Yes
Yes
No
No
956
478

(4)
0.0144(0.0205)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
956
478

N shows the sample size and N g shows the number of counties. The robust SEs incolumns (1) and (2), and the county-level cluster robust SEs are in parentheses incolumns (3) and (4). + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001. The sample size is diﬀerent from that in other analyses because I adjusted the border change
before 1884, and also excluded two outliers (∆Log Population in 1879–1880 was
0.65 and 0.7 while the 99th percentile is 0.1) possibly due to measurement error.

58

Table 6: Exclusion Restriction: Does the Path Increase Other Public Goods?

Railroad County Prop (-1892)
County FE and Year FE
Sum of Slope * Year FE
Log Flat Area * Year FE
Log Area * Year FE
Initial Population Density * Year FE
Coastal * Year FE(Main) Old Road * Year FE
First FN
Ng

Public Expenditure

Log Public Expenditure

OLS

IV

OLS

IV

(1)
-51740.7(123228.2)
Yes
Yes
Yes
Yes
Yes
Yes
Yes

(2)
80865.5(208140.6)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
14.05
504
36

(3)
-0.388(0.239)
Yes
Yes
Yes
Yes
Yes
Yes
Yes

(4)
-0.216(0.527)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
14.09
490
36

504
36

490
36

N shows the sample size and N g shows the number of prefectures. Prefecture-level cluster
robust SEs are in parentheses. + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001. First F
shows the Kleibergen-Paap statistics to test for the weak instrument. I use the data from1889 to 1902. I excluded Tokyo, Osaka, Kyoto, Kanagawa, and Hyogo as outliers, but thequalitative results do not change by excluding them. Public Expenditure is in Yen and themean of Public Expenditure of the sample in this analysis is 514, 013.

59

Table 7: Heterogeneous Impact Analysis on Steam Power of Non-Mining Industry
1888-1891
Railroad (-1892)
Railroad * Initial Population Density

(1)
23.41
(20.51)44.77∗(22.80)Railroad * Distance to Tokyo(2)
35.72+(21.63)36.48(22.21)
18.76(20.42)

Railroad * Distance to Tokyo or Osaka
County FE and Year FE
Sum of Slope * Year FE
Log Flat Area * Year FE
Log Area * Year FE
Distance to Coast * Year FE
Initial Population Density * Year FE(Main) Traditional Road * Year FE
Dist. to Tokyo * Year FE
Dist. to Tokyo or Osaka * Year FE
First F
N
Ng

Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
6.835
1948
487

Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
3.521
1948
487

1888-1902(3)
20.94(20.44)56.16∗(26.80)26.20(19.21)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
5.726
1948
487

(4)
83.39
(58.56)129.6(84.53)

Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
20.42
974
487

(5)
83.96
(62.46)148.6(97.20)-65.17(83.43)

Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
6.718
974
487

(6)
62.50
(65.61)
120.6(79.43)-16.35(58.71)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
7.124
974
487

N shows the sample size and N g shows the number of counties. The county-level cluster robust
SEs are in parentheses. + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001. First F shows the
Kleibergen-Paap statistics to test for the weak instrument. I used steam power in the non-mining
industry, because the mining industry occupies the largest share in horsepower, but its location isquite sensitive to the natural resource location, which is far from Tokyo or Osaka, so the coeﬃcientof the interaction term for distance will be misleading if we include this industry. Due to the low
F-value in the first stage, I estimated the same model with LIML as well, but the results do notchange qualitatively.

60

Table 8: The Eﬀect on Structural Change in 1920
Primary/Total (Worker)
Railroad Growth 1885-1892
Initial Agri. Worker / Population
Sum of Slope
Log Flat Area
Log Area
Distance to Coast
Initial Population Density
Traditional Road
Main Traditional Road
First FN

(1)
-0.0675+(0.0409)
Yes
Yes
Yes
Yes
Yes
No
No
No
62.57
400

(2)
-0.0632+(0.0369)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
62.26
400

(3)
-0.0640(0.0410)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
55.75
400

Industry/Total (Worker)(4)
0.0569+(0.0324)
Yes
Yes
Yes
Yes
Yes
No
No
No
62.57
400

(5)
0.0529+(0.0303)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
62.26
400

(6)
0.0544(0.0342)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
55.75
400

Robust SEs are in parentheses. + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001. N shows the
sample size. Initial Agri Worker/Population is calculated as agricultural workers divided by population in 1885. Agricultural workers’ data is obtained from Huken Tokeisho, and I used the earliest
available data from 1884 to 1886 for each. Since this information is missing for some prefectures,
87 counties were dropped from the main results. Note that the mining industry is accounted in theindustry sector.

61

Table 9: The Eﬀect on Urbanization in 1885–1903, 1920
∆ Log of Population Density
1885-1903
Railroad Growth 1885-1892
Railroad Growth 1885-1892 * Initial Pop. Density
Log Initial Population Density
Sum of Slope
Log Flat Area
Log Area
Distance to Coast(Main) Traditional Road
First FN

(1)
0.0860∗∗(0.0316)0.0531∗(0.0245)-0.0720∗∗∗(0.0117)
Yes
Yes
Yes
Yes
No
36.21
480

(2)
0.0788∗(0.0336)
0.0513∗(0.0239)-0.0734∗∗∗(0.0118)
Yes
Yes
Yes
Yes
Yes
33.93
480

1885-1920(3)
0.169∗(0.0772)
0.282∗∗∗(0.0653)-0.169∗∗∗(0.0304)
Yes
Yes
Yes
Yes
No
36.21
480

(4)
0.141+(0.0825)0.275∗∗∗(0.0630)-0.175∗∗∗(0.0310)
Yes
Yes
Yes
Yes
Yes
33.93
480

N shows the sample size. Robust SEs are in parentheses. + p < 0.1, * p < 0.05, ** p < 0.01,*** p < 0.001. First F shows the Kleibergen-Paap statistics to test for the weak instrument. The
dependent variable is Log of Population Density1903 − Log of Population Density1885 in columns (1)and (2) and Log of Population Density1920 − Log of Population Density1885 in columns (3) and (4).
I drop the counties whose log of population is more than 2.5 s.d. from the mean as outliers for thisanalysis. When I clustered the SEs at prefecture level, SEs for the interaction terms become .036,.034, .106, and .101.

62

A

Appendix

A.1

A Formal Model

A.1.1

Consumers

Consumers have the following utility function over two types of goods, x and y. x is the composite ofindustrial goods, and y represents agricultural goods. They have CES preference over a continuous varietyof x.

u = (1 − β) log y + β log(x + x̄)
]1/α
[∫ N
α
xi di
x=
0

The elasticity of substitution across varieties is given by
σ=

1
>1
1−α

As in the standard way, we can define p x as the aggregate price for aggregated goods x.
px =

[∫ N
0

]1/1−σ
p1−σ
di
i

.

The budget constraint is
p x x + py y = E
where E is the total expenditure and wage is the numeraire.
Given these, we obtain the following equation from the FOCs with respect to x and y,
1−βE
p x (x + x̄) + p x x = E =⇒ x = β − (1 − β) x̄
β
px

63

and the demand for each variety i is
[

E
xi =
β − (1 − β) x̄
px

](

pi
px

)−σ

Consumers can decide which sector to engage in by using one unit of time.
A.1.2

Production

The agricultural sector is competitive and has a production function, ALyθ , where A is its technology level, andI assume 0 < θ < 1. On the other hand, the industrial sector is characterized by monopolistic competition as
in Bustos (2011). Firms are ex-ante homogeneous and have to pay a fixed cost fe for entry. They draw theirproductivity ϕ from the Pareto distribution G(ϕ) = 1 − ϕ−k during entry. There are two types of technology
in this sector, low (old) and high (new). The total cost function of production is given by the following:()
q
TCl (q, γ) = f +ϕ)(q
TCh (q, γ) = f η +γϕwhere η > 1 and γ > 1. Also, they can export to the other symmetric region by paying f x with the iceberg
cost τ. Given the demand q(ω) = E x Pσ−1 p(ω)−σ , and pricing rules pdl = w/(ρϕ), we obtain the following
profit equation,
1
E x (p x ρ)σ−1 ϕσ−1 − fσ
1πdh (ϕ) = E x (p x ρ)σ−1 (ϕγ)σ−1 − f ησ1πlx (ϕ) = (1 + τ1−σ ) E x (p x ρ)σ−1 ϕσ−1 − ( f + f x )
σ
x
1−σ 1
πh (ϕ) = (1 + τ ) E x (p x ρ)σ−1 (ϕγ)σ−1 − ( f η + f x )σπdl (ϕ) =where π’s subscript l (h) denotes the case for choosing low (high) technology, and superscript d (x) denotes
the case for not exporting (exporting).

64

A.1.3Equilibrium

I can show the following by using the results of Bustos (2011).
1. (Sorting) Firms with ϕ < ϕ∗ will exit, firms with ϕ∗ < ϕ < ϕ x will neither export nor

Proposition 1.

adopt high technology, those with ϕ x < ϕ < ϕh will export and will not adopt high technology, andthose with ϕh < ϕ will export and adopt high technology.
2. (Technology adoption) ϕh is strictly decreasing in τ.
3. (Industrial price index) p x is strictly increasing in τ if σ is suﬃciently large.
Proof. (1) and (2) are exactly the same as Bustos (2011), because the endogenous variables E x and L x do
not aﬀect these results. Considering (3), since E x is endogenous, it diﬀers from the original result, whichsimply claims that p x is strictly increasing in τ. Accordingly, following the same step as Bustos (2011), we
obtain the following expression,p x ∝ ∆−1/k (Eβ − (1 − β) x̄p x ) 1−σ =⇒ p x (Eβ − (1 − β) x̄p x ) σ−1 ∝ ∆−1/k
1

1

(A.1)

where
∆=1+τ

−k

(

fx
f

) σ−1−kσ−1

σ−1−k+ (1 + τ1−σ ) σ−1 (γσ−1 − 1) σ−1 (η − 1) σ−1
k

k

(A.2)

We can see that the LHS is strictly increasing in p x as in partial equilibrium when σ is suﬃciently large,
and if so this p x is increasing in τ because τ only strictly increases the RHS through ∆, as shown in Bustos(2011).72 σ is the key variable to predict whether the larger total expenditure on industrial goods is associatedwith the lower price more (or less) than proportionally. If σ is high enough, the income eﬀect generated by
the drop in price of industrial goods will not dominate the partial equilibrium eﬀect. I assume this fact□

throughout the paper.73

The proposition claims railroad access will increase ϕh . Note that 1 − G(ϕh ) is the probability of technology adoption at the firm level, and the number of high technology firms is given by M(1 − G(ϕh )), which
will be the aggregated-level variable, as will be observed in the data.74 We can see the following result,
I assume that Eβ − (1 − β) x̄p x is positive to exclude corner solutions and confirm that the LHS is strictly increasing in p x .
This also matches with the pattern in the aggregated data.
74
The aggregated amount of new technology will also behave similarly.
72
73

65

Proposition 2.

1. M(1 − G(ϕh )) is strictly decreasing in τ when x̄ is suﬃciently large.

Proof. To see this, (1 − G(ϕh )) is strictly decreasing in τ because ϕh is strictly increasing in τ. Therefore,
it suﬃces to show that M is decreasing in τ to obtain the result. Note that M is Er̄x where r̄ is the averagerevenue. By taking the log of M,
∂E

∂r̄

x
∂ log M= ∂τ − ∂τ
∂τ
Ex
r̄
∂E x

∂r̄

Next, I will show that this is decreasing in x̄. First, − ∂ττ is constant about x̄. Therefore, we can focus on E∂τx .
If we take the derivative with respect to x̄,−(1−β)p x∂p x−(1 − β) ∂p x (1 − β)p x ∂E x −(1 − β) ∂p x (1 − β)p x∂ log E x ∂ E x
=
=
+
=
+
(β − 1) x̄
2
2∂ x̄∂τ
∂τ
Ex
∂τ
∂τ
Ex
∂τ
∂τ
Ex
Ex
(
)
∂p x 1∂p x 1=−(1 − β)(Eβ − (1 − β)p x x̄) − (1 − β)2 p x x̄ =(−1 + β)Eβ2∂τ E x∂τ E 2x
Eβ

Eβ
px
∂M
x
This is negative because β < 1 and ∂p∂τ > 0. Note that when x̄ = (1−β)p x − ϵ, ∂τ = −+ϵ(1−β) ∂p xϵ ∂rr̄∂τ − r2 ∂τ ,x
and when ϵ → 0, since p x ∂p∂τ is now constant about ϵ and the other terms go to zero, it goes to a negative

constant. Therefore, there must be a feasible x̄∗ ensuring a positive demand for industrial goods such thatwhen x̄∗ < x̄, then ∂M∂τ < 0.
□This result has an intuition; if there is no income eﬀect, M will decrease by railroad because the averagerevenue will increase. However, if there is an income eﬀect, it also increases labor in the industrial sector andconsequently increases the number of firms. Therefore, if this eﬀect is strong enough, M will also increase.
How does railroad access aﬀect structural change? I can show the following using Proposition 1.
Proposition 3.

1. Ly is strictly increasing in τ.

2. p x /py is strictly increasing in τ.
Proof. We only have to solve the price of agricultural goods and labor allocation in the two sectors. Agricultural wage is determined by the labor market condition in the agricultural sector. Like wage income, they
can get py θALyθ−1 per labor. The total profit in the agricultural sector is py ALyθ − py Ly θALyθ−1 . Therefore, byengaging in the agricultural sector, workers can get py ALyθ as their aggregated income. Note that by competitive labor market conditions, workers can obtain the same amount of income if they engage in the industrial
66

sector, so py ALyθ−1 must be one. Given this, Ly is pinned down by the following market clearing condition
for agricultural goods.
py ALyθ = (1 − β)L(1 + x̄p x )=⇒ Ly − (1 − β)L − (1 − β) x̄p x = 0
This is strictly increasing in Ly (p x is not a function of Ly , because E x = Eβ − p x (1 − β) x̄ and p x ), and strictly
decreasing in τ (by increasing p x ). Therefore, Ly is increasing in τ.
To see the second part, we can consider the following equation,
px 1 − β y=py
β x + x̄τ increases y through the increase in L x and decrease x through an increase in P x because x = E/P x − (1 − β) x̄
(E do not change by τ).
□

A.2

Data Description

A.2.1

Railroad-Related Data Sources

Nihon Kokuyu Tetsudoshi (The History of Japan National Railroad)

This is an oﬃcial record ofnational railroad services published in 1972 by the Japan National Railway. It covers all national railroad
lines constructed before 1945 and the private lines nationalized in 1906, and contains information such as
the opening year of each section. Therefore, I can identify the opening year of the sections in the shapefile
above using this source.75
Nihon Tetsudoshi (History of Japanese Railroad)

This is also a publication of Japanese railroad history

published by the Ministry of Railroads in 1921. This document covers the lines not included in the Nihon
Kokuyu Tetsudoshi and private lines that were not nationalized in 1906 or built after 1906 since this document
has less readability than the Nihon Kokuyu Tetsudoshi.

75

A very limited number of lines closed by 1950, so I used a straight line to locations that the line connected.

67

Nihon no Kaido Handbook (Handbook of Japanese Traditional Roads)

Though there is no oﬃcialrecords about traditional roads, this book collects local information and describes the routes of traditionalroads with the names of the towns that the traditional roads passed through. I made straight lines to connect
these towns to cities to represent the traditional road network. Straight lines will not be the exactly samewith traditional roads, but I do not think that this will change the county-level variation or results because
towns listed are fairly close to each other. The main roads, Gokaido, is also identified as five roads specified
by Tokugawa Shogunate.76
A.2.2

Factory-Related Sources

Noshomu Tokeihyo (Agriculture, Commerce and Manufacturing Statistics)

I digitize these oﬃcial statis-

tics for factory-related variables. The Ministry of Agriculture and Commerce published this data, which
covers the entire nation. This is the most comprehensive industrial statistics series covering Japan in the
1880s-1890s. It has a list of factories with addresses and the horsepower of each type of engine (water or
steam) and number of workers. Unfortunately, the names in some cases were not recorded properly (there
are some cases, such as ”silk factory”), so I construct county-level panel data for the total number and horsepower of engines, and total number of factories and workers for each type of power source. The document
also has product information, so I construct industry-level outcomes for further analysis. Only the publications in 1885, 1888, 1889, 1890, and 1891 have the list; for 1885, many prefectures could not report theirresults to the central government, so use only data for 1888–1891 for the analysis.
Kojo Tsuran (Factory Catalog)

This series is published after 1902 and has the same list of factories asabove. I use its publication in 1902 and 1919, which is after the factory list in Noshomu Tokeihyo became
unavailable.7778 The format is consistent with Noshomu Tokeihyo, which is enough to construct the same
variables related to steam engines at the industry and county levels.

76

They are Tokai-Do and Nakasen-Do from Tokyo to Kyoto, Oshu-Dochu from Tokyo to Shirakawa, Koshu-Kaido from Tokyo
to Shimosuwa and Nikko-Kaido from Tokyo to Nikko.
77
Again, I greatly appreciate Yutaka Arimoto, Kaori Ito, Asuka Imaizumi, Tetsuji Okazaki, Kentaro Nakajima, and Tomohiro
Machikita, who digitized these catalog with support from the JSPS KAKENHI Grant-in-Aid for Scientific Research (B) 21330064,
for providing me permission to use this data.
78
This data set covers factories with more than nine workers. Because Noshomu Tokeihyo covers all factories using water or steam
power, these series do not have consistent data. However, results do not change when excluding factories with less than 10 workers
from the Noshomu Tokeihyo sample because these factories are not the majority and negligible at aggregation.

68

Huken Tokeisho (Prefectural Statistics)

These series of prefecture-level statistics were published by pre-fecture governments. They contain factory list, as in the publications above, and sometimes have more precise information such as revenue or share capital. This is useful to check the positive relationship between
technology and productivity or firm size, which I presume throughout this paper. However, this publication
has many missing volumes in addition to inconsistent data formats across time and prefectures, especiallyafter the central government repealed its publication order. Prefecture governments were free to publish atwill thereafter. Thus, I do not use this factory data for the main results, and keep it only to examine therelationship between steam power and labor productivity or firm size at the factory level. Again, it was
impossible to match factory names to construct panel data.
A.2.3

Population-Related Data Sources

Nihon Teikoku Minseki Koguchi Hyo (Table of Registered Population) This is county-level population
statistics published almost annually from 1879 to 1898 and between 1903 and 1908 under a diﬀerent title(Nihon Teikoku Seitai Jinko Tokei).79 I use a digitized version for 1879, 1880, 1882 and 1885 from theHistorical Regional Statistical Data,80 and newly digitized the data in the other years, 1888-1891 and 1903.
Population Census

In 1920, the government conducted its first census, which I digitized to obtain popula-tion data at the county level.81 Additionally, this document reports the number of workers by sector, so that
we can construct the share of workers in each sector in 1920.
Huken Tokeisho (Prefectural Statistics)

I use this series again to obtain the number of agricultural work-ers around 1885. Because some prefecture did not publish in this year, I used 1884 or 1886 if available.
Combined with the number of population in Nihon Teikoku Minseki Koguchi Hyo, I calculated the share of
population working in the agricultural sector around 1885 to use it as control for regression analysis to see
the impact on industrialization.
79
There were two types of registered population, Honseki and Genju, which requires an explanation of Japanese registry system toillustrate the diﬀerence. Suppose you are born in a family county X; your name will be registered in your house account (Koseki) in
county X. Honseki counts the population based on this information. Now, if you move from county X to county Y, you must submit
a document to the county oﬃces, which will then account for this movement to calculate the total population currently living in eachcounty, which is the Genju population. For this reason, I choose Genju as the main variable, though for 1879, 1880, and 1882, there
are no available data for Genju and I therefore use Honseki in these years only for the falsification test without connecting them with
Genju data after 1885.
80
http://giswin.geo.tsukuba.ac.jp/teacher/murayama/datalist_e.htm
81
I regard this census population variable equivalent to the Genju population variable, since both attempt to measure the residential
population.

69

A.3

Excluded Counties

Some counties are excluded from all the analysis in this paper for various reasons. I have listed such counties
in Table A.1. After excluding these, 487 counties remain in the main data set.

A.4

Descriptive Statistics of Factory-Level Data in 1902

Table A.2 shows the descriptive statistics of factory-level data in 1902 with industry-level breakdowns. Outof 3361 factories, 2097 adopted steam power. This adoption rate is higher for the mining industry, and lower
for the domestic industry. The average steam power and number of workers at the steam-powered factory
level varies. The mining industry factories are larger than the textile and domestic industries.

A.5

Regression Analysis on the First Stage

I confirmed the relationship in Figure 16 by a regression analysis, using the following specification:
Railroad Accessit =Distance from the Pathi ∗ Year FE + Local Geographical Controlsi ∗ Year FE(A.3)(+Local Other Controlsi ∗ Year FE) + Year FE + County FE + eit
where Railroad Accessit is a binary variable, which takes one if county i has railroad access in year t, Distancefrom the Pathi is the distance between the cost-minimizing path and county i, Local Geographical Controlsi
are the time-invariant controls at county i, such as the sum of slopes, log of area, log of flat area, and distancefrom the coast. Table A.3 shows the results. Column (1) is the basic specification and column (2) has the
additional non-pure geographical controls (interacted with Year FE), Local other Controlsi , which include
the population density in 188582 and the existence of traditional roads. The result shows that the instruments
are working as expected such that the Distance from the Pathi has significantly negative coeﬃcients. The
magnitude of coeﬃcients show that until 1898 there is monotonic growth of railroad access in counties close
to the path, and in 1902 and 1903, the prioritized counties continued to have advantage in railroad accessthrough a backlash that had already started.

82
1885 is not the year before construction started so there may be concern about bad control, but most construction started after
1885, and the main results do not change by including this variable.

70

A.6

Other Factory-related Outcomes and Prefecture-level Control and Clustering

Table A.5 shows the results for other factory-related variables. See the main text for the interpretation. Table A.6 shows the result with prefecture-level control and clustering corresponding to Table A.5. The point
estimates do not change dramatically from Table A.5, and the eﬀect on steam power and the share of people working in steam-powered factories are still significant. The eﬀect on population became insignificant,suggesting high correlation in the error term of population within prefectures. However, when I analyze
the heterogeneous impact analysis using the same specification as in Table 9 with prefecture-level control
and clustering, I find a significant heterogeneous eﬀect on the population growth between 1885 and 1920, although I do not discuss it in this paper for brevity. Overall, I think that prefecture-level control and clustering
do not aﬀect the implications of the main results.

A.7

Additional Specification for Cost Function

In the main result, I simply assume the following cost function when passing mesh i
Costi = Ave.Slopei(A.4), but for robustness check I tried additional specifications, which are
Costi = 1 + Ave.Slopei

(A.5)

and
Costi = Ave.Slopei ∗ 1[Ave.Slopei > 25 per mill]

.

(A.6)

Equation (A.5) is very similar to a functional form considered in Faber (2014). By adding a constant term,
not only the slope, but also the moving cost in a totally flat area is now considered, and consequently, theslope is considered less important than in the main specification.
Equation (A.6) uses an indicator function to drop the slope information below 25 per mill. The motivation is that the Railroad Ministry set 25 per mill as the maximum slope for mountain areas (Nihon Kokuyu
Tetsudo, 1997), mainly by the operative reason given the capacity of their locomotives. By having specifications, the cost-minimizing path could avoid steeper areas more than the main specification.

71

Newly calculated paths and paths using the main specification are depicted in Figure A3. They arequite similar, but there is one line where new specifications consistently diverge from the main specification.
In this area, new cost-minimizing paths are along the coastline, which was not chosen as the main line inreality. This is because when they constructed the mainline to the north, there was already a line from Tokyo
to Takasaki, which originally was constructed from Tokyo to the West. This route was abolished because theroute to the West from Takasaki was too steep as explained in the original text, but they extended the linefrom Takasaki to reach the northern area, which is why the line in inland areas, and not the coastline, waschosen.
The result using these alternative cost functions is in Table A.7. Overall, the results in Panels B andC with alternative IVs show that the eﬀect on steam power and engines, and the results do not change
qualitatively. I also see the robustness of Table 8 and Table 9 by using these alternative cost specifications,and I find similar results, although I omit the results for brevity.
The eﬀect on steam power, which is the main outcome of this paper, is lower in Panels B and C than in
Panel A. To determine the reason, I compare industry-specific eﬀects in Table A.7. The results show that thediﬀerence in the eﬀect on steam power between the main result and the result with alternative IVs arises fromthe eﬀect on the mining industry. The diﬀerence in the eﬀect on total steam power is roughly 70–80, and thediﬀerence in the eﬀect on the mining industry’s steam power is 60–70. This is because the location of themining industry is sensitive to the location of natural resources and subtle diﬀerences in the specification for
IV.

A.8

Omitting Subsample

For further robustness checks, I drop the subsample, which may bias my main results. I compare the result
in column (1) of Table 4 with the results obtained by dropping four diﬀerent regions, Shikoku island, thewestern part of Honshu island, Gifu prefecture, and the counties that had already gained railroad access in
1885. The reasons for this selection are the following. First, Shikoku island does not have any destinations as
in Figure 15. I dropped the western part of Honshu island, because the original statement by Nihon Tetsudo
did not include Shimonoseki as its destination, and only noted Kyoto as a destination. As I explained in
the main text, Shimonoseki was a quite natural selection as a final destination since it is the gateway to
Kyushu island, but the inclusion may cause some bias in the main results. I excluded all the prefectureson the possible routes from Kyoto to Shimonoseki. Gifu prefecture is by a concern of a consistent unit of72

measurement throughout my sample period, because in this prefecture there were many changes in counties
at the village level, thereby rendering it diﬃcult to construct a truly consistent unit of measurement. Finally,for further robustness checks, I excluded counties that had already gained railroad access in 1888 or counties
that had no steam power in 1888. Table A.10 shows the results. Each column shows the result omitting the
subsamples, but the results are fairly robust. Only the result without the western part of Honshu island shows
diﬀerent estimates, but this may be because the construction of the line to Shimonoseki was not finished by1892. Also, we observe a significant eﬀect in column (6), which implies that the eﬀect in the main result ispartly coming from the adoption in counties without any steam power in 1888.

A.9

Long-term Eﬀects

As shown in Figure 1, after the 1900s, a less prioritized county far from the cost-minimizing path started to
get railroad access, and by 1920, the advantage of prioritized county had almost disappeared. Now, I analyze
the following reduced form model to see the time series pattern between distance to the path and outcome;

Outcomeit =Distance from the Pathi ∗ Year FE + Local Geographical Controlsi ∗ Year FE+ Year FE + County FE + uit

,

(A.7)

where Outcomeit is the log of population and steam power for 1902; and steam, electricity, or gas power for
1920 because by 1920 electricity and gas became popular as advanced technologies. The coeﬃcients forDistance from the Pathit ∗ Year FE are of main interest; if there is no persistent eﬀect, then the coeﬃcientsof railroad access and outcomes will show a similar pattern. However, if there is persistence, then even after
the coeﬃcients of railroad access become zero, the coeﬃcients for outcomes will not become zero or evendiverge if the positive feedback eﬀect is strong enough.
To show the whole pattern, I rely on a graphical presentation. Figure A4 shows the results, and eachpanel shows the coeﬃcients of Distance from the Pathi ∗ Year FE for each outcome. First, we can see thatcatching up in railroad access started around 1898, when the coeﬃcient started to go back to zero. In linewith this railroad eﬀect, until 1902, less prioritized counties had lower horsepower and population as we saw
in the previous regression results.
However, they fail to catch up in engine power as shown in the graph. Moreover, the gap was widened
even though less prioritized counties did catch up in railroad access by 1920. We can prove this relationship73

using data from the years 1888 and 1919 or 1920 by regression analysis in Table A.21. We cannot identify theexact mechanism and it is beyond the scope of this paper. However, this result has an implication for policy
makers; the decision of where to build railroads first may create a huge gap in the long term. Redding et al.
(2010) and Bleakley and Lin (2012) also found that the initial advantage in transportation makes long-term
term through sunk cost or path dependence. For population, early adopters still have an advantage although
its standard error has increased and is significant. This could be because of the higher variance in population
growth, which we often observe under the process of urbanization.

74

Figure A1: Aggregated Data on Foreign Trade
County Specific Export Value

Export/GDP.04 .06 .08 .1 .12 .14

Million JPY

Openness
300
200
100
0

1880

1890
year

1895

1900

Total
1880

1885

1890
year

1895

to N. America + Europe

1900

to N. Amer. + Euro. + HK

Industry Specific Export Value (1)
250
200
150
100
50
0

Industry Specific Export Value (2)250Million JPY

Million JPY

1885

1880

1885

1890
year

Industry

1895

200
150
100
50
0

1900

1880
TextileMining

1885

1890
year

1895

Agriculture

Source: Umemura et al. (1965). The unit for values is million Japanese Yen.

75

1900
Machinery

0

.2

.4

.6

.8

Figure A2: Relative Price Data at Industry Group Level

1884

1890

1900
year

Textile Price
Coal Price

Metal Price
Machinery Price

Source: Umemura et al. (1965). I obtained these figures by dividing price index of each industry group by price index of agricultural goods.

76

Figure A3: IV with other Specifications

77

1886
1887
1888
1889
1890
1891
1892
1893
1894
1895
1896
1897
1898
1899
1900
1901
1902
1903
1904
1905
1906
1907
1908
1909
1910
1911
1912
1913
1914
1915
1916
1917
1918
1919
1920

-.15

-.1

-.05

0

.05

-.4

-.2

0

Railroad Access

Log Population

78
1886
1887
1888
1889
1890
1891
1892
1893
1894
1895
1896
1897
1898
1899
1900
1901
1902
1903
1904
1905
1906
1907
1908
1909
1910
1911
1912
1913
1914
1915
1916
1917
1918
1919
1920

-4000-3000-2000-1000 0-.6

Figure A4: Reduced Form Eﬀects in the Long Term
Horsepower of Steam, Elec., or Gas

Table A.1: List of Excluded Counties
ReasonDomestic Colonies

Counties (By the name in 1885)
All counties in Hokkaido and Okinawa

Isolated Islands

All counties in Tsushima-island, Nagasaki,
and Oki-island in Shimane
All wards in Tokyo city, Toshima andHigashi-Tama counties in Tokyo, Nakakambara county and Nigata ward in Nigata, Kiku
county in Fukuoka, and Akamagaseki county
in Yamaguchi
All wards in Osaka city in Osaka, Kobe ward
in Hyogo, and Yokohama ward in Kanagawa

Destinations

Foreign Ports and Outliers

79

Table A.2: Descriptive Statistics: Factory-Level Data in 1902

Miningmean
sdsum
Texitlemean
sdsum
Brewing and Printing
mean
sd
sum
Other
mean
sd
sum
Total
mean
sd
sum

Factories

Steam-Powered Factories– Horsepower– Workers

153.7254902.4477325
111

165.46278.8256
18035.14

234.4414
736.0175
26023

2085

.6239808.4845011
1301

17.50445
79.73636
20882.81

96.80092
213.9992
12593899.3636364.483493836

18.22857
29.10082
638

100.1389
145.3673
3605

686.5991254.4904333
411

60.88207
314.2329
24657.24

63.72506
124.1667
26191

3023

.614952.4866872
1859

36.86176
183.13664213.19

97.77138
263.2247
181757The mining industry includes the mining, metal, and explosion materials (such as oil production) industries. The textile industry includes the cotton-spinning, weaving, and yarnmaking industries.

80Table A.3: IV and Railroad Expansion
Distance from Path * 1889
Distance from Path * 1890
Distance from Path * 1891
Distance from Path * 1892
Distance from Path * 1893
Distance from Path * 1894
Distance from Path * 1895
Distance from Path * 1896
Distance from Path * 1897
Distance from Path * 1898
Distance from Path * 1899
Distance from Path * 1900
Distance from Path * 1901
Distance from Path * 1902
Distance from Path * 1903
County FE and Year FE
Sum of Slope * Year FE
Log Flat Area * Year FE
Log Area * Year FE
Distance to Coast * Year FE
Initial Population Density * Year FE(Main) Traditional Road * Year FE
N
Ng

(1)
0.0223(0.0192)-0.116∗∗∗(0.0335)-0.203∗∗∗(0.0392)-0.308∗∗∗(0.0477)-0.312∗∗∗(0.0478)-0.301∗∗∗(0.0476)-0.351∗∗∗(0.0500)-0.363∗∗∗(0.0504)-0.336∗∗∗(0.0505)-0.353∗∗∗(0.0539)-0.282∗∗∗(0.0609)-0.252∗∗∗(0.0596)-0.257∗∗∗(0.0601)-0.229∗∗∗(0.0608)-0.175∗∗(0.0614)
Yes
Yes
Yes
Yes
Yes
No
No
7792
487

(2)
0.0163(0.0193)-0.0865∗∗(0.0290)-0.179∗∗∗(0.0366)-0.288∗∗∗(0.0467)-0.293∗∗∗(0.0469)-0.281∗∗∗(0.0471)-0.336∗∗∗(0.0503)-0.349∗∗∗(0.0512)-0.324∗∗∗(0.0515)-0.352∗∗∗(0.0541)-0.287∗∗∗(0.0600)-0.261∗∗∗(0.0594)-0.262∗∗∗(0.0599)-0.233∗∗∗(0.0597)-0.175∗∗(0.0602)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
7792
487

N represents the sample size and N g shows the number ofcounties. The county-level cluster robust SEs are in parentheses. + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001. I use
data from 1888 to 1903. Distance from Pathi is the Euclidean
distance (100 km) from the path to county i.

81

Table A.4: Result without IV: Eﬀect of Railroads, 1888–1902
DID

Railroad (-1892)
County FE and Year FE
Sum of Slope * Year FE
Log Flat Area * Year FE
Log Area * Year FE
Distance to Coast * Year FE
Initial Population Density * Year FE(Main) Traditional Road * Year FE
N
Ng

Steam Power [HP]

Steam Engines

(1)
48.63
(52.42)
Yes
Yes
Yes
Yes
Yes
No
No
974
487

(3)
1.942
(1.748)
Yes
Yes
Yes
Yes
Yes
No
No
974
487

(2)
41.30
(57.28)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
974
487

(4)
1.274
(1.812)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
974
487

N represents the sample size and N g shows the number of counties. The
county-level cluster robust SEs are in parentheses. + p < 0.1, * p < 0.05, **p < 0.01, *** p < 0.001. I use data in 1888 and 1902.

82

Table A.5: Other Outcomes: Eﬀect of Railroads, 1888–1902
DID-IV
(1)
(2)
(3)
(4)
Panel A: Number of Steam-Powered Factories
Railroad (-1892)
4.557∗
2.589
5.150∗
3.328(2.220)(2.134)(2.335)(2.216)
Panel B: Number of Water-or-Steam-Powered Factories
Railroad (-1892)-0.428-3.249-0.0876-2.728(4.830)(5.113)(4.841)(5.091)Panel C: Workers in Steam-Powered Factory / Population
Railroad (-1892)
0.00767∗∗ 0.00616∗ 0.00826∗∗ 0.00678∗(0.00284) (0.00270) (0.00297) (0.00281)
Panel D: Workers in Factory / Population
Railroad (-1892)
0.002140.000623
0.00301
0.00162(0.00397) (0.00424) (0.00401) (0.00428)
Panel E: Log Population
Railroad (-1892)
0.0748+0.06270.03830.0213(0.0385)(0.0402)(0.0381)(0.0397)
County FE and Year FE
Yes
Yes
Yes
Yes
Sum of Slope * Year FE
Yes
Yes
Yes
Yes
Log Flat Area * Year FE
Yes
Yes
Yes
Yes
Log Area * Year FE
Yes
Yes
Yes
Yes
Distance to Coast * Year FE
Yes
Yes
Yes
Yes
Initial Population Density * Year FE
No
Yes
No
Yes
(Main) Traditional Road * Year FE
No
Yes
No
Yes
Dist. to Destinations * Year FE
No
No
Yes
Yes
First F
41.52
38.15
40.91
36.48
N
956
956
956
956
Ng
487
487
487
487
N shows the sample size and N g shows the number of counties. The county-levelcluster robust SEs are in parentheses. + p < 0.1, * p < 0.05, ** p < 0.01, *** p <0.001. First F shows the Kleibergen-Paap statistics to test for the weak instrument. I
use data in 1888 and 1902.

83

Table A.6: Robustness Check with Prefecture-level Control and Clustering: Eﬀect of Railroads, 1888–1902
DID-IV
Panel A: Steam Power [HP]
Railroad (-1892)

(1)

(2)

(3)

(4)

288.1∗(145.2)295.2+(154.8)253.8∗(107.8)255.3∗(110.8)Panel B: Number of Steam Engines
Railroad (-1892)

20.86∗
20.24∗
19.06∗
18.16∗(9.557)(9.725)(8.582)(8.408)Panel C: Number of Steam-Powered Factories
Railroad (-1892)
3.946
1.873
4.759
2.777
(2.676)(2.550)(3.058)(2.763)
Panel D: Number of Water-or-Steam-Powered Factories
Railroad (-1892)-0.957-3.736-0.443-3.183(7.900)(7.765)(7.957)(7.791)Panel E: Workers in Steam-Powered Factory / Population
Railroad (-1892)
0.00706∗∗ 0.00527∗ 0.00790∗∗ 0.00618∗∗(0.00257) (0.00240) (0.00263) (0.00233)
Panel F: Workers in Factory / Population
Railroad (-1892)
0.001750.0001300.002730.00120(0.00502) (0.00553) (0.00506) (0.00554)
Panel G: Log Population
Railroad (-1892)
0.0712
0.05310.03730.0152(0.0742)(0.0736)(0.0750)(0.0750)
County FE and Year FE
Yes
Yes
Yes
Yes
Sum of Slope * Year FE
Yes
Yes
Yes
Yes
Log Flat Area * Year FE
Yes
Yes
Yes
Yes
Log Area * Year FE
Yes
Yes
Yes
Yes
Distance to Coast * Year FE
Yes
Yes
Yes
Yes
Initial Population Density * Year FE
No
Yes
No
Yes
(Main) Traditional Road * Year FE
No
Yes
No
Yes
Dist. to Destinations
No
No
Yes
Yes
Prefecture Geography * Year FE
Yes
Yes
Yes
Yes
First F
12.11
12.19
12.98
12.70
N
956
956
956
956
Ng
487
487
487
487
Prefecture-level cluster robust SEs are in parentheses. + p < 0.1, * p < 0.05, **p < 0.01, *** p < 0.001. First F shows the Kleibergen-Paap statistics to test for the
weak instrument. I use data in 1888 and 1902. Prefecture Geography includes theprefecture-level average slope and share of the flat area.

84

Table A.7: Robustness Checks on the Main Results during 1888–1902: IV with Alternative Cost Function
Steam Power [HP](1)

Steam Engines(2)299.0∗∗(102.0)21.16∗∗∗(6.015)228.0∗∗(79.90)15.15∗∗∗(4.600)219.3∗∗(74.80)
Yes
Yes
Yes
Yes
Yes
41.52
56.48
50.29
974
487

10.87∗∗(3.856)
Yes
Yes
Yes
Yes
Yes
41.52
56.48
50.29
974
487

Panel A: Main Specification
Railroad (-1892)
Panel B: Adding Constant Term
Railroad (-1892)
Panel C: Ignoring < 25 per millRailroad (-1892)
County FE and Year FE
Sum of Slope * Year FE
Log Flat Area * Year FE
Log Area * Year FE
Distance to Coast * Year FE
First F for Panel A
First F for Panel B
First F for Panel CNNg

N represents the sample size and N g shows the number of counties. The county-level cluster robust SEs are in parentheses. +p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001. First F shows
the Kleibergen-Paap statistics to test for the weak instrument. I use
data in 1888 and 1902. Panel A uses the main IV specification inequation (A.4), which are just replications of the results. Panels B
and C use an alternative specification for IV, equations (A.5) and(A.6).

85

Table A.8: Robustness Checks on Industry-specific Eﬀects during 1888–1902: IV with Alternative Cost Function
DID-IV
Steam Power [HP]
Total
(1)

Domestic
(2)

Mine
(3)

Textile(4)299.0∗∗(102.0)Panel B: Adding Constant Term
Railroad (-1892)
228.0∗∗(79.90)Panel C: Ignoring < 25 per millRailroad (-1892)
219.3∗∗(74.80)5.024+(2.606)184.9∗(80.73)34.15(35.29)4.368+(2.247)124.3∗(58.99)41.84(31.82)

4.146∗(2.081)115.6∗(55.50)50.09+(26.37)

County FE and Year FE
Sum of Slope * Year FE
Log Flat Area * Year FE
Log Area * Year FE
Distance to Coast * Year FE
First F for Panel A
First F for Panel B
First F for Panel C
N
Ng

Yes
Yes
Yes
Yes
Yes
41.52
50.29
56.48
974
487

Yes
Yes
Yes
Yes
Yes
41.52
50.29
56.48
974
487

Yes
Yes
Yes
Yes
Yes
41.52
50.29
56.48
974
487

Panel A: Main Specification
Railroad (-1892)

Yes
Yes
Yes
Yes
Yes
41.52
50.29
56.48
974
487

N shows the sample size and N g shows the number of counties. The
county-level cluster robust SEs are in parentheses. + p < 0.1, * p <0.05, ** p < 0.01, *** p < 0.001. First F shows the KleibergenPaap statistics to test for the weak instrument. I use data in 1888 and
1902. Panel A uses the main IV specification in equation (A.4), which
are just replications of the results. Panels B and C use an alternative
specification for IV, equations (A.5) and (A.6).

86

Table A.9: Robustness Checks on the Main Results during 1888–1902: Main Result without Neighbor Counties of Destinations
DID-IV

Excluded Neighbors of Destinations
Railroad (-1892)
County FE and Year FE
Sum of Slope * Year FE
Log Flat Area * Year FE
Log Area * Year FE
Distance to Coast * Year FE
Initial Population Density * Year FE(Main) Traditional Road * Year FE
Distance to Destinations * Year FE
First FN
Ng

Steam Power [HP]

Steam Engines

15 km
(1)
258.8∗∗(90.39)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
36.09
964
482

15 km
(3)
17.99∗∗(5.777)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
36.09
964
482

30 km
(2)
226.0∗∗(84.56)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
35.98
934
467

30 km
(4)
16.98∗∗(5.704)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
35.98
934
467

N shows the sample size and N g shows the number of counties. The countylevel cluster robust SEs are in parentheses. + p < 0.1, * p < 0.05, ** p <0.01, *** p < 0.001. First F shows the Kleibergen-Paap statistics to test
for the weak instrument. I use data in 1888 and 1902. In column (1) and(3), I excluded the counties within 15 km from the destination counties. In
column (2) and (4), I excluded the counties within 30 km from the destination
counties.

87

Table A.10: Omitting Subsample
DID-IV
Steam Power [HP]

Railroad (-1892)
County FE and Year FE
Sum of Slope * Year FE
Log Flat Area * Year FE
Log Area * Year FE
Distance to Coast * Year FE
Initial Population Density * Year FE(Main) Traditional Road * Year FE
First FNNg

No Omit(1)
304.3∗∗(110.6)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
37.65
974
487

Shikoku(2)
300.7∗∗(115.1)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
33.98
906
453

West
(3)
436.8∗(170.9)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
27.87
770
385

Gifu
(4)
306.0∗∗(109.4)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
38.31
948
474

Rail in 1888(5)
267.5∗∗(99.14)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
42.26
856
428

Steam in 1888(6)
168.6+(98.38)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
30.31
786
393

N shows the sample size and N g shows the number of counties. The county-level cluster robust SEs are inparentheses. + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001. First F shows the Kleibergen-Paap statistics
to test for the weak instrument. I use data in 1888 and 1902. Column (2), (3) and (4) omit Shikoku island,
prefectures between Kyoto and Shimonoseki, and Gifu prefecture respectively. Column (5) omits the counties
that had railroad access in 1888, and column (6) omits the counties that had steam power in 1888.

88

Table A.11: Displacement Eﬀect? Analysis by Diﬀerent Treatment Variables
Steam Power [HP]
Railroad (-1892)(1)
299.0∗∗(102.0)Railroad (-1892) within 50 km(2)
275.3∗∗(96.73)Neighbor’s Railroad within 50 km
County FE and Year FE
Sum of Slope * Year FE
Log Flat Area * Year FE
Log Area * Year FE
Distance to Coast * Year FE
N
Ng
First F

Yes
Yes
Yes
Yes
Yes
974
487
41.52

(3)

Yes
Yes
Yes
Yes
Yes
974
487
37.44

57.10
(198.6)
Yes
Yes
Yes
Yes
Yes
974
487
12.21

Steam Engines(4)
21.16∗∗∗(6.015)

Yes
Yes
Yes
Yes
Yes
974
487
41.52

(5)

(6)

19.48∗∗(6.016)

Yes
Yes
Yes
Yes
Yes
974
487
37.44

-6.043(5.840)
Yes
Yes
Yes
Yes
Yes
974
487
12.21

N shows the sample size and N g shows the number of counties. The county-level cluster robust
SEs are in parentheses. + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001. I use data in 1892
and 1902. First F shows the Kleibergen-Paap statistics to test for the weak instrument. Railroad(-1892) within 50 kmit takes one if Railroad (-1892)it takes one or Railroad (-1892) jt of any ofneighbor counties within 50 km takes one. This is instrumented by Distance to the Pathi ∗ Year
FEt as in the main specification. Out of counties which switch on Railroad (-1892) within 50
kmit between 1888 and 1902, 29 per cent counties switch on due to their neighbor’s access.
Neighbor’s Railroad within 50 kmit takes one if Railroad (-1892)it takes zero but Railroad (1892) within 50 kmit takes one. This is instrumented by Adjacent to the Pathi ∗ Year FEt where
Adjacent to the Pathi takes one if Distance to the Pathi takes from 20 km to 50 km.

89

Table A.12: Displacement Eﬀect? Analysis by using Shikoku Island
Compared with counties # km from the Path:

Shikoku Island * 1902
County FE and Year FE
Sum of Slope * Year FE
Log Flat Area * Year FE
Log Area * Year FE
Distance to Coast * Year FE
N
Ng

20 km - 50 km> 50 km

Power

EnginesPower

Engines(1)-28.97(50.53)
Yes
Yes
Yes
Yes
Yes
274
137

(2)
-1.587(1.076)
Yes
Yes
Yes
Yes
Yes
274
137

(3)
45.88
(31.28)
Yes
Yes
Yes
Yes
Yes
242
121

(4)
-0.817(1.001)
Yes
Yes
Yes
Yes
Yes
242
121

N shows the sample size and N g shows the number of counties. The county-levelcluster robust SEs are in parentheses. + p < 0.1, * p < 0.05, ** p < 0.01, ***p < 0.001. I use data in 1892 and 1902. First F shows the Kleibergen-Paap statistics
to test for the weak instrument. In column (1) and (2), I compared counties in Shikoku
island with counties in other islands 20 km - 50 km far from the path. In column (3)and (4), I compared counties in Shikoku island with counties in other islands morethan 50 km far from the path.

90

Table A.13: Short-Term Eﬀects of Railroads, 1888–1891
DID
Panel A: Steam Power [HP]
Railroad (-1892)
Panel B: Log Population
Railroad (-1892)
County FE and Year FE
Sum of Slope * Year FE
Log Flat Area * Year FE
Log Area * Year FE
Distance to Coast * Year FE
Initial Population Density * Year FE(Main) Traditional Road * Year FE
Dist. to DestinationsFirst FN
Ng

DID-IV

(1)

(2)

(3)

(4)

(5)

8.062
(6.809)63.07∗(26.40)55.55+(29.76)53.86∗(23.66)43.18+(25.80)0.00458+ 0.0112(0.00235) (0.0118)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
No
No
No
No
13.34
1948
1948
487
487

0.00646(0.0129)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
12.69
1948
487

0.00467(0.0116)
Yes
Yes
Yes
Yes
Yes
No
No
Yes
13.84
1948
487

-0.00109(0.0126)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
12.69
1948
487

N shows the sample size and N g shows the number of counties. The county-level cluster
robust SEs are in parentheses. + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001. First F
shows the Kleibergen-Paap statistics to test for the weak instrument. I use data in 1888, 1889,
1890, and 1891. Because of the low F-values in the first stage, I estimated the same modelwith LIML or used the liner trend to be interacted with the distance to the path as an alternative
instrument to increase the F-values as well, but the results do not change qualitatively.

91

Table A.14: Heterogeneous Eﬀect of Railroads on Log of Population, 1888–1891
Log Population
DID
Railroad Access
Railroad * Initial Log Pop Density
County FE and Year FE
Sum of Slope * Year FE
Log Flat Area * Year FE
Log Area * Year FE
Distance to Coast * Year FE
Initial Population Density * Year FE(Main) Traditional Road * Year FE
Dist. to DestinationsFirst FN
Ng

(1)
0.00588(0.0115)-0.000447(0.0138)
Yes
Yes
Yes
Yes
Yes
Yes
No
No
6.022
1948
487

DID-IV(2)
0.00181(0.0131)-0.000947(0.0139)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
7.525
1948
487

(3)
0.000848(0.0113)0.00361(0.0137)
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
7.707
1948
487

(4)
-0.00400(0.0129)0.00282(0.0139)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
7.423
1948
487

N shows the sample size and N g shows the number of counties. The county-levelcluster robust SEs are in parentheses. + p < 0.1, * p < 0.05, ** p < 0.01, *** p <0.001. First F shows the Kleibergen-Paap statistics to test for the weak instrument. I
use data in 1888, 1889, 1890, and 1891. Because of the low F-value in the first stage,
I estimated the same model with LIML or used the liner trend to be interacted withthe distance to the path as an alternative instrument to increase the F-values as well,
but the results do not change qualitatively.

92

Table A.15: Coal Price Channel?
Steam Power (Non-Mining) [HP]1888-1891
1888-1902
Railroad (-1892)
Railroad * Distance to Coal(1)
47.97+(24.93)-2.623(14.55)

Railroad * Initial Population Density
County FE and Year FE
Sum of Slope * Year FE
Log Flat Area * Year FE
Log Area * Year FE
Distance to Coast * Year FE
Initial Population Density * Year FE(Main) Traditional Road * Year FE
Dist. to coal * Year FE
First F
N
Ng

Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
5.165
1948
487

(2)
27.76
(27.23)15.38(24.15)43.55(35.93)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
3.130
1948
487

(3)
92.83
(59.05)33.61(38.89)

Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
17.90
974
487

(4)
70.14
(60.07)116.9(90.06)213.5(165.6)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
5.276
974
487

N shows the sample size and N g shows the number of counties. The countylevel cluster robust SEs are in parentheses. + p < 0.1, * p < 0.05, **p < 0.01, *** p < 0.001. First F shows the Kleibergen-Paap statistics to test
for the weak instrument. The dependent variable is steam power, excluding
the mining industry.

93

Table A.16: Information Channel? The Eﬀect of Railroad on Expositions
N of Expo

Total Days of Expo

OLS

IV

OLS

IV

(1)
2.671
(5.483)(2)
6.261(11.97)(3)
57.80(51.74)(4)
69.46(89.99)County FE and Year FE

Yes

Yes

Yes

Yes

Sum of Slope * Year FE

Yes

Yes

Yes

Yes

Log Flat Area * Year FE

Yes

Yes

Yes

Yes

Log Area * Year FE

Yes

Yes

Yes

Yes

Initial Population Density * Year FE

Yes

Yes

Yes

Yes

Coastal * Year FE

Yes

Yes

Yes

Yes

(Main) Old Road * Year FE
First F
N
Ng

Yes

Yes
17.77
504
36

Yes

Yes
17.77
504
36

Railroad County Prop (-1892)

504
36

504
36

N shows the sample size and N g shows the number of prefectures. Prefecturelevel cluster robust SEs are in parentheses. + p < 0.1, * p < 0.05, ** p < 0.01,*** p < 0.001. First F shows the Kleibergen-Paap statistics to test for the
weak instrument. I use the data from 1889 to 1902. I excluded Tokyo, Osaka,
Kyoto, Kanagawa, and Hyogo as outliers, but the qualitative results do notchange by excluding them. N of Expos is the number of expositions and Total
Days of Expos is the total days the expositions were open.

94

Table A.17: Industry-specific Eﬀects: The Eﬀect of Railroads on Steam Power, 1888–1902

Railroad (-1892)(Normalized coeﬃcient)County FE and Year FE
Sum of Slope * Year FE
Log Flat Area * Year FE
Log Area * Year FE
Distance to Coast * Year FE(Main) Traditional Road * Year FE
Initial Population Density * Year FE
First FN
Ng

Domestic(1)
5.148+(2.822)0.771+
Yes
Yes
Yes
Yes
Yes
Yes
Yes
38.15
974
487

Mining(2)215.9∗(91.59)1.011∗
Yes
Yes
Yes
Yes
Yes
Yes
Yes
38.15
974
487

Textile(3)
17.20(36.84)
0.190
Yes
Yes
Yes
Yes
Yes
Yes
Yes
38.15
974
487

N shows the sample size and N g shows the number of counties. The
county-level cluster robust SEs are in parentheses. + p < 0.1, * p <0.05, ** p < 0.01, *** p < 0.001. First F shows the KleibergenPaap statistics to test for the weak instrument. I use data in 1888 and
1902.

95

Table A.18: Robustness Check: Main Result without Telephone-connected Counties
DID-IV
Steam Power [HP]
Railroad (-1892)
County FE and Year FE
Sum of Slope * Year FE
Log Flat Area * Year FE
Log Area * Year FE
Distance to Coast * Year FE
Initial Population Density * Year FE(Main) Traditional Road * Year FE
First FN
Ng

(1)
255.7∗∗(97.01)
Yes
Yes
Yes
Yes
Yes
No
No
41.25
960
480

(2)
267.8∗(106.8)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
36.91
960
480

Steam Engines(3)
21.34∗∗∗(6.134)
Yes
Yes
Yes
Yes
Yes
No
No
41.25
960
480

(4)
20.95∗∗(6.677)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
36.91
960
480

N shows the sample size and N g shows the number of counties. The countylevel cluster robust SEs are in parentheses. + p < 0.1, * p < 0.05, **p < 0.01, *** p < 0.001. First F shows the Kleibergen-Paap statistics
to test for the weak instrument. I use data in 1888 and 1902. I excluded
telephone-connected counties as of 1902 from the sample, counties where
Nagoya, Fukuoka, Kuwana, Sendai, Kumamoto, Hiroshima, and Kanazawa
cities are located.

96

Table A.19: Robustness Check: Main Result without Counties where State-owned Factories were Located
DID-IV
Steam Power [HP]
Railroad (-1892)
County FE and Year FE
Sum of Slope * Year FE
Log Flat Area * Year FE
Log Area * Year FE
Distance to Coast * Year FE
Initial Population Density * Year FE(Main) Traditional Road * Year FE
First FN
Ng

(1)
295.0∗∗(102.9)
Yes
Yes
Yes
Yes
Yes
No
No
39.36
956
478

(2)
278.6∗∗(106.0)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
35.37
956
478

Steam Engines(3)
20.53∗∗∗(6.090)
Yes
Yes
Yes
Yes
Yes
No
No
39.36
956
478

(4)
19.46∗∗(6.541)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
35.37
956
478

N shows the sample size and N g shows the number of counties. The countylevel cluster robust SEs are in parentheses. + p < 0.1, * p < 0.05, ** p < 0.01,*** p < 0.001. First F shows the Kleibergen-Paap statistics to test for the
weak instrument. I use data in 1888 and 1902. I excluded counties with stateowned factories.

97

Table A.20: Robustness Check: Interest Rate and Instrument
1888
Low
(1)
-0.0720(0.472)

Distance to the Path

1888-1902

High(2)-0.249(0.947)

Railroad County Prop (-1892)
County FE and Year FE
Sum of Slope (* Year FE)
Log Flat Area (* Year FE)
Log Area (* Year FE)
Initial Population Density (* Year FE)
Coastal (* Year FE)(Main) Old Road (* Year FE)
First F
N
Ng

No
Yes
Yes
Yes
Yes
Yes
Yes

No
Yes
Yes
Yes
Yes
Yes
Yes

36

36

Low
(3)

High
(4)

-1.699(1.212)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
13.52
539
36

-2.113(2.680)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
13.52
539
36

Source: Institute for Monetary and Economics Studies, Bank of Japan. Low
and High will mean the lowest and highest. The original data is monthly, so
I take average for each year. N shows the sample size and N g shows thenumber of counties. The robust SEs are in parentheses in column (1) and(2) and county-level cluster robust SEs are in parentheses in column (3) and(4). + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001. First F shows the
Kleibergen-Paap statistics to test for the weak instrument. I use data in 1888 incolumn (1) and (2) and 1888 - 1902 in column (3) and (4). I excluded counties
with state-owned factories.

98Table A.21: Long-Term Eﬀect of Railroads during 1888–1919 or 1888–1920
DID(1)
Panel A: Steam Power [HP]
Railroad (-1892)
Panel B: Log Population
Railroad (-1892)
County FE and Year FE
Sum of Slope * Year FE
Log Flat Area * Year FE
Log Area * Year FE
Distance to Coast * Year FE
Initial Population Density * Year FE(Main) Traditional Road * Year FE
Dist. to Destinations * Year FE
First FN
Ng

DID-IV
(2)

(3)

(4)

(5)

-56.54(1100.3)

6329.9∗ 5252.1∗∗ 5417.6∗∗ 4305.2∗(2582.7) (1881.5) (2077.3) (2016.3)

0.0689∗(0.0333)
Yes
Yes
Yes
Yes
Yes
No
No
No

0.141
(0.114)
Yes
Yes
Yes
Yes
Yes
No
No
No
41.52
974
487

974
487

0.102
(0.122)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
38.15
974
487

0.0621(0.113)
Yes
Yes
Yes
Yes
Yes
No
No
Yes
40.91
974
487

0.0138(0.120)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
36.48
974
487

N shows the sample size and N g shows the number of counties. The county-level cluster
robust SEs are in parentheses. + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001. First F
shows the Kleibergen-Paap statistics to test for the weak instrument. I use data in 1888 and1920 for steam power, and 1888 and 1919 for population.

99