From Samurai to Skyscrapers:
How Transaction Costs Shape Tokyo∗
Junichi Yamasaki† Kentaro Nakajima‡ Kensuke Teshima§
First version: May 2020

This version: March 2022

Abstract:
Whether transaction costs to assemble or split land can persistently hinderurban land use remains unknown. Constructing a 100 m*100 m-cell-level dataset of central
Tokyo from the 19th-century pre-modern era to the 21st-century skyscraper era, we study how
initial lot fragmentation has aﬀected urban development. We exploit a plausibly exogenous
supply shock of large lots in 1868, the release of local lords’ estates (daimyo yashiki) scatteredthroughout central Tokyo. Using ordinary least squares and a regression discontinuity design,
we ﬁnd that cells previously used as local lords’ estates have larger lots today, implying that
lot size persists through transaction costs. Such cells today see more tall buildings, higher land
prices, and higher labor productivity of ﬁrms. We also ﬁnd these eﬀects only in the core area,suggesting higher transaction costs in this area. Finally, the eﬀect of lot size on land prices
became positive only after the rise of skyscrapers. This implies that optimal lot size became
larger and assembly friction became more salient in the skyscraper age. Overall, transaction
costs to change lot size persistently hinder economic activities; in particular, this dominates
the potentially large beneﬁts of assembly for skyscraper development in growing urban cores.
Keywords: Transaction costs, historical persistence, skyscrapers, lot fragmentation, agglomeration economy
JEL Codes: R14, R30, O18, N95∗

We thank Federico Finan, Michael Gechter, Edward Glaeser, Jun Goto, Walker Hanlon, Jessie Handbury,
Takahiro Ito, Hiroyuki Kasahara, Jeﬀrey Lin, Yuhei Miyauchi, Chiaki Moriguchi, Hisahiro Naito, Nathan
Nunn, Atsushi Ohyama, Stephen Redding, Yoichi Sugita, Ryotaro Sugiyama, Yasuo Takatsuki, Masayuki
Tanimoto, Shuntaro Washizaki, Hisaki Yamaga, Jiro Yoshida, and seminar and conference participants fortheir comments and suggestions. We thank Yuri Enomoto, Kisho Hoshi, Takuto Iguchi, Kyosuke Izuo, Arisa
Kawasaki, Chika Kojima, Yukie Kojima, Hiroshi Kumanomido, Yu Osaki, Ryota Tanaka, and Masaya Waki for
their excellent research assistance. All the authors are aﬃliated with TDB-CAREE, Hitotsubashi University,
which provided the access to the Teikoku Databank dataset. We thank Yoshiki Hiramine and Keisuke Takano
for their assistance with the data. We gratefully acknowledge ﬁnancial support from the Japan Society for
the Promotion of Science (Nos. 19K13682 and 18K19955), Japan Center for Economic Research, Suntory
Foundation, Grant for Groundbreaking Young Researchers, Kikawada Foundation, Nomura Foundation, andHitotsubashi University. We also thank the International Research Center for Japanese Studies, the Tokyo
Metropolitan Government, and Takashi Kirimura for permission to use their data.
†
Department of Economics, Kobe University and Hitotsubashi University TDB-CAREE. E-mail:yamasaki@econ.kobe-u.ac.jp‡
Institute of Innovation Research, Hitotsubashi University. E-mail: nakajima@iir.hit-u.ac.jp§
Institute of Economic Research, Hitotsubashi University. E-mail: kensuke-teshima@ier.hit-u.ac.jp

1

Introduction

Cities are the center of economic activities, and thus, the eﬃcient use of scarce land in a city’scentral business district (CBD) can have a sizable impact on the functioning of the economy.
Furthermore, as the structure of economic activities evolves over time, reallocation of land tothe best usage at each point in time is important (Glaeser, 2011). For example, when ﬁrms
agglomerate in urban areas, demand for large oﬃce buildings is high. In particular, demand
for land use reallocation is greater in changing economies.
One key type of land transaction to construct a certain size of building is to change the
lot size by either split or assembly; however, transaction costs may exist to prevent optimaluse of land. For example, assembling land may be costly, because it involves negotiation withmultiple landowners. Such transaction costs are reported in the media and recognized bypolicymakers in many cities in the world (Nelson and Lang, 2007; Kirk, 2017; Chen, 2021).
The presence of such transaction costs may aﬀect city development in the long run bygenerating persistence in lot size if the transaction costs are greater than the beneﬁt of optimalland use. Notably, recent studies using rural/suburban areas show that land size persists, but
this persistence gradually disappears in the very long run (i.e., 150 years) (Bleakley and Ferrie,
2014; Smith, 2020; Finley et al., 2021). This suggests that there are substantial transaction
costs in the rural/suburban land market, but the costs are outweighed by the beneﬁt of optimalland use over 150 years.
However, we do not know the presence and nature of lot size persistence in cities, where theeconomic consequence of such persistence would be signiﬁcant. Findings in rural/suburban
areas might not be applicable to cities. On the one hand, cities have a high beneﬁt of optimalland use owing to agglomeration economies, which may weaken lot size persistence. On theother hand, transaction costs can be higher in cities, possibly because they tend to have moreheterogeneous land uses or the potential beneﬁts of land assembly itself intensiﬁes landowners’
strategic behavior in their negotiation of land assembly, which may strengthen lot size persistence. Understanding the previous relative importance of these two forces has an implication
for future cities, particularly those in developing countries, which have slums with fragmented
lots, and their prospects for urban growth (Bryan et al., 2020; Glaeser, 2021).
Furthermore, when transaction costs are high enough to generate lot size persistence, the1consequence of such persistence can be diﬀerent over space and time. For example, the valueof large lots may be greater with the rise in tall buildings generating agglomeration economies,
because tall buildings require large footprints.1 By investigating how the lot size eﬀect varies
with the change in economic environment, we can shed light on the nature of the lot size eﬀect
and predict the eﬀect in a particular setting.
In this study, we analyze land use and values in Tokyo over a 150-year period. Several
features of Tokyo make its setting an ideal laboratory for studying the long-run eﬀects of theinitial lot size. First, there is a natural experiment that, in our view, oﬀers the closest analog
to exogenous releases of land with larger lot sizes on a large scale throughout central Tokyo.
During the feudal era before 1868, 20% of the land in Tokyo was occupied by daimyo. Daimyo
were among the top of the samurai (warrior) class in Japan and governed their local domain
outside Tokyo as feudal local lords, but had to own estates in Tokyo (daimyo yashiki ) for
political reasons, which we explain later in the background section. These estates were muchlarger than the lots in the other areas in Tokyo. However, after a political regime change in1868 (the Meiji Restoration), these local lords were forced to release their estates into theprivate market. This was a plausibly exogenous shock providing large lots to the Tokyo landmarket.
Furthermore, for a cleaner identiﬁcation, we exploit a discontinuity in a particular central
Tokyo area due to the central government’s zoning before 1868. Speciﬁcally, around the
beginning of the 17th century, the Tokugawa shogunate, Japan’s feudal military governmentthat preceded the Meiji period, designated the western half of newly developed areas to local
lords’ estates and the eastern half to commoners. When the Shogunate further reclaimed
land to the east, the newly reclaimed land became the local lords’ estate zone. These newly
developed areas were in lowlands close to the seashore at the time, and therefore, are likely toshare similar characteristics.
Second, Tokyo provides the historical and modern datasets necessary for our study. We
can keep track of land prices or lot fragmentation over time after the Meiji Restoration, when1
We conﬁrm this relationship using data for Tokyo (Figure A.1). In addition, we observe a similar pattern
between building height and footprint for New York: using height and footprint data, we plot them in a similarmanner to Figure A.1. The result shows a positive relationship between footprint and height. Moreover,assuming 4 m per story, the 120 m-or-higher group in New York corresponds to the 30-story-or-higher group in
Tokyo, both of which have a very similar percentile of footprint (see Figure A.2). These results imply that therelationship in Tokyo (Figure A.1) is not solely due to earthquake risk in Japan.

2the modern property system was introduced. We can also measure the location and height ofall buildings in today’s Tokyo.
Third, Tokyo is one of the largest and most active cities in the world and, after World War II(WWII), it experienced changes in its economic environment, making skyscraper construction
possible. This enables us to study the nature of lot persistence in an urban setting in diﬀerent
economic environments.
We analyze the full sample using ordinary least squares (OLS) with geographical control
variables and, for a cleaner identiﬁcation, we employ a regression discontinuity (RD) designusing the clear zoning boundary for a particular area of the sample. The results of both
approaches consistently show lot size persistence: the presence of local lords before 1868
results in larger lots in 2011. We also ﬁnd that larger lots facilitate urban development today:these areas have taller and fewer buildings, more ﬂoor space, and higher land prices. Theseeﬀects seem economically substantial: for example, if we increase the share of local lords’
estates from zero to one, the number of more-than-or-equal-to-30-story buildings increases by
0.039, which corresponds to its unconditional mean and decay being 4.5 km away from thecenter. This persistence and its economic eﬀects contrast with the prediction of the Coasetheorem without transaction costs (Coase, 1960), whereby the initial allocation of property
rights does not aﬀect long-run outcomes.
To investigate the nature of lot size persistence, we compare the heterogeneous eﬀects
between the core area and the non-core area.2 Lot size persistence and its related eﬀects
may not exist in the core area because the beneﬁt of assembly by constructing high-risebuildings exceeds the transaction costs, as pointed out in Coase (1960).3 However, the result
is inconsistent with this view: we ﬁnd lot size persistence and its eﬀect on land use today
only in the core area. This result implies that transaction costs are higher in the core area,
consistent with other studies (Bleakley and Ferrie, 2014; Smith, 2020; Finley et al., 2021)
ﬁnding no lot size persistence in the rural/suburban land market, which is more similar to the2

For the OLS, we deﬁne the area inside the Yamanote loop line, which connects the major hub stations ofregional and urban railway/subway services such as Shijuku, Shibuya, and (central) Tokyo station, as the corearea. Its area, 60 km2 , roughly corresponds to the area of Manhattan. For the RD analysis, we separate theborder line into two parts: close to and far from the core area.
3
Coase (1960) states that “it is clear that such a rearrangement of rights will only be undertaken when theincrease in the value of production consequent upon the rearrangement is greater than the costs which would
be involved in bringing it about.”3

non-core area than to the core area.
The higher transaction costs in the core area can be explained by two things. One isthat higher potential gain of assembling land may endogenously increase transaction costs
by intensifying landowners’ strategic behavior in their negotiation of assembly costs, which iscalled the hold-out problem in the literature (Miceli and Sirmans, 2007; Brooks and Lutz, 2016;
Grossman et al., 2019). Another possibility is that characteristics other than the potential
beneﬁt of assembly may play a role. For example, population or land use may be moreheterogeneous in the core area and thus landowners may face diﬃculties in collective action(Olson, 2003). Although we cannot quantify these channels, the results suggest a large role oftransaction costs in the urban core land market, which is against the natural conjecture thatthe large economic potential can overcome the cost of assembly.
Next, we analyze the mechanism by which lot size aﬀects land prices. We ﬁnd a positiveeﬀect on land prices in the 2010s, but the sign of this eﬀect may change depending on thetechnological environment, such as the possibility of constructing skyscrapers. To investigate
this point, we examine the eﬀect on lots and land prices before WWII, when Tokyo had
no skyscrapers and industries were less knowledge based. We ﬁnd that local lords’ estates
decreased the number of lots, but had negative eﬀects on land prices. We also ﬁnd that theeﬀect on land prices became zero in 1972 and turned positive in 1983, suggesting that before
WWII, there were split frictions: lots in local lords’ estates were too large for optimal landuse, but were not split owing to split costs. However, after WWII, these large lots obtainedadvantages from technological change (i.e., increased high-rise buildings and the emergence ofskyscrapers and the transition to the knowledge economy) and assembly costs. This suggeststhat the value of a large lot can change according to the technological environment (i.e.,
positive eﬀects arise only after the 1970s with increased high-rise buildings).4
We consider other potential channels to explain the causal link from local lords’ estates tothe land prices of today. For example, lower transaction costs might have facilitated public
infrastructure construction and increased amenities. In addition, larger lots might have alleviated destruction during WWII and aﬀected subsequent land development. Furthermore, thepresence of local lords’ estates might aﬀect land prices by changing the size of the blocks (area4This also suggests that the positive eﬀect on land prices in the main results is not driven by time-invariant
location-speciﬁc eﬀects.

4

surrounded by roads), ﬂoor–area ratio (FAR) regulation, or initial land price in the 1860s.
Although we do not exclude these channels, we ﬁnd that controlling for these factors does notchange the main results qualitatively. We also consider alternative channels to explain thechange in the sign of the eﬀect on land price changes after WWII, such as land use change orowner change caused after WWII, but none of these alter the results.
Finally, to investigate a potential beneﬁt of large lots through agglomeration economies,
we examine the eﬀect of local lords’ estates on ﬁrm productivity using ﬁrm-level microdata in2017 with OLS and RD analyses. We ﬁnd a positive eﬀect on revenue per worker, a proxy of
total factor productivity (TFP). Furthermore, we ﬁnd that this eﬀect is higher in the upper
quantiles, implying that the eﬀect on ﬁrm productivity is through the agglomeration beneﬁts
channel, rather than the exit of less productive ﬁrms (the selection channel). To investigate
the role of tall buildings more explicitly, we conduct a similar analysis using the data in 1993,
when Tokyo had fewer skyscrapers. Both the OLS and the RD analyses show weaker eﬀects on
ﬁrm productivity in 1993 than in 2017. This diﬀerence between 1993 and 2017 is attenuatedwhen we control for the height of buildings. These results suggest that the height of buildings
is an operating channel behind the positive eﬀect on ﬁrm productivity.
Our study contributes to the literature on the role of transaction costs in urban development. Past studies consider coordination problems in redevelopment (Hornbeck and Keniston,
2017; Owens et al., 2020) and delays owing to litigation (Gandhi et al., 2021), for example.5 We
ﬁnd that the transaction costs incurred in changing lot sizes can generate lot size persistence
and aﬀect economic activities over 150 years, particularly in the core area, which is a novel
ﬁnding in the literature.6 These results are linked to recent studies on the formalization costsof slums (Gechter and Tsivanidis, 2018; Harari and Wong, 2019; Michaels et al., 2021). Thesestudies discuss the role of weak property rights in slums limiting their land mobility and ﬁnd
its negative impact on urban development. On the other hand, our results in Tokyo suggest
even if people in slums are entitled to strong property rights, it may not lead to urban development because slums, often dense and fragmented, face assembly costs at redevelopment, as
5
As a related study, Libecap and Lueck (2011) compare two land demarcation regimes, (1) metes and boundsand (2) the rectangular system, in Ohio and ﬁnd a positive impact of the rectangular system on farmland value.
Meanwhile, we examine an urban setting in which larger lots may have large beneﬁts through the constructionof tall buildings.
6
As discussed earlier, we are not aware of studies on lot size persistence and its economic impact in thecontext of cities.

5

Glaeser (2021) discusses.
Our study also oﬀers a perspective on the conﬂicting results found in studies of lot sizeand land prices. Some studies ﬁnd a negative premium of large lots (White, 1988; Brownstone
and Vany, 1991), while others ﬁnd a positive premium (Tabuchi, 1996) with a diﬀerence-indiﬀerences strategy (Brooks and Lutz, 2016).7 Our study examines the relationship between lot
size and land prices based on a natural experiment and compares the relationship in diﬀerent
periods and locations to shed light on how lot size premia arise.
We also contribute to the recently growing literature on building heights (Liu et al., 2017;
Ahlfeldt and McMillen, 2018; Ahlfeldt and Barr, 2022). We investigate the obstacles to constructing high-rise buildings (Barr et al., 2011; Jedwab et al., 2020), which is key to enhancing
the beneﬁts arising from the density of economic activities (Ahlfeldt et al., 2015). We oﬀer aunique contribution to the literature by showing a very close link between lot fragmentationand tall buildings. This link is discussed in the previous literature (Barr, 2016), but systematic evidence is scarce. We also ﬁnd that because lot fragmentation prevents the constructionof tall buildings, the cost of lot fragmentation becomes more salient with the availability of
construction technology for tall buildings.
Our study belongs to the expanding literature that analyzes cities with historical datasets(Hanlon and Heblich, 2021), such as Davis and Weinstein (2002), Bleakley and Lin (2012),
O’Grady (2014), Redding and Sturm (2016), Baruah et al. (2017), Hanlon (2017), Hornbeck
and Keniston (2017), Brooks and Lutz (2019), Ambrus et al. (2020), Heblich et al. (2020),
Harari (2020), and Dericks and Koster (2021). Our study oﬀers a new channel through which
history matters: historically determined lot size diﬀerences persist, but the positive eﬀect of lot
size develops only after the rise of the knowledge economy and the development of construction
technology.
The rest of this paper is organized as follows. Section 2 provides background informationon land use in Tokyo. Section 3 describes the data and the empirical strategy. In Section 4, we
present the results. In Section 5, we brieﬂy discuss policy implications. Section 6 concludes.

7
As a related study, Leonard and Parker (2021) ﬁnds that land fragmentation decreases oil production in
North Dakota because shale extraction is proﬁtable only when a suﬃcient amount of contiguous land is used.

6

2

BackgroundWe ﬁrst describe the historical background in each period (1600–1868, 1868–1945, 1945–) andthen explain the population growth in Tokyo, related regulations, and anecdotes.

2.1

During the Edo Period: 1600–1868

Tokyo, which was called Edo during the Edo period (1600–1868), is one of the most prosperous
cities in the world, but it was not a big city prior to the Edo period.8 A local lord constructed
Edo Castle in 1457, but Edo remained a small town, surrounded by a marsh.
This situation changed in 1590, when Tokugawa Ieyasu, one of the most powerful feudallords of that time, was transferred to Tokyo. He reconstructed the castle to strengthen his
military capacity and reclaimed the inlet in front of the castle to expand the land. He also seems
to have adopted class-based zoning inside the outer moat (the “inner area”).9 The area closest
to the main gate on the east side of the castle was used for the estates of local lords, who govern
their local domains outside Tokyo. The east side of this area was allocated to Tokugawa’s
oﬃcers and to commoners as business districts. Importantly, local lords were among thehighest rank of the samurai class, and therefore, local lords’ estates were on average larger
than other buildings used by oﬃcers (lower-ranked samurai) or commoners. We exploit theclear zoning on the east side of the castle for the RD design (the dash-dot line in Figure 1(a)).
Tokyo became the political capital after Tokugawa won signiﬁcant wars in 1600 and 1615,and his government (Shogunate) ordered all local lords to have estates in Tokyo for politicalinteractions.10 As a result, the Shogunate further reclaimed land and allocated a new area for
local lords. Therefore, the initial coastline (the solid line in Figure 1(a)) became a boundary
between the local lords’ estates zone and the commoners’ zone, except in the northwesternpart, where some local lords’ estates were located by chance on both sides (the dash-line in
Figure 1(a)). This is another discontinuity in our identiﬁcation strategy. The Shogunate also
developed an area outside the outer moat (the “outer area”). In the outer area, the local lords
chose the location for their estates and had to ask permission from the Shogunate to use the8

See Kawasaki (1965), Suzuki (2000), and Matsuyama (2014) for a more detailed historical context.
Other local lords often adopted planning systems in their castle cities.
10
In particular, demand for land grew because Tokugawa required that all local lords (approximately 250)
alternate between living in Tokyo and living their local domains and that their families stay in Tokyo as
hostages. These policies signiﬁcantly increased demand for local lords’ estates.
97

land. In that sense, the Tokugawa shogunate controlled urban land use. However, unlike theinner area, there is no indication of a clear zoning policy.
Although local lords could swap their estates with other lords, the social class of land users
for each land area seems to have been stable until the end of the Edo period in 1868. At thattime, local lords’ estates occupied about 20% of the land in Tokyo, as shown in a map fromthe 1850s (Figure 2). Tokyo experienced signiﬁcant economic and cultural growth during theEdo period, and its estimated population at the end of the 1860s was about 1 million.11[Figure 1 about here.][Figure 2 about here.]

2.2

Meiji Restoration and Pre-WWII: 1868–1945

A commonly held view among historians is that the Meiji Restoration caused a signiﬁcantly
negative shock on Tokyo’s economy. After the collapse of the Tokugawa shogunate, local
lords were no longer required to stay in Tokyo and their estates became vacant. Aroundhalf of Tokugawa’s oﬃcers moved to Shizuoka, where Tokugawa was transferred to, about
150 km away from Tokyo. As a result, the samurai class, which occupied a large proportionof the Tokyo population, migrated out of Tokyo and its economy, which had previously beensustained by the samurai class, collapsed. Moreover, part of Tokyo became a battleﬁeld (during
the Battle of Ueno) in the civil war during the Meiji Restoration. Due to this economic turmoil
in Tokyo, it was uncertain which of Tokyo, Osaka, or Kyoto, three important economic andpolitical cities, would become the new capital of the Meiji Restoration. Finally, the new
government chose Tokyo as its capital, and Tokyo began to grow economically as the nation’s
modern economy took oﬀ. In that sense, the Meiji Restoration is regarded as the initial pointof modern Tokyo’s economic growth.
Local lords typically held three estates during the Edo period, but the Meiji government
allowed local lords to own just one estate in Tokyo and conﬁscated the others. In the corearea, Kasumigaseki, the Meiji government transformed these estates into government oﬃces

11
There are many estimates of the population of Edo, but most estimates range from 1 million to 1.5 million.
See, for example, Kito (1989).

8

or training ﬁelds for the army. The Meiji government sold or gave the remaining estates tothe private sector.
There was continuity in the lot boundary between the periods before and after the Meiji
Restoration. Along with these land transformation processes, the Meiji government introduceda modern property rights and tax system (chiso kaisei) and determined the boundaries of lots
based on the land usage before the Meiji Restoration. Therefore, the local lords’ estates
retained their large lots in the early Meiji period.

2.3

After WWII: 1945–

After 1945, the descendants of local lords experienced a signiﬁcant policy shock. Despite theregime change in 1868, local lords retained their political elite status as noblemen (kazoku)
and possessed privileges such as seats in the house of peers. However, in 1946, the General
Headquarters introduced a very high asset tax to remove the political and economic elites
supported by Imperial Japan. In addition, the General Headquarters deprived the elite of
their political and economic privileges. There are many anecdotes of local lords being required
to sell their properties to pay the asset tax (Sakai, 2016). Consequently, most of the former
local lords’ estates became owned by the private sector in this period, with the exception of
those in the Kasumigaseki area.

2.4

Population Growth and Related Regulations

After Tokyo became the capital of Japan in 1871, its population recovered and began to grow.
In the eight wards of central Tokyo, the residential population, which had been 0.89 million
in 1883, rose to 2.17 million by 1920 (Tokyo Hu, 1887, 1923). Since WWII, the population
became stable (in 2015, it was 1.95 million), but the daytime population (number of people
present during normal business hours) has been increasing (2.95 million in 1955 vs. 4.72 million
in 2015), implying that business activities have continued to expand (Tokyo To, 2015). OldTokyo is now the center of Greater Tokyo, which has about 38 million inhabitants and is thebiggest megalopolis in the world.
Post-WWII economic growth increased demand for high-rise buildings. In 1952, the government deregulated the height restriction that had prohibited buildings over 31 m since 1919.

9

In 1968, the ﬁrst skyscraper, the Kasumigaseki Building, was constructed. The number ofbuildings over 30 stories has been increasing in Tokyo’s 23 wards, rising from 32 in 1990 to 86
in 2000 and 260 in 2010 (Tokyo Shobo Cho Kikaku Chouseibu Kikakuka, 1990–2010).

2.5

Anecdotes of High Assembly Costs

Several anecdotes suggest the presence of high assembly costs, consistent with our argument.
A large conglomerate, Mitsui, was originally an exchange trader and kimono trader in the
Edo period, and held a small lot as its head oﬃce in a former commoners’ area in the CBD(Muromachi). After Mitsui became a large conglomerate, it planned to assemble lots nearby
to expand its headquarters, but it did not succeed and faced opposition by landlords. It ﬁnallycompleted the planned assembly in 1969, but lots in Muromachi remain fragmented.
Meanwhile, a former local lords’ estate area in the CBD (just about 1 km from Muromachi)
has been owned by another large conglomerate, Mitsubishi, since the 1890s, when it bought
the land from the government. Lots are larger than in Muromachi, and there have been
large-scale developments such as Western-style brick buildings before WWII and skyscrapers
today. Comparing these two close but diﬀerent areas, Washizaki (2015) suggests that lot
fragmentation is a potential reason for the low number of skyscrapers in Tokyo.
As another example, the Mori Building Company planned a large-scale (5.6-ha) redevelopment in Akasaka in 1967 and obtained a small lot. Although the government approved
the plan, it was not until 1983 that it could obtain permission from landlords and start theconstruction of the building (Akasaka ARK Hills). In 1986, the building ﬁnally opened. The
company also planned a similar redevelopment project in another area, and it took 17 years
to open the building (Roppongi Hills, opened in 2003). The former CEO looks back on thesedevelopments as a project that would have been impossible if the company had not been family
owned or long-sighted (Mori, 2009).

3

Data

We constructed a 100 m*100 m-cell-level dataset spanning 150 years based on scanned printed
maps and other electronic data.12 We constructed the dataset within the old Tokyo’s (Edo’s)
12This cell size roughly corresponds to the median of area of local lords’ estates (13845 m2 ).

10

city area, which covers and remains the center of economic activities in Tokyo during theEdo period. Among Japanese listed ﬁrms with their headquarters in Tokyo, the headquartersof 72% of ﬁrms are located in our sample area.13 In this section, we present a table with
deﬁnitions of the main variables and their sources (Table 1) and brieﬂy explain the sourcesof the main variables. Panel A in Table 2 shows the descriptive statistics. For the ﬁrm-level
microdata, we do not aggregate the data at the cell level to analyze ﬁrm-level locational orentry/exit choices and/or use ﬁrm-level information (i.e., industry) as control variables. Panel
B in Table 2 shows the descriptive statistics in 1993 and 2017.
Land Usage before 1868 We digitized a map showing landownership in the 1850s. Thismap documented the types of ownership for each lot (local lords, bureaucrats, commoners,
and other owners such as temples).14 Figure 2 plots local lords’ estates, showing they are welldistributed across Edo city. Based on this map, we calculated the share of land owned by local
lords for each cell.15
Lots Cadastral maps are available for 1869, 1876, 1912, 1931, and 2011 (Nishikawa andNishikawa, 1880; Ichihara, ed, 1876; Tokyo Shiku Chosakai, 1912; Seizusha, ed, 1931-1935;
TDi and Inc, 2017). We digitized or used these maps to calculate the number of lots within acell.
Land Prices Before 1945, land prices were available for 1876, 1912, and 1931. The 1876
map can be connected with the land price list published later (Nakai, ed, 1880) using theaddresses.16 The cadastral maps in 1912 and 1931 list the land prices for each lot. These lists
have land prices (and land rental prices for 1931) so that we can calculate the area-weighted
average land price for each cell.
13

Source: http://disclosure.edinet-fsa.go.jp. These ﬁrms with headquarters in our sample area account for 35% of the total number of ﬁrms in Japan.
14
We used georeferenced digital images of this map for creating shapeﬁle (ABP Company, ed, 2009). The
primary sources are several maps published at that time, such as (Kageyama et al., eds, 1849-1862).
15
An alternative and perhaps more natural treatment variable is the number of lots in the 1850s, but the
map describes only blocks (area surrounded by roads) in the commoners’ area without lot boundaries, andthus, we cannot count the number of lots from the map. Instead, in the robustness check, we employ the
maximum lot size of local lords’ estates as an alternative treatment variable.
16
Some areas changed the address system during these periods and we could not match all of them. This
resulted in signiﬁcant missing values for land prices in 1876 in the dataset. In addition, the maps in 1912 and1931 cover slightly diﬀerent areas from the area covered by the map during the Edo period. See Figure A.3
for the heatmaps of the land price variables.

11

These land prices are based on the evaluations used as the basis for land tax. The evaluationof each lot in the 1860s–1870s referred to the market land price used in the Edo period.
However, for land owned by local lords and bureaucrats, there were no market transactions
during the Edo period. For this land, the price when the land was sold to the private sector
by auction was used. When such land was transferred for free, the neighbor’s land prices soldby auction to the private sector were used.17
In 1910, the government updated the land prices in cities using market land rental prices,and the data in 1912 contained this land price. The rental price was multiplied by 10 to
calculate the land price. If it exceeded the previous land price by more than 18 times, the land
price was reduced to avoid a drastic increase in the tax burden for landowners. In 1929, theyagain updated the land prices by simply using rental prices, which contained data for 1931.
After 1945, the government started to use a diﬀerent tax system. It ﬁrst evaluated the placevalue at the road level and then multiplied it by lot-speciﬁc factors such as shape. Because lotspeciﬁc factors are automatically related to lot size, the road-level price is suitable to capture
the eﬀects of lot size on economic activities. The data in 2012 (Research Center for Property
Assessment System, 2012) contain this road-level price, and we calculated the length-weighted
average land price data within a cell.
These variables before the 1980s are not easily available, and thus, we complemented theland price data in 1972 and 1983 by using Tokyo-to Takuchi Tatemono Tohirikigyo Kyokai(1972) and Tokyo-to Takuchi Tatemono Tohirikigyo Kyokai (1983). These maps produced by
the real estate agents’ association record the estimated market value of land per area at eachplace.
Buildings The Tokyo Metropolitan Government has been producing an electronic map covering all the buildings and land usage in Tokyo every ﬁve years since 1986 for urban planning(Tokyo Metropolitan Government, 1986, 1991, 1996, 2001, 2006, 2011). From these maps, we
calculated the number of buildings, total ﬂoor area provided, and average number of stories.
We also conﬁrmed the positive relationship between the number of stories and footprints of
17

There might be a concern that local lords’ estates were priced diﬀerently for political reasons. However,this way of selling their land suggests that the land price fairly reﬂects the market value (Fukushima, 1962).
We also analyze whether such political consideration might change our results using whether landowners areformer local lords’ descendants in the landowner characteristics data of 1931.

12

buildings using data in 2011. In addition, the government has been making electronic maps
for land usage, from which we calculated the share of land used for business or residence.
Geographies We used geographical variables as control variables, because geography may
aﬀect the supply of buildings (Saiz, 2010) and determine the location of local lords’ estates.
Altitude data are available from the Ministry of Land, Infrastructure, Transport and Tourism(2014). We calculated the average and standard deviation of altitude in a cell: higher places
may attract richer people and ﬂatter places may be suitable for large-scale development. We
also controlled for earthquake risk. Tokyo Metropolitan Government (2018) assesses several
types of risk (e.g., building materials) at the community level, and we used the risk resulting
from the type of ground to focus on purely geographical risks. We took the area-weighted
average of these community-level risks at the cell level.
Firm-level Microdata To analyze ﬁrm-level productivity and ﬁrms’ locational or entry/exit
choices, we used a ﬁrm-level dataset in our sample area. We obtained the data from the
Teikoku Databank Center for Advanced Empirical Research on Enterprise and Economy(TDB-CAREE) at Hitotsubashi University. Teikoku Databank is a major Japanese credit
research company, and this dataset covers most Japanese ﬁrms. These data contain basic information such as industry, locations of headquarters, and the number of workers and revenue,
so that we could construct revenue per worker, a proxy of TFP.
Before discussing the regression analysis results, we illustrated our analysis using raw data
for an area around a station in the CBD. Figure 3(a) shows one of the primary sources.
Figure 3(b) shows the distribution of local lords’ estates using red-hatched polygons. We
overlaid the cadastral map of today with the Figure 3(c) and found that former local lords’
estates are associated with larger lots today. When we overlaid today’s tall buildings with
Figure 3(d), most of the tall buildings were found to be located on land that was formerly
local lords’ estates. In the regression analysis, we conﬁrmed these relationships using the wholesample while considering potential endogeneity bias. When we examined the aerial images of
the area using Figure 3(e), we observed a great deal of variation in the height of buildings inthis small area, suggesting high land assembly costs.
[Table 1 about here.]13

[Table 2 about here.][Figure 3 about here.]

4

Results

We ﬁrst show the main results analyzing the eﬀect of local lords’ estates on the outcomes oflots, buildings, and land prices in our modern data. Then, we analyze the nature of persistence
and the lot size eﬀect, the role of tall buildings, and other possible mechanisms that might
explain the main results. We also present the eﬀects on ﬁrm productivity using ﬁrm-level
microdata as suggestive evidence for the agglomeration beneﬁts generated by large lots.

4.1

Main Results

OLS using the full sample Table 3 shows the baseline results from the OLS regressing
the outcome variables on the local lords’ estates share variable. Distance from the center (EdoCastle or today’s Imperial Palace) is associated with a reduction in local lords’ estates andaﬀects urban development; therefore, we control for this variable as a baseline speciﬁcation in
Column (1). Panels A and B show that the greater the proportion of local lords’ estates, theless lots are fragmented, both for 1872 and for 2008–2011. The point estimate shows that if acell is occupied by local lords’ estates, it decreases the number of lots in the 2010s by 23.29,
about half of the mean, implying substantial lot size persistence.
In Panels C to E of Table 3, we ﬁnd negative impacts on the number of buildings andpositive impacts on the number of stories and tall buildings. The decreased number of buildings
implies that large-scale developments with greater footprints are more common in areas thatused to be local lords’ estates. The point estimate for the number of buildings more than orequal to 30 stories is 0.039, which is about the same size as its mean.18 In Panel F, we ﬁndthat land prices increase as predicted. The point estimate shows a large impact: if local lords’
estates occupy a cell (corresponding to a reduction in the number of lots by 23.3 in 2011), it
18

The point estimate for the number of stories is just 0.8, but this is the result of a mixture of positive andnegative eﬀects. When we run a quantile regression, in the 10th percentile, the point estimate is about −0.3,
suggesting that some local lords’ estates became more low-rise housing areas. However, in the 90th percentile,which is more relevant for our study than the lower percentiles, the point estimate becomes 4.3, as predicted.
See Table A.1 for the results for the other percentiles and the results with the control variables.

14

increases the land price by 33.8%.
In Column (2) of Table 3, we control for the key geographical variables, the distance fromthe center, and altitude (mean and standard deviation). We add the mean and standard
deviation of altitude, as higher places may attract richer people, whereas ﬂatter places may besuitable for large-scale development. In Column (3), we control for the longitude and latitude
polynomials to exploit more local variation. In Column (4), we also control for earthquake
risk, which would aﬀect the construction cost and the decision to build high-rise buildings.
The results remain largely unchanged by adding these controls. We also consider the spillover
eﬀects from adjacent cells, but the qualitative results do not change.192021 Overall, theseresults indicate lot size premia and the presence of assembly costs.
[Table 3 about here.]
Exploiting a Historical Zoning Policy As another identiﬁcation strategy, we exploit
a historical zoning policy to conduct a local randomization analysis, as brieﬂy explained in
the background section. Figure 1(a) shows a part of central Tokyo area, some of which theTokugawa shogunate developed via reclamation. At the initial declamation, the shogunate
developed the land to the eastern part of the U-shaped line, the dash and solid part, whichbecame the initial coastline. At the same time, the shogunate clearly set the dash-dot partof the U-shaped line as a boundary between the local lords’ estates zone and the commoners’zone, although we are not aware of formal documents specifying this zoning. The estates
shown with a red border are obviously larger than the lots to the east of the dashed line, thecommoners’ zone. After the increase in demand for land by local lords, the shogunate further
reclaimed the area to the east of the initial coastline, reaching today’s Sumida River. These
19
Expecting positive spillover eﬀects from adjacent local lords’ estates is natural, and if local lords’ estates
are spatially correlated, this would bias the coeﬃcient in the main results. Note that it does not alter thegeneral interpretation that local lords’ estates prevent lot fragmentation and increase skyscraper construction
and land prices. Still, we investigate the spillover eﬀects by adding the local lords’ estate variables deﬁned bya larger square (e.g., 3*3 cells) into the main speciﬁcation. Table A.2 shows that the spillover eﬀect exists up
to the 3*3 cells (100–140 m from each cell) in most outcome variables. One exception is the result of the land
price, which is unstable, but may suggest a spillover eﬀect reaching farther. From this ﬁnding and to address
the concerns discussed in Kelly (2019), we examine the robustness of the main result against the choice ofthreshold for the spatial correlation in the error terms, but the results are mostly robust even when we extend
the threshold to 500 m or 1000 m, as shown in Table A.3.
20
To check the robustness to the speciﬁcation of the treatment variable, we also use the maximum of the lot
area (km2 ) of local lords’ estates in each cell. The results are qualitatively the same, as presented in Table A.4.
21
As a further robustness check, in Table A.5, we show the coeﬃcient stability using the method developedby Oster (2019). We ﬁnd that unobserved confounders do not alter the signs of the estimated eﬀects.

15

areas were occupied largely by local lords. Therefore, the initial coastline became anotherboundary between the local lords’ estate zone and the commoners’ zone, and as a whole,
the U-shaped line works as a boundary between the two zones except the northeastern dashpart without the gray-colored buﬀer in Figure 1(b), where local lords’ estates happened to belocated in both zones. In Figure 1(b), we also overlay high-rise buildings in 2011, indicatedby the black (more than or equal to 30 stories) and gray (15–29 stories) rectangles, and we
observe that they are mainly located in the local lords’ estate zone.
We ﬁrst rely on graphical representation using the sample average and a polynomial regression, as shown in Figure 4, to examine the distribution of the variables. We use cells
whose centroids are within 1 km of the boundary, but exclude cells whose centroids are within
50 m of the boundary, because such cells are separated on both sides and attenuate the jump
(if any) at the discontinuity. The x-axis is the distance from the boundary, taking a positive and negative value in the local lords’ estate zone and the commoners’ zone. Figure 4(a)shows a clear discontinuous jump in the presence of local lords’ estates. Figure 4(b) and 4(c)show the mean and standard deviation of altitude, respectively. Figure 4(b) shows no clear
discontinuity at the boundary in the mean of altitude. Figure 4(c) shows some discontinuity
at the boundary in the standard deviation of altitude, but higher ruggedness is disadvantageous to development, and thus, the simple RD design does not overstate the positive eﬀects
on development. We check the robustness to controlling for these variables in the regression
analysis. Figure 4(d)–4(f) show that the western area has fewer lots, more high-rise buildings,
and higher land prices.22
For the regression analysis, we employ a local randomization approach, because we do
not have a large sample along the boundary (Cattaneo et al., 2019).23 We use the cells
whose centroid is within 250 m of the boundary, corresponding to about one block from theboundary, which is shown as the gray-shaded area in Figure 1(b). We do not use the boundary
in the northeastern part for this analysis (dashed-line without the gray-shaded buﬀer), becausesome local lords had estates along the intimal coastline. We deﬁne a Local Lords’ Estates Zone

22

The plot of land prices within about 500 m of the boundary suggests the presence of positive spillovers
arising from economies of density, which is consistent with the spillovers we ﬁnd in the OLS analysis (Table A.2).
23
We also employ an RD approach using the polynomials of the distances from the boundary as the control
variables. Table A.6 shows qualitatively consistent results with the local randomization approach (Table 4),
but larger standard errors, as expected.

16

dummy by the location of the centroid of cells and regress the outcome variable on this dummy
and the other controls. Table 4 shows the results. In Column (1) of Panel I, we regress the shareof local lords’ estates on the Local Lords’ Estates Zone dummy, which conﬁrms the expectedlarge impact.24 Column (2) adds the distance from the center and whether the centroid ison the western or eastern side of the overground railroads (also shown on the map), because
overground railroads often divide economic activities. Column (3) adds the controls for theother geographical variables, the mean or standard deviation of altitudes, and earthquake risk.
The results are similar across the speciﬁcations. In Panels A–G, we ﬁnd a very similar pattern
to that shown for Panels A–G in Table 3. Again, there is a negative eﬀect on the numberof lots, implying lot size persistence, and a positive eﬀect on high-rise buildings. The eﬀect
on land prices is not statistically signiﬁcant, unlike in Table 3 because of higher standard
errors. We analyze the eﬀect on ﬁrm-level productivity in the next section, which gives us
more precise and direct evidence of the eﬀect on ﬁrm productivity. Another ﬁnding is thatthe point estimates for high-rise buildings and land prices are larger in magnitude than thosein Table 3. This is because this area is the most central part of Tokyo and the agglomeration
beneﬁts from constructing high-rise buildings are larger.25[Figure 4 about here.][Table 4 about here.]
Size of the eﬀects To understand the magnitude of the eﬀects in the OLS analysis, we
compare them with the distance-decay function in land prices and the number of skyscrapers.
Table 5 column (1) shows that the land price decreases by about 17% when 1 km away from
Tokyo station (today’s center). On the other hand, our local lords’ estate eﬀect is 32% (column(2), a replication of column (2) in Panel F, Table 3), meaning that the eﬀect is comparable withbeing about 2 km away from the center. Similarly, the eﬀect on the number of tall buildings iscomparable with being about 4.5 km away from Tokyo station (column (3) vs. (4) in Table 5).
These estimates imply the non-negligible role of transaction costs.
24

In the tables showing the RD results, we add Panel I into the top of the panels so that the panel structureis the same with that in the OLS tables.
25
As a further robustness check, in Table A.7, we show the coeﬃcient stability using the method of Oster(2019). We ﬁnd that unobserved confounders do not alter the signs of the estimated eﬀects.

17

A within-city analysis is limited for assessing a city’s overall impact. For example, thenumber of skyscrapers when we replace the commoners’ zone with local lords’ estates is hardto predict because our data do not tell us the aggregated demand function of skyscrapers inTokyo. As an extreme case, demand for skyscrapers in Tokyo is ﬁxed (i.e., the demand curveis vertical), and thus, removing the transaction costs may not increase the total number ofskyscrapers. However, this is very unlikely given the recent growth in skyscrapers in Tokyoand removing transaction costs would increase land prices and tall buildings at the city level
as well.
[Table 5 about here.]

4.2

Mechanisms

4.2.1

Pattern of Persistence

The main results indicate lot size premia and the presence of assembly costs. To examine thenature of the assembly costs, we split the sample into the core area and the non-core area. The
core area is more aﬀected by the emergence of skyscrapers or the transition to a knowledgebased economy. This could result in two scenarios. If land assembly costs are constant, we
would ﬁnd weaker persistence in the core area, because there would be enough beneﬁts byassembling land and constructing skyscrapers to cover the assembly costs. However, if land
assembly costs are not constant and higher in the core area, for example, owing to the hold-out
problem, we might ﬁnd stronger persistence in this area.
We investigate the heterogeneous impacts between the core and non-core areas in our OLS
and RD analyses. For the OLS analysis, we split the sample into the core area and the noncore area using the Yamanote loop line and execute the regression analysis as in Table 3. Thisloop-line railway connects terminal stations in Tokyo, and the area inside the circular line is
generally recognized as the center of Tokyo. Table 6 shows the results. In columns (1) and(2), we use a 300-m buﬀer from the loop line to deﬁne the core and the non-core area. We ﬁndthat the number of lots in 1876 is lower in both samples (Panel A), but the lot size persists
only in the core area (Panel B). Accordingly, we ﬁnd positive impacts on tall buildings andland prices only in the core area (Panels C–F). This result implies that the eﬀect of local lords’
estates on buildings or land prices today comes through lot size persistence.
18

This pattern does not alter when we change the 300-m buﬀer to a 1000-m or 2000-m
buﬀer in columns (3)–(6). We also examine this pattern using the local randomization design
by estimating the eﬀect of the coastal boundary far from the core area and the non-coastalboundary close to the core area separately (Table 7).26 Again, we ﬁnd lot size persistence andeﬀects on buildings or land prices only in the core area, although both show initial eﬀects on
the number of lots.
These results imply that land assembly costs are not constant and are higher in the corearea, which explains why we observe persistence even after 150 years, unlike other studies
using rural/suburban areas (Bleakley and Ferrie, 2014; Finley et al., 2021; Smith, 2020). This
could be because cities have more heterogeneous land use or the potential beneﬁts of land
assembly itself intensiﬁes landowners’ strategic behavior in their negotiation of land assembly.
Although we cannot determine the exact reason, the result suggests that transaction costs
play a large role in the development of urban core areas.
[Table 6 about here.][Table 7 about here.]
4.2.2

Nature of Lot Size Eﬀect: Role of Skyscrapers

Before the prevalence of skyscrapers or transition to an oﬃce economy, there would have beenfewer agglomeration beneﬁts. If so, smaller lots would be preferred, and if there were no split
costs, large lots would be split.
To analyze this point, we use cadastral map and land price data before the 2010s when there
were fewer skyscrapers. We examine how local lords’ estates aﬀected lot fragmentation andland prices in 1876, 1912, 1931, 1972, and 1983 using the same set of speciﬁcations as in Table 3(OLS) and Table 4 (RD). We graphically show the results with the baseline control variables,
as shown in Figure 5.27 The point estimates show that local lords’ estates had negative eﬀects
on the number of lots before WWII (Figure 5(a)), aﬀecting land prices negatively before
WWII (Figure 5(b)). In 1972 and 1983, when high-rise buildings became more common,the eﬀect on land prices increased to around zero, and in the 2010s, when there were many26

See Figure A.4 and Figure A.5 for the graphical representations in each boundary.
See the regression results with other speciﬁcations for Table A.8 (OLS) and Table A.9 (RD). We ﬁnd
qualitatively similar results across the speciﬁcations.
27

19

skyscrapers, there was a clear positive eﬀect. This pattern is particularly prominent in the RD
analysis using the boundary in the core area (see Figure 6 for a graphical representation).28
These ﬁndings suggest two insights about lot size and land price relationships: (1) before
WWII, smaller lots were preferred, but there were substantial split costs, generating lot size
persistence compared with the main results; and (2) technological progress after WWII (thedevelopment of construction technology for high-rise buildings and the transition in production
from factories to oﬃces) changed the relationship between lot size and land prices.29[Figure 5 about here.][Figure 6 about here.]
4.2.3

Other Possible Mechanisms

The results above support the view that local lords’ estates increase lot size today, facilitateskyscraper construction by decreasing the transaction costs of assembly, and increase the land
price. We consider alternative channels to explain the link between local lords’ estates andland prices.30
First, transaction costs may be relevant in the public sector. For example, large lots
may facilitate the construction of transportation infrastructure (wider roads and proximity torailroad stations) or buildings for the public sector (hospitals, universities, and parks), which
would increase the land price. We consider these channels by controlling for average roadwidth, the share of land used as hospitals, universities, and parks, and distance to the nearest
station in 2017 and 1950 for both the OLS and the RD analyses;31 however, the main results
mostly hold.32 This suggests that these factors are not the main drivers of the key results.
Second, local lords’ estates may facilitate skyscraper construction, but not through lot
size. We consider the size of blocks (not each lot, but the area surrounded by roads) as an
28

See Figure A.6 and Figure A.7 for graphs using both boundaries and the non-core boundary only. See
Figure A.8 for aerial images of areas around the core boundary in 1970 and 2011, showing the growth inhigh-rise buildings between these periods.
29
There is anecdotal evidence that larger lots were less preferred in 17th-century Manhattan as well (Barr,
2016).
30
See “Other variables” Table 1 for the data sources of the variables used to account for alternative channels.
31
Demolished stations can have persistent eﬀects, as shown in Brooks and Lutz (2019).
32
See Table A.10 and Table A.11 for the OLS and RD results. The eﬀect on land prices is less signiﬁcant in
the RD design, but the core area shows signiﬁcant signs in Panel G of Table A.11.

20

alternative channel, because it is another constraint for constructing large buildings. Anotheralternative channel is the FAR regulation, because it can aﬀect skyscraper construction.3334
We add these variables into the main speciﬁcation, ﬁnding that the main results remain largely
unchanged.3536 Similarly, the 1876 low land price in local lords’ estates may work as an alternative channel, because it may aﬀect the dynamic path of land use. However, the coeﬃcientof local lords’ estates changes little when controlling for the land price in 1876 in the OLS and
RD analyses.37
Overall, these channels may exist in the causal chain from local lords’ estates to land prices,
but they do not fully explain the main results.
Next, we consider alternative historical shocks that might explain why we observe persistence only in the core area or the sign of the eﬀect on land price changes. We ﬁrst consider
the destruction during the WWII bombing. This might be concentrated in the non-core area
and aﬀect lot size persistence. In addition, it might aﬀect the change in the land price after
WWII. However, the results are unchanged when we control for the share of the destroyed
area.38
Second, we consider the change in land use caused by the end of WWII. Before WWII, thedescendants of local lords still used a part of the estates to live on or for military infrastructure.
There should be a signiﬁcant change in land use in those areas after WWII, which may explain
the diﬀerence between the core and non-core areas or in the sign of the eﬀect on local lords’
estates. Similarly, land owned by local lords’ descendants in 1931 might have been priced lower
33

Note that the FAR regulation may be loosened by increased demand for skyscrapers, and thus, it may justexist in the causal chain between lot size and skyscraper construction in the main results rather than as analternative mechanism.
34
Before 1919, there were no height restrictions or FAR regulations. However, in response to rapid city
growth, in 1919, the government established height regulations, and in 1961, the government switched fromheight regulations to FAR regulations.
35
FAR regulations depend on the land use zones established under urban planning laws and the width of
the roads that the buildings face under construction laws. Speciﬁcally, when road width x is equal to or morethan 12 m, the maximum FAR is equal to that set by land use zones (F ARzone ). When x is less than 12 m,
the formula min{F ARzone , x ∗ k ∗ 100} determines the maximum FAR, where k = 0.6 (k = 0.4) when theland is commercial (residential). For example, suppose that the land use zone regulation speciﬁes 500% as the
maximum ratio. If the road in front of the land is 6 m and the land is commercial, the maximum FAR is reduced
to 360%. Although there are some special cases in which k > 0.6 due to policies by local municipalities, in the
regression analysis, we calculate the road-level maximum FAR using the formula above and use its weighted
mean using the length of each road segment as the control variable.
36
See Table A.12 and Table A.13 for the OLS and RD results. The eﬀect on land prices is less signiﬁcant in
the RD design, but the core area shows signiﬁcant signs in Panel G of Table A.13.
37
See Table A.14 and Table A.15.
38
See Table A.16, Table A.17, Table A.18, and Table A.19.

21

than its market value for political reasons, as discussed in the data section. After WWII, lords
were deprived of their political privilege; they might have sold their land and their land might
have been highly valued. To address these concerns, we control for the descendants’ estatesor their other land and military infrastructure in the regression analysis. However, the results
are robust to controlling for these factors.39

4.3

Eﬀect on Firm Productivity

To further examine the positive eﬀect of local lords’ estates on land prices through agglomeration beneﬁts, we analyze the impact on ﬁrm productivity using microdata. There are twochannels of how local lords’ estates aﬀect local-level TFP through high-rise buildings: theselection channel in which competition becomes tougher and less productive ﬁrms exit andthe agglomeration beneﬁts channel in which ﬁrms increase their TFP by knowledge spillovers,a thick labor market, sharing common sources, and so on. If the selection channel is the main
driver of the eﬀect on land prices, it does not represent a productivity gain for ﬁrms.
To disentangle these channels, we examine the distribution of ﬁrm productivity in each cell
(Combes et al., 2012). The selection channel generates a cutoﬀ in the lower tail, because theleast productive ﬁrm exits. Meanwhile, the agglomeration beneﬁts channel shifts the whole
distribution to the right and/or the upper tail becomes thicker when productive ﬁrms canenjoy the agglomeration beneﬁts more.
Figure 7(a) shows the distribution of ﬁrm productivity in 2017 proxied by revenue per
worker in cells whose local lords’ estates share is zero and one (the solid line and dash line,
respectively). We ﬁnd that the lower tail does not show a signiﬁcant diﬀerence, suggesting avery weak cutoﬀ channel, but the upper tail becomes thicker in the local lords’ estates zone.
We ﬁnd a very similar pattern when we compare ﬁrms in the local lords’ estates zone and theother zone using ﬁrms close to the boundary, as in the local randomization design (Figure 7(b)).
Quantile regression analysis with the inclusion of the other cells in the sample and controllingfor other variables (controls in the main analysis and industry ﬁxed eﬀects) conﬁrm thispattern, with a larger eﬀect in the upper tail (shown in Figure 8(a) and Figure 8(b)). These
results imply that local lords estates contribute to productivity gains for ﬁrms mainly through39

See Table A.20, Table A.21, Table A.22, and Table A.23.

22

agglomeration beneﬁts.40
For further investigation of the selection channel, we examine ﬁrms’ moving, exit, andentry using the datasets in 1993 and 2017. We ﬁnd no evidence that less productive ﬁrms
disappear from the local lords’ estates zone through these channels.41 These two sets of results
indicate that the presence of local lords’ estates contributes to ﬁrm productivity through theagglomeration beneﬁts channel, which is a pure gain for ﬁrms.
The comparison between 1993 and 2017 is also useful for examining the role of tall buildings
in generating the lot size premia. Although the results in Figure 5 show a stark diﬀerence inthe eﬀect of lot size on land prices between the periods, there may be changes in those 150
years other than the emergence of skyscrapers or the knowledge-based economy to explain
the diﬀerence. To investigate the role of tall buildings more explicitly, we focus on more
recent changes in the height of buildings from 1993.4243 Using the same speciﬁcation as
Figure 8, we ﬁnd that the eﬀects are smaller in 1993 (shown as triangles in Figure 9(a)) thanin 2017. In addition, once we control for the average number of stories, the eﬀect of local lords’
estates attenuates and the diﬀerence between 2017 and 1993 becomes smaller (Figure 9(b)).
We ﬁnd a similar pattern when we employ the local randomization design (Figure 9(c) and
Figure 9(d)).44 Although the analyses are not free from the bad control problem, this provides
suggestive evidence that local lords’ estates contribute to a productivity gain for ﬁrms through40

Among the alternative channels discussed in the previous section, public amenities may aﬀect ﬁrm productivity, but not through skyscrapers. We include these variables as additional controls and conﬁrm that themain results hold. See Table A.24 and Table A.25.
41
Panel A in Table A.26 shows that more productive ﬁrms do not signiﬁcantly move into the area with ahigh local lords’ estates share. Panel B shows that new ﬁrms are less productive, but this pattern does not
become severe in the high local lords’ estates share area. Similarly, Panel C shows that less productive ﬁrms
in 1993 are more likely to exit from our sample area, but this pattern does not become severe in the high local
lords’ estates share area. These results suggest that the selection channel does not operate in these margins.
As a counterpart of our local randomization design, Table A.27 analyzes the move from or to the local lords’
estates zone and entry and exit from our sample area for local randomization. The results are similar to thoseof Table A.26, providing little evidence of the selection channel.
42
The eﬀect of local lords’ estates on skyscrapers was increasing in these 25 years. See Table A.28 andTable A.29.
43
We also split the sample into a business zone (more than half of the land is used for the purpose ofbusiness) and a residential zone (similarly deﬁned) to investigate heterogeneous eﬀects. This classiﬁcation is
endogenous, and thus, the results should be interpreted with caution. Table A.30 shows that in both zones,
local lords’ estates prevent lot fragmentation and encourage large-scale development (Panels A–C), but promote
the construction of high-rise buildings only in the business zone (Panels D–E). For land prices, both zones show
the positive impacts of local lords’ estates and the eﬀect in the business zone is higher in the core area, although
the signiﬁcance varies owing to the diﬀerent levels of standard errors. This suggests that the presence of local
lords’ estates may aﬀect land prices in the residential zone through diﬀerent channels such as higher amenities
arising from less density and the presence of parks.
44
See Table A.31 and Table A.32 for the corresponding regression tables.

23

agglomeration beneﬁts in high-rise buildings.
[Figure 7 about here.][Figure 8 about here.][Figure 9 about here.]5

Policy Implications

Policymakers recognize that lot fragmentation is an important obstacle in urban development(Nelson and Lang, 2007), but the long-run eﬀects of lot fragmentation are not well understood.
This is particularly relevant to today’s growing cities in developing countries (Bryan et al.,2020), which often have poor urban slums in core areas. The provision of property rights
in urban slums to enhance economic development has been discussed and implemented inpractice. Various studies analyze whether such entitlements increase investments in housing(Field, 2005; Field and Torero, 2006; Henderson et al., 2016). Our results imply that entitlingproperty rights may have unintended consequences for productivity through lot fragmentationin rapidly growing cities when such areas need to be transformed into business zones withhigh-rise buildings, echoing the view in Glaeser (2021).45
To obtain a more policy-relevant parameter, we estimate the impact of additional lots in
1872 on the land prices of today using local lords’ estates as an instrument. Note that the
map in 1872 covers only the relatively central area, which results in a bigger eﬀect than the
average eﬀect, as analyzed in Table 6. We ﬁnd that additional lots in 1872 decrease the land
price in 2012 by 1.3–3.9%.46 Because the standard deviation of the number of lots is about
10, this suggests a substantial negative impact of initial lot fragmentation on the land price.
Although this parameter is heterogeneous across cities, it is a benchmark for policymakers in
rapidly growing cities in developing countries.

45

Similarly, Harari and Wong (2019) and Michaels et al. (2021) show that upgrading amenities in slums may
result in lower land prices and shorter buildings by increasing formalization costs.
46
See Table A.33 for the results, including the results for the other main outcome variables.

24

6Conclusion

In this study, we investigate whether transaction costs in the urban land market generate lot
size persistence and hinder eﬃcient land use. We construct a 100 m*100 m-cell-level dataset
spanning 150 years and use a plausibly exogenous release of large lots (local lords’ estates)to the private market in 1868. Using OLS and local randomization designs, we ﬁnd thatcells used as local lords’ estates formerly have larger lots even after 150 years. This lot size
persistence is stronger in the core area, implying higher transaction costs there. We also ﬁndthat previous local lords’ estates generate agglomeration beneﬁts in the 2010s: there are more
skyscrapers, higher land prices, and productive ﬁrms. We also conﬁrm that the eﬀect on ﬁrm
productivity does not come from the moving, exit, or entry of ﬁrms. Meanwhile, before WWII,
former local lords’ estates had larger lots than other areas but lower land prices. This opposite
result on the land price from the 2010s means that previous local lords’ estates were too large
for optimal land use and discounted due to land split costs. These ﬁndings imply that theprediction of the Coase theorem without transaction costs is not relevant in the urban landmarket, particularly in the CBD, and initial lot sizes have substantial impacts on economic
activities even after 150 years. City planners in developing countries should take account ofthese results when entitling property rights to fragmented areas such as slums.

25References
ABP Company, ed., Edo Meiji Tokyo Kasane Chizu (Maps of Tokyo from the Edo andMeiji Periods), Tokyo, Japan: ABP Company, 2009.
Ahlfeldt, Gabriel M. and Daniel P. McMillen, “Tall Buildings and Land Values: Heightand Construction Cost Elasticities in Chicago, 1870-2010,” The Review of Economics andStatistics, December 2018, 100 (5), 861–875.
and Jason Barr, “The Economics of Skyscrapers: A Synthesis,” Journal of Urban Economics, May 2022, 129, 103419.
, Stephen J. Redding, Daniel M. Sturm, and Nikolaus Wolf, “The Economics ofDensity: Evidence From the Berlin Wall,” Econometrica, 2015, 83 (6), 2127–2189.
Ambrus, Attila, Erica Field, and Robert Gonzalez, “Loss in the Time of Cholera: LongRun Impact of a Disease Epidemic on the Urban Landscape,” American Economic Review,
February 2020, 110 (2), 475–525.
Barr, Jason M., Building the skyline: the birth and growth of Manhattan’s skyscrapers, New
York, NY: Oxford University Press, 2016.
Barr, Jason, Troy Tassier, and Rossen Trendaﬁlov, “Depth to Bedrock and the Formation of the Manhattan Skyline, 1890–1915,” The Journal of Economic History, December
2011, 71 (4), 1060–1077.
Baruah, Neeraj G., J. Vernon Henderson, and Cong Peng, “Colonial legacies: shaping
African cities,” November 2017.
Bleakley, Hoyt and Jeﬀrey Lin, “Portage and Path Dependence,” Quarterly Journal of
Economics, April 2012, pp. 1–58.
and Joseph Ferrie, “Land openings on the Georgia frontier and the coase theorem in the
short-and long-run,” Mimeo 2014.
Brooks, Leah and Byron Lutz, “From Today’s City to Tomorrow’s City: An EmpiricalInvestigation of Urban Land Assembly,” American Economic Journal: Economic Policy,
August 2016, 8 (3), 69–105.
26

and

, “Vestiges of Transit: Urban Persistence at a Microscale,” The Review of Economics

and Statistics, July 2019, 101 (3), 385–399.
Brownstone, David and Arthur De Vany, “Zoning, Returns to Scale, and the Value of
Undeveloped Land,” The Review of Economics and Statistics, November 1991, 73 (4), 699.
Bryan, Gharad, Edward Glaeser, and Nick Tsivanidis, “Cities in the Developing
World,” Annual Review of Economics, August 2020, 12 (1), 273–297.
Cattaneo, Matias D., Nicolás Idrobo, and Rocío Titiunik, A Practical Introduction to
Regression Discontinuity Designs: Foundations, 1 ed., Cambridge University Press, November 2019.
Chen, Stefanos, “New Development Returns, Along With New Holdouts,” September 2021.
Coase, R.H., “The Problem of Social Cost,” The Journal of Law & Economics, October 1960,
3, 1–44.
Combes, Pierre-Philippe, Gilles Duranton, Laurent Gobillon, Diego Puga, andSébastien Roux, “The Productivity Advantages of Large Cities: Distinguishing Agglomeration From Firm Selection,” Econometrica, 2012, 80 (6), 2543–2594.
Davis, Donald R and David E Weinstein, “Bones, Bombs, and Break Points: The Geography of Economic Activity,” American Economic Review, December 2002, 92 (5), 1269–1289.
Dericks, Gerard H and Hans R A Koster, “The Billion Pound Drop: The Blitz andAgglomeration Economics in London,” Journal of Economic Geography, November 2021, 21(6), 869–897.
Field, E. and M. Torero, “Do Property Titles Increase Credit Access among the Urban
Poor? Evidence from a Nationwide Titling Program,” Mimeo 2006.
Field, Erica, “Property Rights and Investment in Urban Slums,” Journal of the European
Economic Association, 2005, 3 (2-3), 279–290.
Finley, Theresa, Raphaël Franck, and Noel D Johnson, “The Eﬀects of Land Redistribution: Evidence from the French Revolution,” Journal of Law and Economics,, 2021, 64,
35.
27

Fukushima, Masao, Chiso Kaisei no Kenkyu (Research on Land Tax Reform), Tokyo, Japan:
Yuhikaku, 1962.
Gandhi, Sahil, Vaidehi Tandel, Alexander Tabarrok, and Shamika Ravi, “Too slow
for the urban march: Litigations and the real estate market in Mumbai, India,” Journal of
Urban Economics, May 2021, 123, 103330.
Gechter, Michael and Nick Tsivanidis, “The Welfare Consequences of Formalizing Developing Country Cities: Evidence from the Mumbai Mills Redevelopment,” mimeo 2018.
Glaeser, Edward L., Triumph of the city: how our greatest invention makes us richer,smarter, greener, healthier, and happier, New York:

Penguin Press, 2011.

OCLC:

ocn650211168.
, “What can developing cities today learn from the urban past?,” Regional Science and Urban
Economics, June 2021, p. 103698.
Grossman, Zachary, Jonathan Pincus, Perry Shapiro, and Duygu Yengin, “Secondbest mechanisms for land assembly and hold-out problems,” Journal of Public Economics,
July 2019, 175, 1–16.
Hanlon, W. Walker, “Temporary Shocks and Persistent Eﬀects in Urban Economies: Evidence from British Cities after the U.S. Civil War,” The Review of Economics and Statistics,
2017, 99 (1), 67–79.
Hanlon, W.Walker and Stephan Heblich, “History and Urban Economics,” Regional
Science and Urban Economics, November 2021, p. 103751.
Harari, Mariaﬂavia, “Cities in Bad Shape: Urban Geometry in India,” American Economic
Review, August 2020, 110 (8), 2377–2421.
and Maisy Wong, “Slum Upgrading and Long-run Urban Development: Evidence fromIndonesia,” Mimeo 2019.
Heblich, Stephan, Stephen J Redding, and Daniel M Sturm, “The Making of the Modern Metropolis: Evidence from London,” The Quarterly Journal of Economics, November
2020, 135 (4), 2059–2133.
28

Henderson, J. Vernon, Anthony J. Venables, Tanner Regan, and Ilia Samsonov,“Building functional cities,” Science, May 2016, 352 (6288), 946–947.
Hornbeck, Richard and Daniel Keniston, “Creative Destruction: Barriers to Urban
Growth and the Great Boston Fire of 1872,” American Economic Review, June 2017, 107(6), 1365–1398.
Ichihara, Masahide, ed., “Meiji Tokyo Zenzu,” 1876.
Jedwab, Remi, Jason Barr, and Jan Brueckner, “Cities without Skylines: Worldwide
Building-Height Gaps and Their Implications,” CESifo Working Paper Series 8511 August
2020.
Kageyama, Muneyasu, Masanori Tomatsu, and Yoshitomo Iyama, eds, Edo Kiriezu(Illustration Map of Tokyo), Tokyo, Japan: Owariya Seishici, 1849-1862.
Kawasaki, Husagoro, Meiji Shonen no Bukechi Shori Mondai (Issues of Treating Samurai Class’s Land in Meiji 1), Vol. 13 of Toshi Kiyo, Tokyo, Japan: Tokyo Metropolitan
Government, 1965.
Kazoku Kaikan, “Kazoku Meibo (List of Noblemen),” 1931.
Kelly, Morgan, “The Standard Errors of Persistence,” Mimeo 2019.
Kirk, Mimi, “The World’s Most Stubborn Real Estate Holdouts,” April 2017.
Kito, Hiroshi, “Edo Tokyo no Jinko Hatten: Meiji Ishin no Mae to Ato,” Jochi Keizai Ronshi,
1989, 34, 48–69.
Leonard, Bryan and Dominic P Parker, “Fragmented Ownership and Natural Resource
Use: Evidence from the Bakken,” The Economic Journal, April 2021, 131 (635), 1215–1249.
Libecap, Gary D. and Dean Lueck, “The Demarcation of Land and the Role of Coordinating Property Institutions,” Journal of Political Economy, June 2011, 119 (3), 426–467.
Liu, Crocker H., Stuart S. Rosenthal, and William C. Strange, “Building Specialization, Anchor Tenants and Agglomeration Economies,” mimeo 2017.

29

Matsuyama, Megumi, Edo Tokyo no Toshishi: Kindai Ikoki no Toshi Kenchiku Shakai(Urban History of Edo/Tokyo), Tokyo, Japan: University of Tokyo Press, 2014.
Miceli, Thomas J. and C. F. Sirmans, “The holdout problem, urban sprawl, and eminent
domain,” Journal of Housing Economics, November 2007, 16 (3), 309–319.
Michaels, Guy, Dzhamilya Nigmatulina, Ferdinand Rauch, Tanner Regan, Neeraj
Baruah, and Amanda Dahlstrand, “Planning Ahead for Better Neighborhoods: LongRun Evidence from Tanzania,” Journal of Political Economy, July 2021, 129 (7), 2112–2156.
Microsoft, “Microsoft Building Footprint Data,” 2017.
Ministry of Land, Infrastructure, Transport and Tourism, “National Land Numerical
Information download service,” 2014.
Mori, Minoru, Hiruzu Chosen suru Toshi Asahi Shinsho, Tokyo, Japan: Asahi Shimbun
Shuppan, 2009.
Nakai, Jotaro, ed., “Senko Hukyu Tokyo Chisho Meisai,” 1880.
Nelson, Arthur C and Robert Lang E., “The Next 100 Million,” Planning, 2007, 73 (1),
4–6.
Nishikawa, Mutsumichi and Mitsuho Nishikawa, Dainihon Kaisei Tokyo Zenzu 1880.
O’Grady, Trevor, “Spatial Institutions in Urban Economies: How City Grids Aﬀect Density
and Development,” mimeo January 2014.
Olson, Mancur, The Logic of Collective Action: Public Goods and the Theory of Groups
number 124. In ‘Harvard economic studies.’, 21. printing ed., Cambridge, Mass.: Harvard
Univ. Press, 2003.
Oster, Emily, “Unobservable Selection and Coeﬃcient Stability: Theory and Evidence,”
Journal of Business & Economic Statistics, April 2019, 37 (2), 187–204.
Owens, Raymond, Esteban Rossi-Hansberg, and Pierre-Daniel Sarte, “Rethinking
Detroit,” American Economic Journal: Economic Policy, May 2020, 12 (2), 258–305.

30

Redding, Stephen J. and Daniel M. Sturm, “Estimating Neighborhood Eﬀects: Evidence
from War-time Destruction in London,” Mimeo 2016.
Research Center for Property Assessment System, “Kotei Shisanzei Rosenka Tou Data,”
2012.
Saiz, Albert, “The geographic determinants of housing supply,” The Quarterly Journal of
Economics, 2010, 125 (3), 1253–1296.
Sakai, Miiko, “Moto Kazoku Tachi no Sengo Shi (Noblemen After WWII),” 2016.
Seizusha, Uchiyama Mokei, ed., Tokyo Shi Chisekizu (Tokyo City Cadastral Maps), Tokyo,
Japan: Uchiyama Mokei Seizusha, 1931-1935.
Shobumsha, “Mapple 10000,” September 2018.
Smith, Cory, “Land Concentration and Long-Run Development in the Frontier UnitedStates,” Mimeo 2020.
Suzuki, Rio, Edo ha Koushite Tukurareta (Edo Was Constructed in This Way), Tokyo, Japan:
Chikuma Shobo, 2000.
Tabuchi, Takatoshi, “Quantity Premia in Real Property Markets,” Land Economics, 1996,
72 (2), 206–217.
TDi and Inc, “SoMap,” 2017.
Tokyo Hu, Tokyo Hu Tokei Sho Meiji-15 nen (Tokyo Prefecture Statistics in 1885) 1887.
, Tokyo Hu Tokei Sho Taisho-8 nen (Tokyo Prefecture Statistics in 1920) 1923.
Tokyo Metropolitan Government, “Tochi Riyou Genkyou Chousa (Land Use Survey)
1986,” 1986.
, “Tochi Riyou Genkyou Chousa (Land Use Survey) 1991,” 1991.
, “Tochi Riyou Genkyou Chousa (Land Use Survey) 1996,” 1996.
, “Tochi Riyou Genkyou Chousa (Land Use Survey) 2001,” 2001.

31

, “Tochi Riyou Genkyou Chousa (Land Use Survey) 2006,” 2006.
, “Tochi Riyou Genkyou Chousa (Land Use Survey) 2011,” 2011.
, “Community Earthquake Risk Assessment Study,” 2018.
Tokyo Shiku Chosakai, “Tokyo Shi Oyobi Setuzoku Gunbu Chiseki Chizu / Daicho,” 1912.
Tokyo Shobo Cho Kikaku Chouseibu Kikakuka, Tokyo Shobo Cho Tokei Sho (Tokyo
Fire Department Statistics) 1990–2010.
Tokyo To, Tokyo To Tokei Nenkan Heisei-27 nen (Tokyo Metropolitan Oﬃce Statistics in2015) 2015.
Tokyo-to Takuchi Tatemono Tohirikigyo Kyokai, “Tokyo-to Chikazu (Tokyo Land Price
Map): Showa 47 Nendo Ban,” 1972.
, “Tokyo-to Chikazu (Tokyo Land Price Map): Showa 58 Nendo Ban,” 1983.
Ueno, Rokuo, “Teito Kimbo Zu,” 1945.
Washizaki, Shuntaro, “Meijiki Tokyo no Hudousan Chintai Keiei ni okeru Mitsubishi no
Yakuwari to Igi (The Role and Signiﬁcance of Mitsubishi in Rental Property Market inTokyo during the Meiji period),” Mitsubishi Shiryokan Ronju, 2015, 16, 163–172.
White, James R., “Large lot zoning and subdivision costs: A test,” Journal of Urban Economics, May 1988, 23 (3), 370–384.
Yomiuri Shimbun Hanbaikyoku, ed., Edo Kiriezu (Illustration Map of Tokyo), Tokyo,
Japan: Yomiuri Shimbun, 1990-1991.

32

Figure 1: Zoning in the Initially Developed Area

B
o
u
n
d
a
ry
(In
itia
lC
o
a
stlin
e
)
!

!
!

!

E
d
oC
a
stle

!

!

S
u
m
id
a
R
ive
r
!

O
u
te
r
M
o
a
tA
re
a

0 .2
5 .5

1K
ilo
m
e
te
rs

(a)

B
o
u
n
d
a
ry
(In
itia
lC
o
a
stlin
e
)
!

!
!

!

E
d
oC
a
stle

!

!

S
u
m
id
a
R
ive
r
!

O
u
te
r
M
o
a
tA
re
a

0 .2
5 .5

1K
ilo
m
e
te
rs

(b)
Notes: Polygons with red borders are local lords’ estates. The U-shaped line in both ﬁgures is the boundary
between the local lords’ estate zone (the outer side) and the commoners’ zone (the inner side). The dash-dot
part is the initial boundary between the zones. The solid and dash parts are the initial coastline. The solidpart became part of the boundary after the second reclamation. The gray area in the right ﬁgure shows a
250-m buﬀer, which we use for the local randomization regression analysis. Another line in the right ﬁgurefrom south to north shows the overground railroad loop line (Yamanote line). In the right ﬁgure, we overlay
high-rise buildings in 2011, indicated by black (more than or equal to 30 stories) and gray (15–29 stories)
rectangles.

33

Figure 2: Distribution of Local Lords’ Estates

E
d
o
C
a
stle

0 .5 1

2K
ilo
m
e
te
rs

Notes: This map covers the whole of Edo’s city area (Sumibiki sen-nai). Red areas represent local lords’
estates. The gray-shaded area shows the area within the loop (Yamanote) line.

34

Figure 3: Illustration from a Corner of the Tokyo CBD(a) Original Data Source(b) Local Lords’ Estates (Red-hatched Area)

B(c) (b) + Lot in 2008–2011

C(d) (b) + Tall Buildings: Black (Gray) Buildings Have More Than 30 (15) Stories(e) Google Earth’s Aerial Image Suggesting High Land Assembly Costs: Labels(A, B, and C) correspond to buildings in (d).
Notes: These ﬁgures show the raw data and aerial images around Tamachi station, a station in the CBD. Panel(a) is an reprinted map of one of the original data sources (Yomiuri Shimbun Hanbaikyoku, ed, 1990-1991).
35
Panel (d) is the aerial image as of November 2021 taken from Google Earth.

Figure 4: Distribution along the Zoning Boundary
Mean of Altitude

-500

0

Sample average within bin

500

1000

-1000

Polynomial fit of order 4

-500

0

Sample average within bin

500

(b)

S.D. of Altitude

Number of Lots in 2008-2011

0

20

40

.5

60

1

80

1.5

1000

Polynomial fit of order 4

(a)
100

-1000

2

0

3

.2

4

.4

5

.6

6

7

.8

Local Lords' Estates Share

-1000

-500

0

Sample average within bin

500

1000

-500

0

Sample average within bin

500

1000

Polynomial fit of order 4(d)Number of Buildings >= 30 Stories in 2011

Log Land Price in 2012

.1

13.5

14

.2

.3

14.5

.4

15

(c)

13

0

-1000

-1000

Polynomial fit of order 4

-500

0

Sample average within bin

500

1000

Polynomial fit of order 4

-1000

-500

0

Sample average within bin

(e)

500

1000

Polynomial fit of order 4(f)Notes: We use all cells within 1 km of the boundary in Figure 1(b) excluding cells within 50 m of the boundary
to avoid mechanical attenuation eﬀects. The x-axis is the distance from the boundary, which is shown as thesolid line and dash-dot line in Figure 1(b), taking a positive and negative value in the local lords’ estate zone
and the commoners’ zone, respectively. The points show the average of each outcome variable within each bin.
The number of bins is chosen using the mimicking variance evenly spaced method using spacing estimators.
The lines show the fourth-order polynomial ﬁt for each zone.

36

-2

-1.5

-1

-.5

0

Figure 5: Time-varying Eﬀects of Local Lords’ Estates

1876

1912

1935

1972 1983

2010s

Year
OLS with 95 % CI

RD with 95 % CI

-1.5

-1

-.5

0

.5

1

(a) The Eﬀect of Local Lords’ Estates on the Number of Lots

1876

1912

1935

1972 1983

2010s

Year
OLS with 95 % CI

RD with 95 % CI(b) The Eﬀect of Local Lords’ Estates on Land Prices
Notes: The circles show the point estimates using OLS with controlling for Distance from the Center (Castle),
Mean of altitude, and S.D. of Altitude. The diamonds show the point estimates using local randomization
while controlling for Distance from the Center (Castle) and West of the Yamanote line. Figure 5(a) shows theeﬀect of local lords’ estates on the number of lots after normalization, and Figure 5(b) shows the eﬀect of local
lords’ estates on the log of land price.

37

Figure 6: Number of Lots and Land Prices along Zoning Boundary in the Core Area from1912 to the 2010s
Number of Lots in 2008-2011

-1000

-500

0

Sample average within bin

500

1000

0

5

5

10

20

10

15

15

40

20

20

60

25

25

80

Number of Lots in 1931
30

Number of Lots in 1912

-1000

Polynomial fit of order 4

-500

0

Sample average within bin

500

1000

-1000

Polynomial fit of order 4

-500

0

Sample average within bin

500

1000

Polynomial fit of order 4(b) Number of Lots in 1931(c) Number of Lots in the 2010s

Log Land Price in 1912

Log Land Price in 1931

Log Land Price in 1972
15

3

3.5

13.5

3.5

4

14

4

4.5

4.5

14.5

5

5

5.5

5.5

(a) Number of Lots in 1912

-1000

-500
Sample average within bin

0

500

1000

-1000

Polynomial fit of order 4

-500

0

Sample average within bin(d) Log Land Price in 1912

500

1000

-1000

Polynomial fit of order 4(e) Log Land Price in 1931

0

500

1000

Polynomial fit of order 4(f) Log Land Price in 1972

14

13.5

14

14.5

14.5

15

15

15.5

15.5

16

Log Land Price in 201216

Log Land Price in 1983

-500
Sample average within bin

-1000

-500
Sample average within bin

0

500

1000

-1000

Polynomial fit of order 4(g) Log Land Price in 1983

-500
Sample average within bin

0

500

1000

Polynomial fit of order 4(h) Log Land Price in the 2010s

Notes: We use all cells within 1 km of the boundary in Figure 1 excluding cells within 50 m of the boundary
to avoid mechanical attenuation eﬀects. The x-axis is the distance from the boundary, which is represented
by the dash-dot line in Figure 1, taking a positive and negative value in the local lords’ estate zone and thecommoners’ zone, respectively. The points show the average of each outcome variable within each bin. The
number of bins is chosen using the mimicking variance evenly spaced method using spacing estimators. Thelines show the fourth-order polynomial ﬁt for each zone.

38

.4
.3
.2
.1
0

0

.1

.2

.3

.4

Figure 7: Firm Productivity Distribution in the Local Lords’ Estate Area and Other Areas

02

4
Log (Revenue / Worker)

Local Lords' Estates = 0

6

8

0

Local Lords' Estates = 1

2

4
Log (Revenue / Worker)

Commoner's Zone(a) OLS

68

Local Lords' Estates Zone(b) Local Randomization

Notes: Figure 7(a) shows the distribution of ﬁrm productivity in 2017 proxied by revenue per worker in cells
whose local lords’ estates share is zero and one (solid line and dash line, respectively). Similarly, Figure 7(b)shows the distribution of ﬁrm productivity in 2017 proxied by revenue per worker in the local lords’ estateszone and the other zone within the sample, respectively, for the local randomization design.

39

-.05

-.05

0

0

.05

.1

.05

.15

.1

.2

Figure 8: Quantile Regression Results on Firm Productivity

.1

.25

.5
Percentile
95% CI in 2017

.75

.9

.1

Effect in 2017.25

.5
Percentile
95% CI in 2017(a) OLS with Controls

.75

.9

Effect in 2017(b) Local Randomization with Controls

Notes: (a) shows the coeﬃcients when regressing the log of revenue per worker on local lords’ estates share,
conditional on Distance from the Center (Castle), Mean of altitude, S.D. of Altitude, and industry ﬁxed eﬀects.
Diamonds (Triangles) show the results using the sample in 2017 (1993). (b) shows the coeﬃcients whenregressing the log of revenue per worker on the local lords’ estates zone variable, conditional on Distance fromthe Center (Castle), Mean of altitude, S.D. of Altitude, West of the Yamanote line, Earthquake Risk, andindustry ﬁxed eﬀects. See Panel B in Table A.31 and Table A.32 for the corresponding regression tables.

40

.1
.05
0
-.05

-.05

0

.05

.1

Figure 9: Tall Buildings and Firm Productivity

.1

.25

.5
Percentile
95% CI in 2017
95% CI in 1993

.75

.9

.1

.25

Effect in 2017
Effect in 1993

.5
Percentile
95% CI in 2017
95% CI in 1993

.9

Effect in 2017
Effect in 1993(b) (a) + Controlling for Stories

-.15

-.1

-.15 -.1 -.05

-.05

0

0

.05

.05

.1

.1

.15

.15

.2

.2

(a) OLS with Controls

.75

.1

.25

.5
Percentile
95% CI in 2017
95% CI in 1993

.75

.9

.1

Effect in 2017
Effect in 1993

.25

.5
Percentile
95% CI in 2017
95% CI in 1993(c) Local Randomization with Controls

.75

.9

Effect in 2017
Effect in 1993(d) (c) + Controlling for Stories

Notes: (a) shows the coeﬃcients when regressing the log of revenue per worker on local lords’ estates share,
conditional on Distance from the Center (Castle), Mean of altitude, S.D. of Altitude, and industry ﬁxed eﬀects.
Diamonds (triangles) show the results using the sample in 2017 (1993). (b) shows the results when we add themean of stories (aboveground) as an additional control variable. (c) shows the coeﬃcients when regressing thelog of revenue per worker on the local lords’ estates zone variable, conditional on Distance from the Center(Castle), Mean of altitude, S.D. of Altitude, West of the Yamanote line, Earthquake Risk, and industry ﬁxed
eﬀects. (d) shows the results when we add the mean of stories (aboveground) as an additional control variable.

41

Table 1: Deﬁnition of Variables and Their Data Sources
Variable
Main variables
Local Lords’ Estates Share
Number of Lots

Number of Buildings
Stories

Log Land Price in 2012
Log Land Price in 1876, 1912,
and 1931
Log Land Price in 1972 and1983
Other variables
Average Road Width
Hospital, University, and Park
Share
Distance to Station in (Year)Deﬁnition

Data Source

The share of areas owned by local lords in the 1850s.
The number of lots located (at least a part of the lot) in a cell.

ABP Company, ed (2009)
Nishikawa and Nishikawa (1880), Ichihara, ed(1876), Tokyo Shiku Chosakai (1912), Seizusha,ed (1931-1935), TDi and Inc (2017)
Tokyo Metropolitan Government (2011)

The number of buildings located (at least a part of thebuilding) in a cell.
The average number of buildings’ stories in a cell.
(aboveground) counts only the stories aboveground, whereas(including underground) includes the stories underground.
The average of the road-level price factor by weighting thelength of each road.
The area-weighted average of the lot land price.
The average of land prices.

Research Center for Property Assessment
System (2012)
Nakai, ed (1880), Tokyo Shiku Chosakai (1912),
Seizusha, ed (1931-1935)
Tokyo-to Takuchi Tatemono Tohirikigyo Kyokai(1972) and Tokyo-to Takuchi Tatemono
Tohirikigyo Kyokai (1983)

The length-weighted average width of roads in a cell.
The share of areas used for these purposes in each cell.

Shobumsha (2018)
Tokyo Metropolitan Government (2011).

The distance in meters to the nearest station in each year.

Ministry of Land, Infrastructure, Transport andTourism (2014)
Tokyo Metropolitan Government (2011) and
Shobumsha (2018)
Tokyo Metropolitan Government (2011)
Ueno (1945)

FAR Regulations

The average of maximum ﬂoor-area ratio.

Block Area
WWII Destruction Share

The average of blocks’ (areas surrounded by roads)s area.
The share of area destroyed during WWII air raids on Tokyoin each cell.
The share of area owned by the descendants of local lords andused as their estate. in each cell
The share of area owned by the descendants of local lords notas their estate/used as military infrastructure in each cell.
This includes latitude, longitude, their squared terms, andtheir interaction term.

Remaining Estates Share in
1931
Other Lords’ Land / Military
Use Share in 1931
Lon and Lat controls

Tokyo Metropolitan Government (2011)

42

Kazoku Kaikan (1931)
Seizusha, ed (1931-1935)
Centroid of each cell

Table 2: Descriptive Statistics
Observation

Mean

SD

Min

Max

Panel A: Cell-level Variables
Local Lords’ Estates Share
Number of Lots in 1872
Number of Lots in 1931
Number of Lots in 1912
Number of Lots in 2008–2011
Land Price in 1876 (Thousand Yen)
Land Price in 1912 (Thousand Yen)
Land Price in 1931 (Thousand Yen)
Land Rental Price in 1931 (Thousand Yen)
Log Land Price in 1972 (Thousand Yen)
Log Land Price in 1983 (Thousand Yen)
Land Price in 2012 (Thousand Yen)
Stories (aboveground) in 2011
Number of Buildings in 2011
Number of Buildings >= 30 Stories in 2011

9761
5530
7830
8133
9101
3644
7122
4711
7024
6071
3276
8971
9542
9542
9542

0.219
12.15
17.21
14.38
55.99
0.006490.04850.03340.0391573.2
1512.3
908.1
5.764
35.17
0.0380

0.364
10.02
11.65
10.11
36.57
0.008900.06220.04410.0369530.61492.11516.5
4.687
25.22
0.214

0
1
1
1
1
9.99e-09
5.69e-08
0.000000302
0.000000711
91.30
249.7
98
0
1
0

1
80
129
86
202
0.105
0.612
0.424
0.360
6640.022650.2
16658.2
56
136
3

Panel B: Firm-level Variables
Log Revenue per Worker in 2017
Log Revenue per Worker in 1993

80473
85313

3.363
3.579

1.167
1.085

-3.466-3.020

12.48
12.04

43

Table 3: Main Results
(1)

(2)

(3)

Panel A: Number of Lots in 1872 (N: 5530)
Local Lords’ Estates Share-11.45∗∗∗ -11.23∗∗∗ -11.30∗∗∗(0.682)(0.660)(0.690)Panel B: Number of Lots in 2008-2011 (N: 9101)
Local Lords’ Estates Share-23.29∗∗∗ -21.53∗∗∗ -19.41∗∗∗(2.686)(2.648)(2.692)
Panel C: Number of Buildings in 2011 (N: 9542)
Local Lords’ Estates Share-13.06∗∗∗ -11.61∗∗∗ -10.39∗∗∗(1.675)(1.654)(1.651)Panel D: Stories (aboveground) in 2011 (N: 9542)
Local Lords’ Estates Share0.802∗∗
0.884∗∗
0.913∗∗∗(0.352)(0.353)(0.319)Panel E: Number of Buildings >= 30 Stories in 2011 (N: 9542)
Local Lords’ Estates Share
0.0394∗∗∗ 0.0385∗∗ 0.0382∗∗∗(0.0147) (0.0150) (0.0146)
Panel F: Log Land Price in 2012 (N: 8971)
Local Lords’ Estates Share
0.338∗∗∗
0.321∗∗∗
0.218∗∗∗(0.0696) (0.0728) (0.0607)
Distance from the Center (Castle)
Yes
Yes
Yes
Mean of Altitude
No
Yes
Yes
S.D. of Altitude
No
Yes
Yes
Lon and Lat Controls
No
No
Yes
Earthquake Risk
No
No
No

(4)

-11.35∗∗∗(0.691)-19.49∗∗∗(2.695)-10.36∗∗∗(1.652)0.845∗∗∗(0.305)0.0360∗∗∗(0.0139)0.200∗∗∗(0.0567)
Yes
Yes
Yes
Yes
Yes

Standard errors are shown in parentheses. We allow a within-300 m correlationin the error terms. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
See Table 1 for the deﬁnitions of variables.

44

Table 4: Local Randomization Design
(1)

(2)

(3)

Panel I: Local Lords’ Estates Share (N: 351)
Local Lords’ Estates Zone
0.542∗∗∗ 0.538∗∗∗ 0.468∗∗∗(0.0803) (0.0755) (0.0822)
Panel A: Number of Lots in 1872 (N: 350)
Local Lords’ Estates Zone
-12.14∗∗∗ -11.97∗∗∗ -10.79∗∗∗(1.896)(1.777)(2.059)Panel B: Number of Lots in 2008-2011 (N: 352)
Local Lords’ Estates Zone
-23.74∗∗∗ -24.29∗∗∗ -22.22∗∗∗(7.128)(5.781)(6.297)
Panel C: Number of Buildings in 2011 (N: 351)
Local Lords’ Estates Zone
-10.06∗∗ -11.16∗∗∗ -10.60∗∗∗(4.446)(3.866)(3.656)
Panel D: Stories (aboveground) in 2011 (N: 351)
Local Lords’ Estates Zone
2.159∗∗∗ 2.317∗∗∗2.020∗∗(0.746)(0.715)(0.873)
Panel E: Number of Buildings >= 30 Stories in 2011 (N: 351)
Local Lords’ Estates Zone
0.114∗∗
0.126∗∗
0.124∗∗∗(0.0452) (0.0512) (0.0469)
Panel F: Log Land Price in 2012 (N: 341)
Local Lords’ Estates Zone
0.179
0.443∗
0.343∗(0.333)(0.244)(0.202)
Distance from the Center (Castle)
No
Yes
Yes
West of the Yamanote line
No
Yes
Yes
Mean of Altitude
No
No
Yes
S.D. of Altitude
No
No
Yes
Earthquake Risk
No
No
Yes
Standard errors are in parentheses. We allow a within-300 m correlation in the error terms. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
See Table 1 for the deﬁnitions of the variables.
Local Lords’ Estates Zone takes a value of one if the central pointof the cell is in the local lords’ estate zone, the outer side of the
U-shaped boundary in Figure 1.

45

Table 5: Comparison between the Eﬀect and Distance Decay

Distance from Tokyo Station

Log Land Price

Number of Buildings

in 2012>= 30 Stories in 2011(1)
-0.168∗∗∗(0.0223)

Local Lords’ Estates Share
Distance from the Center (Castle)
Mean of Altitude
S.D. of Altitude
N

No
No
No
8971

(2)

0.321∗∗∗(0.0728)
Yes
Yes
Yes
8971

(3)
-0.00821∗∗(0.00367)

No
No
No
9542

(4)

0.0385∗∗(0.0150)
Yes
Yes
Yes
9542

Standard errors are shown in parentheses. We allow a within-300 m correlationin the error terms. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
See Table 1 for the deﬁnitions of the variables.

46

Table 6: Lot Size Persistence, Core vs. Non-core

Inside vs Outside the Circle (Yamanote) Line
300 m

1000 m2000 mInsideOutsideInsideOutsideInsideOutside

(1)

(2)

(3)

(4)

(5)

(6)

-11.72∗∗∗(0.751)-8.372∗∗∗(1.262)-11.71∗∗∗(0.720)-6.198∗∗∗(1.315)-21.32∗∗∗(2.925)-5.143(4.523)-20.40∗∗∗(2.808)0.501(5.667)-10.05∗∗∗(1.797)-2.826(3.455)-9.680∗∗∗(1.715)2.833(4.206)0.749∗∗(0.321)0.727(0.661)0.710∗∗(0.309)0.325(0.785)0.0375∗∗(0.0151)

0.0155(0.0217)0.0344∗∗(0.0143)0.0327(0.0223)0.187∗∗∗(0.0598)
Yes
Yes
Yes
Yes
Yes
4045
7160
7333
7333
7333
6855

0.00292(0.0401)
Yes
Yes
Yes
Yes
Yes
1485
1941
2209
2209
2209
2116

0.181∗∗∗(0.0598)
Yes
Yes
Yes
Yes
Yes
4702
7977
8315
8315
8315
7811

-0.0398(0.0394)
Yes
Yes
Yes
Yes
Yes
828
1124
1227
1227
1227
1160

Panel A: Number of Lots in 1872
Local Lords’ Estates Share-11.28∗∗∗ -10.01∗∗∗(0.790)(1.153)
Panel B: Number of Lots in 2008-2011
Local Lords’ Estates Share-23.27∗∗∗-6.457(2.890)(5.312)
Panel C: Number of Buildings in 2011
Local Lords’ Estates Share-11.34∗∗∗-3.696(1.773)(3.863)
Panel D: Stories (aboveground) in 2011
Local Lords’ Estates Share
0.871∗∗∗
0.211(0.318)(0.557)
Panel E: Number of Buildings >= 30 Stories in 2011
Local Lords’ Estates Share
0.0379∗∗0.00621(0.0154) (0.0166)
Panel F: Log Land Price in 2012
Local Lords’ Estates Share
0.247∗∗∗-0.141∗∗(0.0519) (0.0716)
Distance from the Center (Castle)
Yes
Yes
Mean of Altitude
Yes
Yes
S.D. of Altitude
Yes
Yes
Lon and Lat Controls
Yes
Yes
Earthquake Risk
Yes
Yes
N in Panel A
3555
1975
N in Panel B
6137
2964
N in Panel C
6144
3398
N in Panel D
6144
3398
N in Panel E
6144
3398
N in Panel F
5704
3267

Standard errors are in parentheses. We allow a within-300 m correlation in the error terms. ∗ p < 0.1,∗∗ p < 0.05, ∗∗∗ p < 0.01.

See Table 1 for the deﬁnitions of the variables.
Column (1) uses cells inside the (Yamanote) loop line or cells whose centroid is within 300 m of theloop line. Column (2) uses the other cells. Columns (3) and (4), or (5) and (6) use 1000 m or 2000 m
for the threshold, respectively.

47

Table 7: Lot Size Persistence, Core vs. Non-core (Local Randomization)
(1)

(2)

(3)

Panel I: Local Lords’ Estates Share (N: 351)
Local Lords’ Estates Zone (Core)
0.574∗∗∗
0.563∗∗∗ 0.478∗∗∗(0.108)(0.0824) (0.0931)
Local Lords’ Estates Zone (Non-core) 0.492∗∗∗
0.502∗∗∗ 0.456∗∗∗(0.114)(0.127)(0.118)Panel A: Number of Lots in 1872 (N: 350)
Local Lords’ Estates Zone (Core)-15.55∗∗∗ -13.11∗∗∗ -12.86∗∗∗(1.689)(2.088)(2.039)Local Lords’ Estates Zone (Non-core) -6.890∗∗∗ -10.40∗∗∗ -8.210∗∗∗(2.114)(2.541)(2.706)
Panel B: Number of Lots in 2008-2011 (N: 352)
Local Lords’ Estates Zone (Core)-40.98∗∗∗ -36.26∗∗∗ -34.63∗∗∗(5.671)(5.407)(6.817)
Local Lords’ Estates Zone (Non-core)
3.292-7.405-6.375(7.120)(7.601)(7.711)Panel C: Number of Buildings in 2011 (N: 351)
Local Lords’ Estates Zone (Core)-20.90∗∗∗ -19.06∗∗∗ -19.65∗∗∗(2.743)(2.852)(3.249)Local Lords’ Estates Zone (Non-core)
6.786-0.00764
0.957(5.596)(5.616)(5.544)
Panel D: Stories (aboveground) in 2011 (N: 351)
Local Lords’ Estates Zone (Core)3.685∗∗∗
3.931∗∗∗
3.164∗∗(0.939)(1.179)(1.484)
Local Lords’ Estates Zone (Non-core)-0.2140.03660.560(0.542)(0.774)(0.698)Panel E: Number of Buildings >= 30 Stories in 2011 (N: 351)
Local Lords’ Estates Zone (Core)
0.199∗∗∗
0.239∗∗∗ 0.213∗∗∗(0.0647) (0.0748) (0.0728)
Local Lords’ Estates Zone (Non-core) -0.0185∗-0.03310.0113(0.00990) (0.0382) (0.0327)
Panel F: Log Land Price in 2012 (N: 341)
Local Lords’ Estates Zone (Core)
0.922∗∗∗
1.035∗∗∗ 0.827∗∗∗(0.322)(0.225)(0.228)
Local Lords’ Estates Zone (Non-core) -0.874∗∗∗-0.333-0.237(0.301)(0.272)(0.275)Distance from the Center (Castle)
No
Yes
Yes
West of the Yamanote line
No
Yes
Yes
Mean of Altitude
No
No
Yes
S.D. of Altitude
No
No
Yes
Earthquake Risk
No
No
Yes
Standard errors are in parentheses. We allow a within-300 m correlationin the error terms. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
See Table 1 for the deﬁnitions of the variables.
Local Lords’ Estates Zone (Core) (Local Lords’ Estates Zone (NonCore)) takes a value of one if the central point of the cell is in thelocal lords’ estate zone, and the closest boundary is the solid (dash-dot)line in Figure 1.

48

Appendix Not for Publication0

Footprint (m^2)
1000 2000 3000 4000 5000

Figure A.1: Building Heights and Footprint

0

10

20

30

Story (topcoded at 30)
10 percentile
50 percentile
90 percentile

25 percentile
75 percentile

Notes: This graph shows the distribution of footprint of buildings located in Tokyo’s 23 wards in 2011 (Tokyo
Metropolitan Government, 2011). See the main text for the details of the dataset. We show the percentiles offootprint conditional on the number of stories.

10

Footprint (m^2)
1000 2000 3000 4000 5000

Figure A.2: Building Heights and Footprint in New York

0--10m20--30m
40--50m
60--70m
80--90m
100--110m
120+ m10--20m
30--40m
50--60m
70--80m
90--100m
110-120m

Height Group
10 percentile
50 percentile
90 percentile

25 percentile
75 percentile

Notes: This graph shows the distribution of footprint of buildings located in New York City (Syracuse andManhattan) in 2017 (Microsoft, 2017). We show the percentiles of footprint conditional on the height groups.

2Figure A.3: Coverage and Pattern of Land Price Data

Log Land Price in 1876
(2.637653,4.653054](1.977718,2.637653](1.588815,1.977718](1.288247,1.588815](.9883158,1.288247](.2908919,.9883158](-.8271542,.2908919][-11.51366,-.8271542]
No data(a) 1876

Log Land Price in 1876
(4.667208,6.416247](4.152652,4.667208](3.659582,4.152652](3.209641,3.659582](2.824726,3.209641](2.458769,2.824726](1.595291,2.458769][-9.774121,1.595291]
No data(b) 1912

Log Land Price in 1876
(13.67797,15.70863](13.3415,13.67797](13.11836,13.3415](12.93604,13.11836](12.76168,12.93604](12.64836,12.76168](12.52052,12.64836][11.42191,12.52052]
No data(d) 1972

Log Land Price in 1876
(4.309379,6.049422](3.649019,4.309379](3.203579,3.649019](2.772494,3.203579](2.421038,2.772494](2.201466,2.421038](1.68408,2.201466][-8.1059,1.68408]
No data(c) 1931

Log Land Price in 1876
(14.79954,16.93568](14.39408,14.79954](14.0374,14.39408](13.81426,14.0374](13.61755,13.81426](13.37243,13.61755](13.25464,13.37243][12.42796,13.25464]
No data(e) 1983

Log Land Price in 1876
(14.05936,16.62841](13.64651,14.05936](13.36212,13.64651](13.10888,13.36212](12.90905,13.10888](12.73606,12.90905](12.56117,12.73606][11.49272,12.56117]
No data(f) 2012

Notes: These maps show the pattern of land price data within our sample deﬁned by the border of old Tokyo
city. Black cells indicate missing values.

3Figure A.4: Distribution along the Zoning Boundary in the Core Area
Mean of Altitude

0

3

.2

4

.4

5

.6

6

.8

7

Local Lords' Estates Share

-1000

-500

0

Sample average within bin

500

1000

-1000

Polynomial fit of order 4

-500

0

Sample average within bin

500

(a)

(b)

S.D. of Altitude

Number of Lots in 2008-2011

-1000

0

0

20

.5

40

1

60

80

1.5

1000

Polynomial fit of order 4

-500

0

Sample average within bin

500

1000

-1000

Polynomial fit of order 4

-500

0

Sample average within bin

500

1000

Polynomial fit of order 4(d)Number of Buildings >= 30 Stories in 2011

Log Land Price in 2012

0

13.5

14

.2

14.5

15

.4

15.5

.6

16

(c)

-1000

-500

0

Sample average within bin

500

1000

-1000

Polynomial fit of order 4

-500

0

Sample average within bin

(e)

500

1000

Polynomial fit of order 4(f)Notes: We use all cells within 1 km of the boundary in Figure 1 excluding cells within 50 m of the boundary
to avoid mechanical attenuation eﬀects. The x-axis is the distance from the boundary, which is represented
by the dash-dot line in Figure 1, taking a positive and negative value in the local lords’ estate zone and thecommoners’ zone, respectively. The points show the average of each outcome variable within each bin. The
number of bins is chosen using the mimicking variance evenly spaced method using spacing estimators. Thelines show the fourth-order polynomial ﬁt for each zone.

4

Figure A.5: Distribution along the Zoning Boundary in the Non-core Area
Mean of Altitude

-.2

1.5

2

0

2.5

.2

3

.4

3.5

4

.6

Local Lords' Estates Share

-1000

-500

0

Sample average within bin

500

1000

-1000

Polynomial fit of order 4

0

500

1000

Polynomial fit of order 4(b)S.D. of Altitude

Number of Lots in 2008-2011

0

.5

20

1

40

60

1.5

2

80

(a)

0

-500

0

Sample average within bin

500

1000

-1000

Polynomial fit of order 4

-500

0

Sample average within bin

500

(d)

Number of Buildings >= 30 Stories in 2011

Log Land Price in 2012

-1000

13

-.1

13.5

0

14

14.5

.1

15

.2

1000

Polynomial fit of order 4

(c)
15.5

-1000

-500
Sample average within bin

-500

0

Sample average within bin

500

1000

-1000

Polynomial fit of order 4

-500

0

Sample average within bin

(e)

500

1000

Polynomial fit of order 4(f)Notes: We use all cells within 1 km of the boundary in Figure 1 excluding cells within 50 m of the boundary
to avoid mechanical attenuation eﬀects. The x-axis is the distance from the boundary, which is shown as thesolid line in Figure 1, taking a positive and negative value in the local lords’ estate zone and the commoners’zone, respectively. The points show the average of each outcome variable within each bin. The number of bins
is chosen using the mimicking variance evenly spaced method using spacing estimators. The lines show the
fourth-order polynomial ﬁt for each zone.

5

Figure A.6: Number of Lots and Land Prices along Zoning Boundary, from 1912 to the 2010s
Log Land Price in 1912

5

4

10

15

4.5

20

5

25

30

5.5

Number of Lots in 1912

-1000

-500

0

Sample average within bin

500

1000

-1000

Polynomial fit of order 4

-500

0

Sample average within bin

500

1000

Polynomial fit of order 4(b)Number of Lots in 1931

Log Land Price in 1931

-1000

3

10

3.5

15

4

20

4.5

25

5

30

(a)

-500

0

500

1000

-1000

Polynomial fit of order 4

0

500

1000

Polynomial fit of order 4(d)Number of Lots in 2008-2011

Log Land Price in 2012

14.5

15

(c)

14
13.5

60

13

40
20

-1000

-500
Sample average within bin

80

100

Sample average within bin

-500

0

Sample average within bin

500

1000

-1000

Polynomial fit of order 4

-500

0

Sample average within bin

(e)

500

1000

Polynomial fit of order 4(f)Notes: We use all cells within 1 km of the boundary in Figure 1 excluding cells within 50 m of the boundary
to avoid mechanical attenuation eﬀects. The x-axis is the distance from the boundary, which is shown as thesolid line and dash-dot line in Figure 1, taking a positive and negative value in the local lords’ estate zone andthe commoners’ zone, respectively. The points show the average of each outcome variable within each bin. The
number of bins is chosen using the mimicking variance evenly spaced method using spacing estimators. Thelines show the fourth-order polynomial ﬁt for each zone.

6

Figure A.7: Number of Lots and Land Prices along Zoning Boundary in the Non-core Area,
from 1912 to the 2010s
Log Land Price in 1912

0

4

4.5

10

5

20

5.5

6

30

Number of Lots in 1912

-1000

-500

0

Sample average within bin

500

1000

-1000

Polynomial fit of order 4

-500

0

Sample average within bin

500

1000

Polynomial fit of order 4(b)Number of Lots in 1931

Log Land Price in 1931

-500

0

Sample average within bin

500

1000

-1000

Polynomial fit of order 4

-500

0

Sample average within bin

500

(d)

Number of Lots in 2008-2011

Log Land Price in 2012

0

13

13.5

20

14

40

14.5

60

15

80

1000

Polynomial fit of order 4

(c)
15.5

-1000

3

0

3.5

10

4

4.5

20

5

30

5.5

(a)

-1000

-500

0

Sample average within bin

500

1000

-1000

Polynomial fit of order 4

-500

0

Sample average within bin

(e)

500

1000

Polynomial fit of order 4(f)Notes: We use all cells within 1 km of the boundary in Figure 1 excluding cells within 50 m of the boundary
to avoid mechanical attenuation eﬀects. The x-axis is the distance from the boundary, which is shown as thesolid line in Figure 1, taking a positive and negative value in the local lords’ estate zone and the commoners’zone, respectively. The points show the average of each outcome variable within each bin. The number of bins
is chosen using the mimicking variance evenly spaced method using spacing estimators. The lines show the
fourth-order polynomial ﬁt for each zone.

7

Figure A.8: Aerial Image around the Boundary (Core) in 1970 and 2011(a) 1970(b) 2011

Notes: These images are aerial images around the boundary closer to the core area (shown as the white dashlines). The panel (a) is image in 1970 and panel (b) is the aerial image as of Mar/2011 taken from Google
Earth.

Table A.1: Quantile Regression for Building Stories in 2011
(1)
Panel A: Log Land Price in 2012 (N: 9542)
Local Lords’ Estates Share-0.311∗∗∗(0.0850)
Panel B: Log Land Price in 2012 (N: 9542)
Local Lords’ Estates Share-0.314∗∗∗(0.0642)
Panel C: Log Land Price in 2012 (N: 9542)
Local Lords’ Estates Share-0.251∗∗(0.103)
Panel D: Log Land Price in 2012 (N: 9542)
Local Lords’ Estates Share
0.532∗∗∗(0.185)
Panel E: Log Land Price in 2012 (N: 9542)
Local Lords’ Estates Share
4.265∗∗∗(0.594)
Distance from the Center (Castle)
Yes
Mean of Altitude
No
No
S.D. of Altitude(2)-0.371∗∗∗(0.0853)-0.154∗∗∗(0.0342)-0.119(0.102)0.660∗∗∗(0.187)4.150∗∗∗(0.607)
Yes
Yes
Yes

Standard errors are in parentheses. ∗ p < 0.1, ∗∗ p <0.05, ∗∗∗ p < 0.01.
See Table 1 for the deﬁnitions of the variables.

8

Table A.2: Spillover Eﬀects(1)
Panel A: Number of Lots in 1872 (N: 5530)
Local Lords’ Estates Share-11.23∗∗∗(0.660)Local Lords’ Estates (3*3 Cells)

(2)

(3)

(4)

-7.751∗∗∗(0.822)-0.587∗∗∗(0.176)-7.288∗∗∗(0.661)-0.819∗∗∗(0.190)0.0527(0.0567)-7.636∗∗∗(0.639)-0.569∗∗∗(0.174)-0.162∗(0.0910)0.0962∗∗(0.0433)-10.16∗∗∗(1.829)-1.821∗∗∗(0.686)-0.0240(0.178)-10.91∗∗∗(1.737)-1.300∗∗(0.603)-0.449(0.284)0.187(0.138)-3.391∗∗∗(1.099)-1.139∗∗∗(0.432)-0.0807(0.110)-3.627∗∗∗(1.045)-0.974∗∗∗(0.369)-0.216(0.185)0.0597(0.0906)

0.222(0.264)0.0732(0.0776)
0.0129(0.0207)0.194(0.249)0.0926(0.0792)-0.00307(0.0367)
0.00703(0.0153)

Local Lords’ Estates (5*5 Cells)
Local Lords’ Estates (7*7 Cells)
Panel B: Number of Lots in 2008-2011 (N: 9101)
Local Lords’ Estates Share-21.53∗∗∗ -9.930∗∗∗(2.648)(2.142)
Local Lords’ Estates (3*3 Cells)-1.928∗∗∗(0.548)
Local Lords’ Estates (5*5 Cells)
Local Lords’ Estates (7*7 Cells)
Panel C: Number of Buildings in 2011 (N: 9542)
Local Lords’ Estates Share-11.61∗∗∗ -2.639∗∗(1.654)(1.302)-1.500∗∗∗Local Lords’ Estates (3*3 Cells)(0.341)
Local Lords’ Estates (5*5 Cells)
Local Lords’ Estates (7*7 Cells)
Panel D: Stories (aboveground) in 2011 (N: 9542)
Local Lords’ Estates Share
0.884∗∗
0.101(0.353)(0.323)
Local Lords’ Estates (3*3 Cells)
0.131∗(0.0758)
Local Lords’ Estates (5*5 Cells)
Local Lords’ Estates (7*7 Cells)

Panel E: Number of Buildings >= 30 Stories in 2011 (N: 9542)
Local Lords’ Estates Share
0.0385∗∗ -0.00176
0.00412(0.0150) (0.0167)(0.0141)
Local Lords’ Estates (3*3 Cells)
0.00673∗
0.00391(0.00363) (0.00368)
Local Lords’ Estates (5*5 Cells)
0.000632(0.000907)
Local Lords’ Estates (7*7 Cells)
Panel F: Log Land Price in 2012 (N: 8971)
Local Lords’ Estates Share
0.321∗∗∗(0.0728)
Local Lords’ Estates (3*3 Cells)-0.162∗∗∗(0.0311)0.0830∗∗∗(0.0161)

0.0685∗∗∗(0.0210)-0.0268(0.0170)0.0245∗∗∗(0.00430)

Yes
Yes
Yes

Yes
Yes
Yes

Local Lords’ Estates (5*5 Cells)
Local Lords’ Estates (7*7 Cells)
Distance from the Center (Castle)
Mean of Altitude
S.D. of Altitude

Yes
Yes
Yes

0.00163(0.0134)
0.00565(0.00368)-0.000799(0.00167)
0.000629(0.000669)
0.0127(0.0149)
0.0128(0.0119)-0.00886(0.00753)
0.0146∗∗∗(0.00363)
Yes
Yes
Yes

Standard errors are in parentheses. We allow a within-300 m correlation in theerror terms. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
See Table 1 for the deﬁnitions of the variables.
Local Lords’ Estates (3*3 Cells) is the sum of Local Lords’ Estates in 3 ∗ 3 − 1 cells
surrounding each cell. This means that the point estimate is the eﬀect when oneof the cells becomes fully local lords’ estates, which is comparable with the point
estimate of Local Lords’ Estates. Other treatment variables are deﬁned similarly.

9

Table A.3: Diﬀerent Thresholds for Conley Standard Errors

Allowing Correlation Within
300 m
(1)

500 m
(2)

(3)

Panel A: Number of Lots in 1872 (N: 5530)
Local Lords’ Estates Share-11.45∗∗∗ -11.35∗∗∗ -11.45∗∗∗(0.682)(0.691)(0.893)
Panel B: Number of Lots in 2008-2011 (N: 9101)
Local Lords’ Estates Share-23.29∗∗∗ -19.49∗∗∗ -23.29∗∗∗(2.686)(2.695)(3.189)Panel C: Number of Buildings in 2011 (N: 9542)
Local Lords’ Estates Share-13.06∗∗∗ -10.36∗∗∗ -13.06∗∗∗(1.675)(1.652)(1.982)
Panel D: Stories (aboveground) in 2011 (N: 9542)
Local Lords’ Estates Share0.802∗∗
0.845∗∗∗
0.802∗∗(0.352)(0.305)(0.407)
Panel E: Number of Buildings >= 30 Stories in 2011 (N: 9542)
Local Lords’ Estates Share
0.0394∗∗∗ 0.0360∗∗∗ 0.0394∗∗∗(0.0147)(0.0139)(0.0151)
Panel F: Log Land Price in 2012 (N: 8971)
Local Lords’ Estates Share
0.338∗∗∗
0.200∗∗∗
0.338∗∗∗(0.0696)(0.0567)(0.0908)
Distance from the Center (Castle)
Yes
Yes
Yes
Mean of Altitude
No
Yes
No
S.D. of Altitude
No
Yes
No
Lon and Lat Controls
No
Yes
No
Earthquake Risk
No
Yes
No

1000 m
(4)

(5)

(6)

-11.35∗∗∗(0.882)-11.45∗∗∗(1.206)-11.35∗∗∗(1.072)-19.49∗∗∗(3.277)-23.29∗∗∗(3.568)-19.49∗∗∗(3.536)-10.36∗∗∗(2.008)-13.06∗∗∗(2.136)-10.36∗∗∗(2.227)0.845∗∗(0.334)0.802∗(0.432)0.845∗∗∗(0.316)

0.0360∗∗(0.0141)0.0394∗∗(0.0162)

0.0360∗∗(0.0142)0.200∗∗∗(0.0735)
Yes
Yes
Yes
Yes
Yes

0.338∗∗∗(0.103)
Yes
No
No
No
No

0.200∗∗(0.0800)
Yes
Yes
Yes
Yes
Yes

Standard errors are in parentheses. We allow a within-300 m, 300 m, or 1000 m correlation in error
terms in columns (1)–(2), (3)–(4), or (5)–(6), respectively. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
See Table 1 for the deﬁnitions of the variables.

10

Table A.4: Alternative Speciﬁcation of the Treatment Variable: Lot Size of Local Lords’
Estates(1)(2)

Panel A: Number of Lots in 1872 (N: 5530)
Local Lords’ Estates Lot Area (Max) -0.00127∗∗∗-0.00126∗∗∗(0.0000760)(0.0000742)
Panel B: Number of Lots in 2008-2011 (N: 9101)
Local Lords’ Estates Lot Area (Max) -0.00249∗∗∗-0.00227∗∗∗(0.000313)(0.000310)
Panel C: Number of Buildings in 2011 (N: 9542)
Local Lords’ Estates Lot Area (Max) -0.00134∗∗∗-0.00113∗∗∗(0.000196)(0.000197)
Panel D: Stories (aboveground) in 2011 (N: 9542)
Local Lords’ Estates Lot Area (Max)
0.0000407
0.0000568(0.0000409)(0.0000425)
Panel E: Number of Buildings >= 30 Stories in 2011 (N: 9542)
Local Lords’ Estates Lot Area (Max) 0.00000362∗∗ 0.00000357∗∗(0.00000169) (0.00000177)
Panel F: Log Land Price in 2012 (N: 8971)
Local Lords’ Estates Lot Area (Max) 0.0000278∗∗∗ 0.0000232∗∗∗(0.00000728) (0.00000765)
Distance from the Center (Castle)
Yes
Yes
Mean of Altitude
No
Yes
S.D. of AltitudeNo
Yes
Lon and Lat Controls
No
No
Earthquake Risk
No
No

(3)

-0.00129∗∗∗(0.0000781)-0.00214∗∗∗(0.000320)-0.00109∗∗∗(0.000199)
0.0000729∗(0.0000373)
0.00000375∗∗(0.00000166)
0.0000153∗∗(0.00000601)
Yes
Yes
Yes
Yes
Yes

Standard errors are in parentheses. We allow a within-300 m correlation in the error
terms. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
See Table 1 for the deﬁnitions of the variables. Local Lords’ Estates Lot Area (Max)is the max of lot area (km2 ) of local lords’ estates in each cell. This takes zero if acell has no local lords’ estates.

11

Table A.5: Coeﬃcient Stability in Main Results
(1)

(2)

(3)

-11.45∗∗∗(0.682)-12.05-11.23∗∗∗(0.660)-9.743-11.35∗∗∗(0.691)-11.19Panel B: Number of Lots in 2008-2011
Local Lords’ Estates Share-23.29∗∗∗(2.686)
Bias-Adjusted Beta
-18.14-21.53∗∗∗(2.648)-17.92-19.49∗∗∗(2.695)-15.96

Panel C: Number of Buildings in 2011
Local Lords’ Estates Share-13.06∗∗∗(1.675)
Bias-Adjusted Beta
-10.24-11.61∗∗∗(1.654)-8.897-10.36∗∗∗(1.652)-7.380Panel D: Stories (aboveground) in 2011
Local Lords’ Estates Share0.802∗∗(0.352)
Bias-Adjusted Beta
0.493

0.884∗∗(0.353)0.605

0.845∗∗∗(0.305)
0.547

Panel E: Number of Buildings >= 30 Stories in 2011
Local Lords’ Estates Share
0.0394∗∗∗ 0.0385∗∗(0.0147) (0.0150)
Bias-Adjusted Beta
0.03080.03050.0360∗∗∗(0.0139)0.0300Panel I: Number of Lots in 1872
Local Lords’ Estates Share
Bias-Adjusted Beta

Panel F: Log Land Price in 2012
Local Lords’ Estates Share
Bias-Adjusted Beta
Distance from the Center (Castle)
Mean of Altitude
S.D. of Altitude
Lon and Lat Controls
Earthquake Risk

0.338∗∗∗(0.0696)0.229

0.321∗∗∗(0.0728)0.208

0.200∗∗∗(0.0567)
0.0424

Yes
No
No
No
No

Yes
Yes
Yes
No
No

Yes
Yes
Yes
Yes
Yes

Standard errors are in parentheses. We allow a within-300 m correlation in the error terms. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
See Table 1 for the deﬁnitions of the variables.
The ﬁrst row shows the coeﬃcients using speciﬁcations in columns(2) and (3) of Table 3. The second row shows the bias-adjusted
coeﬃcient by considering potential unobserved confounders, as proposed by Oster (2019). As suggested in this study, we set δ = 1
2
and Rmax= 1.3R̃2 , where R̃2 is R2 in the regression models in eachcolumn as plausible parameters. In case there are multiple solutions
for the bias-adjusted beta, we show the one closest to the original
beta.

12

Table A.6: Regression Discontinuity Design with Polynomials
(1)

(2)

(3)

Panel I: Local Lords’ Estates Share (N: 1269)
Local Lords’ Estates Zone
0.507∗∗∗ 0.541∗∗∗ 0.585∗∗∗(0.117)(0.105)(0.0841)
Panel A: Number of Lots in 1872 (N: 1350)
Local Lords’ Estates Zone
-9.432∗∗∗ -9.222∗∗∗ -11.00∗∗∗(2.517)(2.597)(2.058)Panel B: Number of Lots in 2008-2011 (N: 1293)
Local Lords’ Estates Zone
-15.62-14.55-19.47∗∗∗(10.16)(9.436)(7.281)Panel C: Number of Buildings in 2011 (N: 1296)
Local Lords’ Estates Zone
-5.651-4.487-7.740∗(6.110)(5.757)(4.334)
Panel D: Stories (aboveground) in 2011 (N: 1296)
Local Lords’ Estates Zone
2.159∗
1.854∗
2.729∗∗∗(1.252)(0.993)(0.990)Panel E: Number of Buildings >= 30 Stories in 2011 (N: 1296)
Local Lords’ Estates Zone
0.126∗
0.112∗∗
0.127∗∗(0.0663) (0.0556) (0.0545)
Panel F: Log Land Price in 2012 (N: 1135)
Local Lords’ Estates Zone
0.194
0.08580.208(0.408)(0.388)(0.309)Distance from the Boundary (1st-3rd)
Yes
Yes
Yes
Distance from the Boundary (4th)
No
Yes
No
Distance from the Center (Castle)
No
No
Yes
Standard errors are in parentheses. We allow a within-300 m correlationin the error terms. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
See Table 1 for the deﬁnitions of the variables.
We use cells within 1000 m of the boundary (dash-dot line and solid line
in Figure 1).

13

Table A.7: Coeﬃcient Stability in Main Results (Local Randomization)(1)
Panel I: Local Lords’ Estates Share
Local Lords’ Estates Zone
0.538∗∗∗(0.0755)
Bias-Adjusted Beta
0.586
Panel A: Number of Lots in 1872
Local Lords’ Estates Zone(2)
0.468∗∗∗(0.0822)0.779-11.97∗∗∗(1.777)-11.72-10.79∗∗∗(2.059)-8.173

Panel B: Number of Lots in 2008-2011
Local Lords’ Estates Zone
-24.29∗∗∗(5.781)
Bias-Adjusted Beta
-24.61-22.22∗∗∗(6.297)-21.04

Panel C: Number of Buildings in 2011
Local Lords’ Estates Zone
-11.16∗∗∗(3.866)
Bias-Adjusted Beta
-11.66-10.60∗∗∗(3.656)-10.93

Panel D: Stories (aboveground) in 2011
Local Lords’ Estates Zone
2.317∗∗∗(0.715)
Bias-Adjusted Beta
2.432

2.020∗∗(0.873)
1.917

Bias-Adjusted Beta

Panel E: Number of Buildings >= 30 Stories in 2011
Local Lords’ Estates Zone
0.126∗∗
0.124∗∗∗(0.0512) (0.0469)
Bias-Adjusted Beta
0.137
0.132
Panel F: Log Land Price in 2012
Local Lords’ Estates Zone
Bias-Adjusted Beta
Distance from the Center (Castle)
West of the Yamanote line
Mean of Altitude
S.D. of Altitude
Earthquake Risk

0.443∗(0.244)0.536

0.343∗(0.202)
0.422

Yes
Yes
No
No
No

Yes
Yes
Yes
Yes
Yes

Standard errors are in parentheses. We allow a within300 m correlation in the error terms. ∗ p < 0.1, ∗∗ p <0.05, ∗∗∗ p < 0.01.
See Table 1 for the deﬁnitions of the variables.
The ﬁrst row shows the coeﬃcients using speciﬁcations
in columns (2)–(3) of Table 4. The second row shows thebias-adjusted coeﬃcient by considering potential unobserved confounders, as proposed by Oster (2019). As
2suggested in this study, we set δ = 1 and Rmax
= 1.3R̃2 ,22
where R̃ is R in the regression models in each column,
as plausible parameters. In case there are multiple solutions for the bias-adjusted beta, we show the one closest
to the original beta.

14

Table A.8: Time-varying Eﬀects of Local Lords’ Estates
(1)

(2)

(3)

(4)

-10.76∗∗∗(0.647)-10.51∗∗∗(0.618)-10.45∗∗∗(0.658)-10.51∗∗∗(0.657)-12.04∗∗∗(0.561)-11.72∗∗∗(0.547)-11.83∗∗∗(0.576)-11.84∗∗∗(0.578)-9.537∗∗∗(0.863)-9.238∗∗∗(0.845)-9.376∗∗∗(0.870)-9.399∗∗∗(0.873)-0.255(0.175)-0.312∗(0.181)-0.375∗∗(0.184)-0.402∗∗(0.183)-0.421∗∗∗(0.119)-0.312∗∗∗(0.103)-0.499∗∗∗(0.101)-0.507∗∗∗(0.101)-0.633∗∗∗(0.139)Panel G: Log Land Rental Price in 1931
Local Lords’ Estates Share-0.439∗∗∗(0.0991)
Panel H: Log Land Price in 1972
Local Lords’ Estates Share-0.0783(0.0494)
Panel I: Log Land Price in 1983
Local Lords’ Estates Share
0.0410(0.0575)
Distance from the Center (Castle)
Yes
Mean of Altitude
No
S.D. of AltitudeNo
Lon and Lat Controls
No
Earthquake Risk
No
N in Panel A
5316
N in Panel B
8133
N in Panel C
7830
N in Panel D
3644
N in Panel E
7122
N in Panel F
4711
N in Panel G
7024
N in Panel H
6071
N in Panel I
3276-0.525∗∗∗(0.119)-0.698∗∗∗(0.111)-0.722∗∗∗(0.109)-0.319∗∗∗(0.0856)-0.393∗∗∗(0.0800)-0.406∗∗∗(0.0794)-0.0615(0.0482)-0.0420(0.0440)-0.0525(0.0417)0.0559(0.0569)
Yes
Yes
Yes
No
No
5316
8133
7830
3644
7122
4711
7024
6071
3276

0.0141(0.0490)
Yes
Yes
Yes
Yes
No
5316
8133
7830
3644
7122
4711
7024
6071
3276

0.00125(0.0451)
Yes
Yes
Yes
Yes
Yes
5316
8133
7830
3644
7122
4711
7024
6071
3276

Panel A: Number of Lots in 1876
Local Lords’ Estates Share
Panel B: Number of Lots in 1912
Local Lords’ Estates Share
Panel C: Number of Lots in 1931
Local Lords’ Estates Share
Panel D: Log Land Price in 1876
Local Lords’ Estates Share
Panel E: Log Land Price in 1912
Local Lords’ Estates Share
Panel F: Log Land Price in 1931
Local Lords’ Estates ShareStandard errors are in parentheses. We allow a within-300 m correlation in theerror terms. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
See Table 1 for the deﬁnitions of the variables.
Log Land Rental Price in 1931 is used to assess Log Land Price in 1931, andLog Land Rental Price in 1931 is available, with greater coverage in Seizusha,ed (1931-1935).

15

Table A.9: Time-varying Eﬀects of Local Lords’ Estates (Local Randomization)(1)
Panel I: Local Lords’ Estates Share
Local Lords’ Estates Zone
0.542∗∗∗(0.0803)
Panel A: Number of Lots in 1876
Local Lords’ Estates Zone
-12.31∗∗∗(1.912)
Panel B: Number of Lots in 1912
Local Lords’ Estates Zone
-11.27∗∗∗(2.033)
Panel C: Number of Lots in 1931
Local Lords’ Estates Zone
-9.522∗∗∗(2.543)
Panel D: Log Land Price in 1876
Local Lords’ Estates Zone
-0.894∗∗∗(0.236)
Panel E: Log Land Price in 1912
Local Lords’ Estates Zone
-0.838∗∗∗(0.211)Panel F: Log Land Price in 1931
Local Lords’ Estates Zone
-0.697∗∗∗(0.188)Panel G: Log Land Rental Price in 1931
Local Lords’ Estates Zone
-0.462∗∗∗(0.151)Distance from the Center (Castle)
No
West of the Yamanote line
No
Mean of Altitude
No
S.D. of Altitude
No
Earthquake Risk
No
N in Panel I
351
N in Panel A
348
N in Panel B
343
N in Panel C
347
N in Panel D
319
N in Panel E
294
N in Panel F
268
N in Panel G
299

(2)

(3)

0.538∗∗∗(0.0755)

0.468∗∗∗(0.0822)-11.94∗∗∗(1.741)-10.86∗∗∗(2.043)-11.03∗∗∗(1.781)-9.991∗∗∗(2.095)-10.55∗∗∗(1.737)-9.672∗∗∗(2.021)-0.876∗∗∗(0.226)-0.728∗∗∗(0.237)-0.873∗∗∗(0.241)-0.709∗∗∗(0.242)-0.604∗∗∗(0.228)-0.487∗∗(0.234)-0.331∗∗(0.152)
Yes
Yes
No
No
No
351
348
343
347
319
294
268
299

-0.342∗(0.180)
Yes
Yes
Yes
Yes
Yes
351
348
343
347
319
294
268
299

Standard errors are in parentheses. We allow a within-300 m correlation in the error terms. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
See Table 1 for the deﬁnitions of the variables.
Log Land Rental Price in 1931 is used to assess Log Land Price in
1931, and Log Land Rental Price in 1931 is available, with greater
coverage in Seizusha, ed (1931-1935).

16

Table A.10: Controlling for Public Infrastructure (OLS)
(1)

(2)

(3)

(4)

(5)

-11.35∗∗∗(0.691)-11.41∗∗∗(0.671)-11.30∗∗∗(0.650)-11.07∗∗∗(0.679)-11.15∗∗∗(0.639)-19.49∗∗∗(2.695)-14.26∗∗∗(2.370)-15.07∗∗∗(2.233)-18.76∗∗∗(2.649)-10.82∗∗∗(1.861)-10.36∗∗∗(1.652)-6.132∗∗∗(1.408)-7.653∗∗∗(1.414)-10.38∗∗∗(1.646)-4.568∗∗∗(1.224)0.845∗∗∗(0.305)
Panel E: Number of Buildings >= 30 Stories in 2011
Local Lords’ Estates Share
0.0360∗∗∗(0.0139)
Panel F: Log Land Price in 2012
Local Lords’ Estates Share
0.200∗∗∗(0.0567)
Road WidthNo
Hospital, University, and Parks Share
No
Distance to Nearest Station in 2018 and 1950
No
Distance from the Center (Castle)
Yes
Mean of Altitude
Yes
S.D. of Altitude
Yes
Lon and Lat Controls
Yes
Earthquake Risk
Yes
N in Panel A
5530
N in Panel B
9101
N in Panel C
9542
N in Panel D
9542
N in Panel E
9542
N in Panel F
8971

0.714∗∗(0.281)1.075∗∗∗(0.296)0.890∗∗∗(0.297)0.843∗∗∗(0.267)0.0352∗∗∗(0.0136)0.0399∗∗∗(0.0141)0.0350∗∗(0.0140)0.0354∗∗∗(0.0136)0.115∗∗(0.0483)
Yes
No
No
Yes
Yes
Yes
Yes
Yes
5137
8527
9003
9003
9003
8909

0.207∗∗∗(0.0565)
No
Yes
No
Yes
Yes
Yes
Yes
Yes
5530
9101
9542
9542
9542
8971

0.205∗∗∗(0.0529)
No
No
Yes
Yes
Yes
Yes
Yes
Yes
5530
9101
9542
9542
9542
8971

0.132∗∗∗(0.0457)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
5137
8527
9003
9003
9003
8909

Panel A: Number of Lots in 1872
Local Lords’ Estates Share
Panel B: Number of Lots in 2008-2011
Local Lords’ Estates Share
Panel C: Number of Buildings in 2011
Local Lords’ Estates Share
Panel D: Stories (aboveground) in 2011
Local Lords’ Estates ShareStandard errors are in parentheses. We allow a within-300 m correlation in the error terms. ∗ p < 0.1,∗∗ p < 0.05, ∗∗∗ p < 0.01.
See Table 1 for the deﬁnitions of the variables. Road Width is the average road width. Hospital,
University, and Parks Share is the share of land used as hospitals or universities, or parks. Distanceto Nearest Station in 2018 and 1950 is the distance to the nearest station in 2018 and 1950 (includingtram stations).

17

Table A.11: Controlling for Public Infrastructure (Local Randomization)
(1)

(2)

(3)

(4)

(5)

0.468∗∗∗(0.0822)0.432∗∗∗(0.0786)

0.472∗∗∗(0.0807)

0.466∗∗∗(0.0777)0.430∗∗∗(0.0744)-10.79∗∗∗(2.059)-9.935∗∗∗(2.042)-10.76∗∗∗(2.040)-10.69∗∗∗(2.164)-9.602∗∗∗(2.099)-22.22∗∗∗(6.297)-16.16∗∗∗(5.523)-22.03∗∗∗(6.256)-22.47∗∗∗(5.648)-15.79∗∗∗(5.021)-10.60∗∗∗(3.656)-7.362∗∗(3.154)-10.52∗∗∗(3.622)-11.10∗∗∗(3.246)-7.557∗∗∗(2.824)2.020∗∗(0.873)
Panel E: Number of Buildings >= 30 Stories in 2011
Local Lords’ Estates Zone
0.124∗∗∗(0.0469)
Panel F: Log Land Price in 2012
Local Lords’ Estates Zone
0.343∗(0.202)
Panel G: Log Land Price in 2012
Local Lords’ Estates Zone (Core)
0.827∗∗∗(0.228)
Local Lords’ Estates Zone (Non-core)-0.237(0.275)
Road WidthNo
Hospital, University, and Parks Share
No
Distance to Nearest Station in 2018 and 1950
No
Distance from the Center (Castle)
Yes
West of the Yamanote line
Yes
Mean of Altitude
Yes
S.D. of Altitude
Yes
Earthquake Risk
Yes
N in Panel I
351
N in Panel A
350
N in Panel B
352
N in Panel C
351
N in Panel D
351
N in Panel E
351
N in Panel F
341
N in Panel G341

1.897∗∗(0.766)2.048∗∗(0.890)1.975∗∗(0.851)1.796∗∗(0.727)0.120∗∗∗(0.0462)0.126∗∗∗(0.0473)

0.117∗∗(0.0478)

0.112∗∗(0.0454)0.173(0.168)0.354∗(0.201)0.366∗(0.193)0.199(0.166)0.544∗∗(0.212)-0.227(0.264)
Yes
No
No
Yes
Yes
Yes
Yes
Yes
338
336
338
338
338
338
336
336

0.836∗∗∗(0.233)-0.225(0.268)
No
Yes
No
Yes
Yes
Yes
Yes
Yes
351
350
352
351
351
351
341
341

0.699∗∗∗(0.215)-0.0475(0.274)
No
No
Yes
Yes
Yes
Yes
Yes
Yes
351
350
352
351
351
351
341
341

0.427∗∗(0.194)-0.0603(0.250)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
338
336
338
338
338
338
336
336

Panel I: Local Lords’ Estates Share
Local Lords’ Estates Zone
Panel A: Number of Lots in 1872
Local Lords’ Estates Zone
Panel B: Number of Lots in 2008-2011
Local Lords’ Estates Zone
Panel C: Number of Buildings in 2011
Local Lords’ Estates Zone
Panel D: Stories (aboveground) in 2011
Local Lords’ Estates Zone

Standard errors are in parentheses. We allow a within-300 m correlation in the error terms. ∗ p < 0.1,∗∗ p < 0.05, ∗∗∗ p < 0.01.

See Table 1 for the deﬁnitions of the variables. Road Width is the average road width. Hospital,
University, and Parks Share is the share of land used as hospitals or universities, or parks. Distanceto Nearest Station in 2018 and 1950 is the distance to the nearest station in 2018 and 1950 (includingtram stations).

18

Table A.12: Controlling for Block Size or FAR (OLS)(1)
Panel A: Number of Lots in 1872
Local Lords’ Estates Share(2)-11.35∗∗∗ -10.30∗∗∗(0.691)(0.698)Panel B: Number of Lots in 2008-2011
Local Lords’ Estates Share-19.49∗∗∗ -12.86∗∗∗(2.695)(2.544)
Panel C: Number of Buildings in 2011
Local Lords’ Estates Share-10.36∗∗∗ -6.325∗∗∗(1.652)(1.531)Panel D: Stories (aboveground) in 2011
Local Lords’ Estates Share
0.845∗∗∗
1.077∗∗∗(0.305)(0.303)
Panel E: Number of Buildings >= 30 Stories in 2011
Local Lords’ Estates Share
0.0360∗∗∗ 0.0377∗∗∗(0.0139)(0.0143)
Panel F: Log Land Price in 2012
Local Lords’ Estates Share
0.200∗∗∗
0.180∗∗∗(0.0567)(0.0569)
Block SizeNo
Yes
FAR Regulation
No
No
Distance from the Center (Castle)
Yes
Yes
Mean of Altitude
Yes
Yes
S.D. of Altitude
Yes
Yes
Lon and Lat Controls
Yes
Yes
Earthquake Risk
Yes
Yes
N in Panel A
5530
5529
N in Panel B
9101
9095
N in Panel C
9542
9541
N in Panel D
9542
9541
N in Panel E
9542
9541
N in Panel F
8971
8968

(3)

(4)

-11.66∗∗∗(0.674)-11.15∗∗∗(0.664)-16.08∗∗∗(2.522)-12.35∗∗∗(2.344)-7.948∗∗∗(1.466)-5.605∗∗∗(1.347)0.886∗∗∗(0.255)0.939∗∗∗(0.253)0.0392∗∗∗(0.0139)0.0387∗∗∗(0.0139)0.165∗∗∗(0.0461)
No
Yes
Yes
Yes
Yes
Yes
Yes
5134
8521
9001
9001
9001
8909

0.149∗∗∗(0.0459)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
5133
8518
9000
9000
9000
8906

Standard errors are in parentheses. We allow a within-300 m correlation in theerror terms. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
See Table 1 for the deﬁnitions of the variables.

19

Table A.13: Controlling for Block Size or FAR (Local Randomization)

Panel I: Local Lords’ Estates Share
Local Lords’ Estates Zone
Panel A: Number of Lots in 1872
Local Lords’ Estates Zone

(1)

(2)

(3)

0.468∗∗∗(0.0822)0.478∗∗∗(0.0811)0.412∗∗∗(0.0810)-10.79∗∗∗(2.059)-10.90∗∗∗(2.083)-10.09∗∗∗(2.053)

Panel B: Number of Lots in 2008-2011
Local Lords’ Estates Zone
-22.22∗∗∗ -21.60∗∗∗(6.297)(6.234)
Panel C: Number of Buildings in 2011
Local Lords’ Estates Zone
-10.60∗∗∗ -10.43∗∗∗(3.656)(3.645)
Panel D: Stories (aboveground) in 2011
Local Lords’ Estates Zone
2.020∗∗
2.082∗∗(0.873)(0.862)
Panel E: Number of Buildings >= 30 Stories in 2011
Local Lords’ Estates Zone
0.124∗∗∗ 0.120∗∗∗(0.0469) (0.0456)
Panel F: Log Land Price in 2012
Local Lords’ Estates Zone
0.343∗
0.323(0.202)(0.203)
Panel G: Log Land Price in 2012
Local Lords’ Estates Zone (Core)
0.827∗∗∗ 0.806∗∗∗(0.228)(0.236)
Local Lords’ Estates Zone (Non-core)-0.237-0.241(0.275)(0.278)Block SizeNo
Yes
FAR Regulation
No
No
Distance from the Center (Castle)
Yes
YesWest of the Yamanote line
Yes
Yes
Mean of Altitude
Yes
Yes
S.D. of Altitude
Yes
Yes
Earthquake Risk
Yes
Yes
N in Panel I
351
351
N in Panel A
350
350
N in Panel B
352
352
N in Panel C
351
351
N in Panel D
351
351
N in Panel E
351
351
N in Panel F
341
341
N in Panel G
341
341-18.25∗∗∗(6.333)-8.457∗∗(3.488)2.019∗∗(0.836)0.121∗∗(0.0481)0.165(0.140)0.454∗∗(0.188)-0.153(0.181)
No
Yes
Yes
Yes
Yes
Yes
Yes
338
336
338
338
338
338
336
336

Standard errors are in parentheses. We allow a within-300 m correlationin the error terms. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
See Table 1 for the deﬁnitions of the variables.

20

Table A.14: Controlling for Initial Land Price in 1876 (OLS)
(1)

(2)

(3)

Panel A: Number of Lots in 1872 (N: 3618)
Local Lords’ Estates Share-12.66∗∗∗ -12.41∗∗∗ -12.83∗∗∗(0.721)(0.702)(0.732)
Panel B: Number of Lots in 2008-2011 (N: 3614)
Local Lords’ Estates Share-18.64∗∗∗ -18.22∗∗∗ -17.78∗∗∗(3.666)(3.657)(3.527)
Panel C: Number of Buildings in 2011 (N: 3605)
Local Lords’ Estates Share-9.501∗∗∗ -9.450∗∗∗ -6.930∗∗∗(2.223)(2.229)(2.266)
Panel D: Stories (aboveground) in 2011 (N: 3605)
Local Lords’ Estates Share
1.787∗∗∗
1.810∗∗∗
1.244∗∗∗(0.406)(0.406)(0.380)Panel E: Number of Buildings >= 30 Stories in 2011 (N: 3605)
Local Lords’ Estates Share
0.0748∗∗∗ 0.0741∗∗∗ 0.0581∗∗∗(0.0199)(0.0199)(0.0196)
Panel F: Log Land Price in 2012 (N: 3467)
Local Lords’ Estates Share
0.327∗∗∗
0.331∗∗∗
0.116(0.103)(0.102)(0.0810)
Log Land Price in 1876
No
Yes
No
Distance from the Center (Castle)
Yes
Yes
Yes
Mean of Altitude
Yes
Yes
Yes
S.D. of Altitude
Yes
Yes
Yes
Lon and Lat Controls
No
No
Yes
Earthquake Risk
No
No
Yes

(4)

-12.54∗∗∗(0.725)-17.18∗∗∗(3.610)-6.814∗∗∗(2.303)
1.270∗∗∗(0.382)
0.0568∗∗∗(0.0197)0.119(0.0787)
Yes
Yes
Yes
Yes
Yes
Yes

Standard errors are in parentheses. We allow a within-300 m correlation in theerror terms. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
See Table 1 for the deﬁnitions of the variables.

21

Table A.15: Controlling for Initial Land Price in 1876 (Local Randomization)
(1)

(2)

(3)

Panel I: Local Lords’ Estates Share (N: 319)
Local Lords’ Estates Zone
0.605∗∗∗ 0.518∗∗∗ 0.531∗∗∗(0.0734) (0.0823) (0.0617)
Panel A: Number of Lots in 1872 (N: 319)
Local Lords’ Estates Zone
-11.83∗∗∗ -7.893∗∗∗ -10.85∗∗∗(1.933)(1.762)(2.064)
Panel B: Number of Lots in 2008-2011 (N: 319)
Local Lords’ Estates Zone
-20.79∗∗∗-10.45-20.91∗∗∗(7.725)(7.195)(6.244)
Panel C: Number of Buildings in 2011 (N: 319)
Local Lords’ Estates Zone
-8.383∗-2.782-10.15∗∗∗(4.844)(4.812)(3.582)
Panel D: Stories (aboveground) in 2011 (N: 319)
Local Lords’ Estates Zone
1.925∗∗
1.038
1.745∗∗∗(0.803)(0.729)(0.665)
Panel E: Number of Buildings >= 30 Stories in 2011 (N: 319)
Local Lords’ Estates Zone
0.123∗∗
0.0654∗0.137∗∗∗(0.0519) (0.0367) (0.0468)
Panel F: Log Land Price in 2012 (N: 309)
Local Lords’ Estates Zone
0.141
-0.07430.397∗∗(0.356)(0.338)(0.184)
Log Land Price in 1876
No
Yes
No
Distance from the Center (Castle)
No
No
Yes
West of the Yamanote line
No
No
Yes
Mean of Altitude
No
No
Yes
S.D. of Altitude
No
No
Yes
Earthquake Risk
No
No
Yes

(4)

0.507∗∗∗(0.0631)-8.676∗∗∗(2.178)-16.02∗∗∗(5.774)-7.905∗∗(3.447)1.342∗∗(0.611)
0.0962∗∗(0.0377)
0.412∗∗(0.189)
Yes
Yes
Yes
Yes
Yes
Yes

Standard errors are in parentheses. We allow a within-300 m correlation in theerror terms. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
See Table 1 for the deﬁnitions of the variables.

22

Table A.16: Controlling for Destruction during WWII (OLS, Core vs. Non-core)

Inside vs Outside the Circle (Yamanote) Line
300 m

1000 m2000 mInsideOutsideInsideOutsideInsideOutside

(1)

(2)

(3)

(4)

(5)

(6)

-10.87∗∗∗(0.721)-7.696∗∗∗(1.204)-10.81∗∗∗(0.692)-6.241∗∗∗(1.228)-17.44∗∗∗(2.674)-2.753(4.206)-16.82∗∗∗(2.591)

0.959(5.307)-7.967∗∗∗(1.693)-0.468(3.384)-7.734∗∗∗(1.638)3.860(4.082)0.795∗∗(0.322)0.616(0.706)0.737∗∗(0.311)

0.349(0.789)0.0398∗∗∗(0.0153)0.00986(0.0248)0.0357∗∗(0.0145)0.0327(0.0222)0.181∗∗∗(0.0582)
Yes
Yes
Yes
Yes
Yes
Yes
4045
7160
7333
7333
7333
6855

-0.0000948(0.0402)
Yes
Yes
Yes
Yes
Yes
Yes
1485
1941
2209
2209
2209
2116

0.176∗∗∗(0.0582)
Yes
Yes
Yes
Yes
Yes
Yes
4702
7977
8315
8315
8315
7811

-0.0376(0.0386)
Yes
Yes
Yes
Yes
Yes
Yes
828
1124
1227
1227
1227
1160

Panel A: Number of Lots in 1872
Local Lords’ Estates Share-10.40∗∗∗ -9.442∗∗∗(0.736)(1.152)
Panel B: Number of Lots in 2008-2011
Local Lords’ Estates Share-19.16∗∗∗-3.914(2.648)(5.156)
Panel C: Number of Buildings in 2011
Local Lords’ Estates Share-9.306∗∗∗-0.659(1.678)(3.694)
Panel D: Stories (aboveground) in 2011
Local Lords’ Estates Share
0.995∗∗∗-0.0374(0.319)(0.603)
Panel E: Number of Buildings >= 30 Stories in 2011
Local Lords’ Estates Share
0.0429∗∗∗ -0.00198(0.0158) (0.0197)
Panel F: Log Land Price in 2012
Local Lords’ Estates Share
0.253∗∗∗-0.140∗∗(0.0507) (0.0685)
WWII Destruction ShareYes
Yes
Distance from the Center (Castle)
Yes
Yes
Mean of Altitude
Yes
Yes
S.D. of Altitude
Yes
Yes
Lon and Lat Controls
Yes
Yes
Earthquake Risk
Yes
Yes
N in Panel A
3555
1975
N in Panel B
6137
2964
N in Panel C
6144
3398
N in Panel D
6144
3398
N in Panel E
6144
3398
N in Panel F
5704
3267

Standard errors are in parentheses. We allow a within-300 m correlation in the error terms. ∗ p < 0.1,∗∗ p < 0.05, ∗∗∗ p < 0.01.

See Table 1 for the deﬁnitions of the variables.

23

Table A.17: Controlling for Destruction during WWII (Local Randomization, Core vs. Noncore)
(1)

(2)

(3)

Panel I: Local Lords’ Estates Share (N: 351)
Local Lords’ Estates Zone (Core)
0.564∗∗∗ 0.551∗∗∗ 0.474∗∗∗(0.107)(0.0847) (0.0951)
Local Lords’ Estates Zone (Non-core) 0.486∗∗∗ 0.496∗∗∗ 0.454∗∗∗(0.116)(0.127)(0.118)Panel A: Number of Lots in 1872 (N: 350)
Local Lords’ Estates Zone (Core)-13.77∗∗∗ -10.92∗∗∗ -10.97∗∗∗(1.351)(1.955)(1.791)
Local Lords’ Estates Zone (Non-core) -5.923∗∗∗ -9.415∗∗∗ -7.354∗∗∗(2.019)(2.328)(2.346)
Panel B: Number of Lots in 2008-2011 (N: 352)
Local Lords’ Estates Zone (Core)-40.04∗∗∗ -35.08∗∗∗ -33.55∗∗∗(6.403)(5.877)(6.919)Local Lords’ Estates Zone (Non-core)
3.812-6.879-5.884(7.729)(8.051)(8.041)Panel C: Number of Buildings in 2011 (N: 351)
Local Lords’ Estates Zone (Core)-20.70∗∗∗ -18.90∗∗∗ -19.38∗∗∗(3.238)(3.139)(3.542)
Local Lords’ Estates Zone (Non-core)
6.893
0.06351.083(5.586)(5.544)(5.476)
Panel D: Stories (aboveground) in 2011 (N: 351)
Local Lords’ Estates Zone (Core)3.306∗∗∗ 3.526∗∗∗2.840∗(1.048)(1.299)(1.577)
Local Lords’ Estates Zone (Non-core)-0.423-0.1420.415(0.634)(0.835)(0.754)
Panel E: Number of Buildings >= 30 Stories in 2011 (N: 351)
Local Lords’ Estates Zone (Core)
0.174∗∗∗ 0.214∗∗∗ 0.192∗∗∗(0.0591) (0.0703) (0.0704)
Local Lords’ Estates Zone (Non-core) -0.0320∗-0.04390.00214(0.0191) (0.0407) (0.0354)
Panel F: Log Land Price in 2012 (N: 341)
Local Lords’ Estates Zone (Core)
0.933∗∗∗ 1.087∗∗∗ 0.888∗∗∗(0.326)(0.235)(0.248)
Local Lords’ Estates Zone (Non-core) -0.868∗∗∗-0.311-0.210(0.301)(0.271)(0.272)
WWII Destruction
Yes
Yes
Yes
Distance from the Center (Castle)
No
Yes
Yes
West of the Yamanote line
No
Yes
Yes
Mean of Altitude
No
No
Yes
S.D. of Altitude
No
No
Yes
Earthquake Risk
No
No
Yes
Standard errors are in parentheses. We allow a within-300 m correlationin the error terms. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
See Table 1 for the deﬁnitions of the variables.

24

Table A.18: Controlling for Destruction during WWII (OLS, Before vs. After WWII)
(1)

(2)

Panel A: Number of Lots in 1912 (N: 8133)
Local Lords’ Estates Share-11.72∗∗∗ -10.72∗∗∗(0.547)(0.538)Panel B: Number of Lots in 1931 (N: 7830)
Local Lords’ Estates Share-9.238∗∗∗ -7.935∗∗∗(0.845)(0.829)Panel C: Log Land Price in 1912 (N: 7122)
Local Lords’ Estates Share-0.312∗∗∗ -0.334∗∗∗(0.103)(0.103)
Panel D: Log Land Price in 1931 (N: 4711)
Local Lords’ Estates Share-0.525∗∗∗ -0.512∗∗∗(0.119)(0.121)Panel E: Log Land Rental Price in 1931 (N: 7024)
Local Lords’ Estates Share-0.319∗∗∗ -0.309∗∗∗(0.0856) (0.0863)
Panel F: Log Land Price in 1972 (N: 6071)
Local Lords’ Estates Share-0.0615-0.0739(0.0482) (0.0460)
Panel G: Log Land Price in 1983 (N: 3276)
Local Lords’ Estates Share
0.05590.0288(0.0569) (0.0548)
Panel H: Log Land Price in 2012 (N: 8971)
Local Lords’ Estates Share
0.321∗∗∗ 0.281∗∗∗(0.0728) (0.0696)
WWII Destruction ShareNo
Yes
Distance from the Center (Castle)
Yes
Yes
Mean of Altitude
Yes
Yes
S.D. of Altitude
Yes
Yes
Lon and Lat Controls
No
No
Earthquake Risk
No
No

(3)

(4)

-11.72∗∗∗(0.547)-11.09∗∗∗(0.561)-9.238∗∗∗(0.845)-8.261∗∗∗(0.837)-0.312∗∗∗(0.103)-0.509∗∗∗(0.101)-0.525∗∗∗(0.119)-0.686∗∗∗(0.112)-0.319∗∗∗(0.0856)-0.390∗∗∗(0.0803)-0.0615(0.0482)-0.0527(0.0408)0.0559(0.0569)-0.00415(0.0447)0.321∗∗∗(0.0728)
No
Yes
Yes
Yes
No
No

0.192∗∗∗(0.0552)
Yes
Yes
Yes
Yes
Yes
Yes

Standard errors are in parentheses. We allow a within-300 m correlation in theerror terms. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
See Table 1 for the deﬁnitions of the variables.

25

Table A.19: Controlling for Destruction during WWII (Local Randomization, Before vs. After
WWII)
(1)

(2)

Panel A: Number of Lots in 1912 (N: 343)
Local Lords’ Estates Zone
-11.03∗∗∗ -8.972∗∗∗(1.781)(1.605)
Panel B: Number of Lots in 1931 (N: 347)
Local Lords’ Estates Zone
-10.55∗∗∗ -9.112∗∗∗(1.737)(1.901)Panel C: Log Land Price in 1912 (N: 294)
Local Lords’ Estates Zone
-0.873∗∗∗ -0.839∗∗∗(0.241)(0.250)Panel D: Log Land Price in 1931 (N: 268)
Local Lords’ Estates Zone
-0.604∗∗∗ -0.563∗∗(0.228)(0.245)
Panel E: Log Land Rental Price in 1931 (N: 299)
Local Lords’ Estates Zone
-0.331∗∗-0.261(0.152)(0.162)
Panel F: Log Land Price in 1972 (N: 279)
Local Lords’ Estates Zone
0.05250.0639(0.146)(0.135)
Panel G: Log Land Price in 1983 (N: 157)
Local Lords’ Estates Zone
0.148
0.141
(0.134)(0.133)
Panel H: Log Land Price in 2012 (N: 341)
Local Lords’ Estates Zone
0.443∗
0.428∗(0.244)(0.221)
WWII Destruction
No
Yes
Distance from the Center (Castle)
Yes
YesWest of the Yamanote line
Yes
Yes
Mean of Altitude
No
No
S.D. of Altitude
No
No
Earthquake Risk
No
No

(3)

(4)

-9.991∗∗∗(2.095)-8.201∗∗∗(1.802)-9.672∗∗∗(2.021)-8.386∗∗∗(2.085)-0.709∗∗∗(0.242)-0.698∗∗∗(0.254)-0.487∗∗(0.234)-0.443∗(0.245)-0.342∗(0.180)-0.271(0.183)-0.0439(0.157)-0.00818(0.154)0.103(0.144)0.117(0.152)

0.343∗(0.202)
No
Yes
Yes
Yes
Yes
Yes

0.361∗(0.197)
Yes
Yes
Yes
Yes
Yes
Yes

Standard errors are in parentheses. We allow a within-300 m correlation in theerror terms. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
See Table 1 for the deﬁnitions of the variables.

26

Table A.20: Controlling for Land Use/Ownership in 1931 (OLS, Before vs. After WWII)
(1)

(2)

Panel A: Number of Lots in 1912 (N: 7319)
Local Lords’ Estates Share-12.02∗∗∗-11.79∗∗∗(0.572)(0.578)Panel B: Number of Lots in 1931 (N: 7830)
Local Lords’ Estates Share-9.431∗∗∗-9.403∗∗∗(0.878)(0.889)Panel C: Log Land Price in 1912 (N: 6552)
Local Lords’ Estates Share-0.584∗∗∗-0.596∗∗∗(0.105)(0.105)
Panel D: Log Land Price in 1931 (N: 4711)
Local Lords’ Estates Share-0.769∗∗∗-0.792∗∗∗(0.110)(0.110)Panel E: Log Land Rental Price in 1931 (N: 7024)
Local Lords’ Estates Share-0.466∗∗∗-0.480∗∗∗(0.0811)(0.0817)
Panel F: Log Land Price in 1972 (N: 5080)
Local Lords’ Estates Share-0.0448-0.0439(0.0413)(0.0414)
Panel G: Log Land Price in 1983 (N: 2770)
Local Lords’ Estates Share-0.0000857 0.00467(0.0445)(0.0448)
Panel H: Log Land Price in 2012 (N: 7332)
Local Lords’ Estates Share
0.171∗∗∗
0.172∗∗∗(0.0556)(0.0557)
Remaining Estates Share in 1931
Yes
No
Other Lords’ Land Share in 1931
No
Yes
Military Use Share in 1931
No
No
Distance from the Center (Castle)
Yes
Yes
Mean of Altitude
Yes
Yes
S.D. of Altitude
Yes
Yes
Lon and Lat Controls
Yes
Yes
Earthquake Risk
Yes
Yes

(3)

(4)

-11.93∗∗∗(0.589)-11.64∗∗∗(0.595)-8.895∗∗∗(0.907)-8.887∗∗∗(0.927)-0.539∗∗∗(0.102)-0.566∗∗∗(0.105)-0.684∗∗∗(0.109)-0.747∗∗∗(0.110)-0.382∗∗∗(0.0794)-0.462∗∗∗(0.0817)-0.0447(0.0406)-0.0403(0.0422)0.000961(0.0436)0.00953(0.0455)

0.169∗∗∗(0.0558)
No
No
Yes
Yes
Yes
Yes
Yes
Yes

0.178∗∗∗(0.0576)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes

Standard errors are in parentheses. We allow a within-300 m correlation in theerror terms. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
See Table 1 for the deﬁnitions of the variables. Remaining Estates Share in 1931is the share of land used as an estate for the descendants of local lords in 1931.
Other Lords’ Land Share in 1931 is the share of land owned by the descendants
of local lords, not as their estate, in 1931. Military Use Share in 1931 is theshare of land used for military purposes in 1931.

27

Table A.21: Controlling for Land Use/Ownership in 1931 (Local Randomization, Before vs.
After WWII)
(1)

(2)

Panel I: Local Lords’ Estates Share (N: 347)
Local Lords’ Estates Zone
0.523∗∗∗ 0.507∗∗∗(0.0829) (0.0762)
Panel A: Number of Lots in 1912 (N: 339)
Local Lords’ Estates Zone
-11.18∗∗∗ -10.66∗∗∗(2.089)(1.866)
Panel B: Number of Lots in 1931 (N: 347)
Local Lords’ Estates Zone
-9.823∗∗∗ -10.67∗∗∗(2.602)(1.805)
Panel C: Log Land Price in 1912 (N: 293)
Local Lords’ Estates Zone
-0.786∗∗∗ -0.777∗∗∗(0.220)(0.254)
Panel D: Log Land Price in 1931 (N: 268)
Local Lords’ Estates Zone
-0.737∗∗∗ -0.647∗∗∗(0.194)(0.235)
Panel E: Log Land Rental Price in 1931 (N: 299)
Local Lords’ Estates Zone
-0.493∗∗∗ -0.353∗∗(0.156)(0.158)Panel F: Log Land Price in 1972 (N: 279)
Local Lords’ Estates Zone
-0.2500.0736(0.236)(0.149)Panel G: Log Land Price in 1983 (N: 157)
Local Lords’ Estates Zone
-0.08960.167(0.224)(0.138)Panel H: Log Land Price in 2012 (N: 339)
Local Lords’ Estates Zone
0.213
0.481∗∗(0.334)(0.241)
Remaining Estates Share in 1931
Yes
Yes
Distance from the Center (Castle)No
YesWest of the Yamanote lineNo
Yes
Mean of Altitude
No
No
S.D. of Altitude
No
No
Earthquake Risk
No
No

(3)

0.440∗∗∗(0.0839)-9.681∗∗∗(2.152)-9.798∗∗∗(2.096)-0.622∗∗(0.251)-0.532∗∗(0.239)-0.362∗∗(0.182)-0.0298(0.156)0.114(0.146)0.365∗(0.202)
Yes
Yes
Yes
Yes
Yes
Yes

Standard errors are in parentheses. We allow a within-300 m correlation in the error terms. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
See Table 1 for the deﬁnitions of the variables. Remaining Estates
Share in 1931 is the share of land used as an estate for the descendants of local lords in 1931.
There was no other lords’ land other than their estate or military
land in 1931 in this sample.
28

Table A.22: Controlling for Land Use/Ownership in 1931 (OLS, Core vs. Non-core)

Inside vs Outside the Circle (Yamanote) Line
300 m

2000 m1000 mInsideOutsideInsideOutsideInsideOutside

(1)

(2)

(3)

(4)

(5)

(6)

-12.18∗∗∗(0.724)-7.340∗∗∗(1.114)-12.02∗∗∗(0.702)-5.734∗∗∗(1.384)-21.67∗∗∗(2.910)-5.868(4.230)-20.74∗∗∗(2.797)0.218(5.477)-10.47∗∗∗(1.869)-4.061(3.408)-10.20∗∗∗(1.795)

3.259(4.223)

1.068∗∗∗(0.343)0.798(0.641)1.037∗∗∗(0.327)0.192(0.737)0.0502∗∗∗(0.0169)

0.0178(0.0198)

0.0459∗∗∗(0.0159)

0.0246(0.0169)

0.187∗∗∗(0.0617)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
3822
5955
5922
5922
5922
5596

0.0166(0.0384)
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
1470
1828
1804
1804
1804
1736

0.178∗∗∗(0.0601)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
4470
6700
6663
6663
6663
6314

-0.0391(0.0323)
No
Yes
No
Yes
Yes
Yes
Yes
Yes
822
1083
1063
1063
1063
1018

Panel A: Number of Lots in 1872
Local Lords’ Estates Share-11.58∗∗∗ -9.209∗∗∗(0.802)(1.145)
Panel B: Number of Lots in 2008-2011
Local Lords’ Estates Share-23.29∗∗∗-5.655(2.934)(4.367)
Panel C: Number of Buildings in 2011
Local Lords’ Estates Share-11.80∗∗∗-2.981(1.869)(3.288)Panel D: Stories (aboveground) in 2011
Local Lords’ Estates Share
1.147∗∗∗
0.555(0.345)(0.572)
Panel E: Number of Buildings >= 30 Stories in 2011
Local Lords’ Estates Share
0.0499∗∗∗0.0149(0.0175) (0.0166)
Panel F: Log Land Price in 2012
Local Lords’ Estates Share
0.252∗∗∗-0.101∗(0.0542) (0.0605)
Remaining Estates Share in 1931
Yes
No
Other Lords’ Land Share in 1931
Yes
Yes
Military Use Share in 1931
Yes
Yes
Distance from the Center (Castle)
Yes
Yes
Mean of Altitude
Yes
Yes
S.D. of Altitude
Yes
Yes
Lon and Lat Controls
Yes
Yes
Earthquake Risk
Yes
Yes
N in Panel A
3366
1926
N in Panel B
5269
2514
N in Panel C
5237
2489
N in Panel D
5237
2489
N in Panel E
5237
2489
N in Panel F
4925
2407

Standard errors are in parentheses. We allow a within-300 m correlation in the error terms. ∗ p < 0.1,∗∗ p < 0.05, ∗∗∗ p < 0.01.

See Table 1 for the deﬁnitions of the variables. Remaining Estates Share in 1931 is the share of land
used as an estate for the descendants of local lords in 1931. Other Lords’ Land Share in 1931 is theshare of land owned by the descendants of local lords, not as their estate, in 1931. Military Use Sharein 1931 is the share of land used for military purposes in 1931.
Military Use Share in 1931 takes zero in column (6), and Other Lords’ Land Share in 1931 takes zeroin columns (4) and (6).

29

Table A.23: Controlling for Land Use/Ownership in 1931 (Local Randomization, Core vs.
Non-core)(1)
Panel I: Local Lords’ Estates Share (N: 347)
Local Lords’ Estates Zone (Core)
0.563∗∗∗(0.108)
Local Lords’ Estates Zone (Non-core) 0.455∗∗∗(0.117)
Panel A: Number of Lots in 1912 (N: 339)
Local Lords’ Estates Zone (Core)-15.17∗∗∗(1.738)
Local Lords’ Estates Zone (Non-core) -4.710∗∗(1.829)Panel B: Number of Lots in 1931 (N: 347)
Local Lords’ Estates Zone (Core)-14.53∗∗∗(2.761)
Local Lords’ Estates Zone (Non-core)-1.602(2.592)
Panel C: Log Land Price in 1912 (N: 293)
Local Lords’ Estates Zone (Core)-1.160∗∗∗(0.350)Local Lords’ Estates Zone (Non-core) -0.399∗∗∗(0.135)
Panel D: Log Land Price in 1931 (N: 268)
Local Lords’ Estates Zone (Core)-1.040∗∗∗(0.298)
Local Lords’ Estates Zone (Non-core) -0.457∗∗∗(0.148)Panel E: Log Land Rental Price in 1931 (N: 299)
Local Lords’ Estates Zone (Core)-0.590∗∗∗(0.224)
Local Lords’ Estates Zone (Non-core) -0.389∗∗(0.171)Panel F: Log Land Price in 2012 (N: 339)
Local Lords’ Estates Zone (Core)
0.906∗∗∗(0.323)
Local Lords’ Estates Zone (Non-core) -0.911∗∗∗(0.302)
Remaining Estates Share in 1931
Yes
Distance from the Center (Castle)
No
West of the Yamanote line
No
Mean of Altitude
No
S.D. of Altitude
No
Earthquake Risk
No

(2)

(3)

0.544∗∗∗(0.0804)0.450∗∗∗(0.130)0.460∗∗∗(0.0904)
0.412∗∗∗(0.123)-12.68∗∗∗(1.926)-7.785∗∗∗(2.701)-12.76∗∗∗(1.866)-5.969∗∗(2.852)-12.63∗∗∗(2.435)-7.658∗∗(3.176)-12.51∗∗∗(2.805)-6.140∗∗(2.907)-0.980∗∗(0.388)-0.516∗(0.280)-0.778∗∗(0.391)-0.447∗(0.231)-1.060∗∗∗(0.342)-0.332(0.214)-1.093∗∗∗(0.321)-0.0690(0.156)-0.435∗(0.239)-0.249(0.181)-0.526∗∗(0.254)-0.182(0.185)1.030∗∗∗(0.227)-0.317(0.278)
Yes
Yes
Yes
No
No
No

0.819∗∗∗(0.230)-0.219(0.277)
Yes
Yes
Yes
Yes
Yes
Yes

Standard errors are in parentheses. We allow a within-300 m correlationin the error terms. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
See Table 1 for the deﬁnitions of the variables. Remaining Estates Sharein 1931 is the share of land used as an estate for the descendants oflocal lords in 1931.
There was no other lords’ land other than their estate or military landin 1931 in this sample.
30

Table A.24: Quantile Regressions on Firm Productivity with Public Infrastructure (OLS Sample)
Log Revenue / Worker
Percentiles
10

25

50

75

90

(1)

(2)

(3)

(4)

(5)

Results in 2017
Panel A: Geographical Controls, Industry Fixed Eﬀects, and Public Amenities
Local Lords’ Estates Share
0.02600.0445∗∗∗ 0.0464∗∗∗ 0.0524∗∗∗ 0.0784∗∗∗(0.0167)(0.0107)(0.0115)(0.0143)(0.0186)
Panel B: Panel A with Controlling for Stories
Local Lords’ Estates Share
0.01170.0359∗∗∗ 0.0271∗∗
0.0264∗0.0507∗∗∗(0.0158)(0.0105)(0.0119)(0.0147)(0.0195)
Results in 1993
Panel C: Geographical Controls, Industry Fixed Eﬀects, and Public Amenities
Local Lords’ Estates Share -0.000172 0.00739
0.00930 0.0361∗∗∗ 0.0368∗∗(0.0128)(0.0110)(0.0104)(0.0124)(0.0163)
Panel D: Panel C with Controlling for Stories
Local Lords’ Estates Share -0.00138
0.00506
0.00251
0.0261∗∗0.0203(0.0119)(0.0110)(0.0102)(0.0127)(0.0165)
Robust standard errors are in parentheses. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
In Panels A and C, we control for the geographical variables used in the main speciﬁcation, Distance from the Center (Castle), Mean of Altitude, S.D. of Altitude, twodigit-level industry ﬁxed eﬀects, and Road Width, Hospital, University, and Parks
Share, and Distance to Nearest Station in 2018 and 1950. We add the mean of stories(aboveground) as an additional control in Panels B and D.
The number of observations (ﬁrms) in Panels A–C (Panels D–F) is 79471 (84617).

31

Table A.25: Quantile Regressions on Firm Productivity with Public Infrastructure (Local
Randomization)
Log Revenue / Worker
Percentiles
10

25

50

75

90

(1)

(2)

(3)

(4)

(5)

Results in 2017
Panel A: Geographical Controls, Industry Fixed Eﬀects, and Public Amenities
Local Lords’ Estates Zone -0.0261
0.04310.0744∗∗∗ 0.120∗∗∗ 0.153∗∗∗(0.0452) (0.0316)(0.0280) (0.0351) (0.0462)
Panel B: Panel A with Controlling for Stories
Local Lords’ Estates Zone 0.0117 0.0359∗∗∗ 0.0271∗∗
0.0264∗ 0.0507∗∗∗(0.0158) (0.0105)(0.0119) (0.0147) (0.0195)
Results in 1993
Panel C: Geographical Controls, Industry Fixed Eﬀects, and Public Amenities
Local Lords’ Estates Zone -0.0215-0.00378
0.01450.04690.0780∗∗(0.0303) (0.0232)(0.0319) (0.0316) (0.0364)
Panel D: Panel C with Controlling for Stories
Local Lords’ Estates Zone -0.0194-0.00775
0.0227
0.0566∗0.0810∗∗(0.0335) (0.0251)(0.0296) (0.0307) (0.0343)
Robust standard errors are in parentheses. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
In Panels A and C, we control for the geographical variables used in the main
speciﬁcation, Distance from the Center (Castle), Mean of Altitude, S.D. of Altitude, West of the Yamanote Line, Earthquake Risk, two-digit-level industry ﬁxed
eﬀects, Road Width, Hospital, University, and Parks Share, and Distance to Nearest Station in 2018 and 1950. We add the mean of stories (aboveground) as anadditional control in Panels B and D.
The number of observations (ﬁrms) in Panels A and B (Panels C and D) is 7397
(8137).

32

Table A.26: Selection Channel
(1)

(2)

(3)

Panel A: Sorting of Movers∆ Local Lords’ Estates
Log Revenue per Worker in 1993

0.000819(0.00209)
No
25542

Industry FEs
Observation (Firm)

0.00102(0.00246)
Yes
25542

Panel B: Entrant (Outcome: Entrant Dummy)
Sample: Full in 2017
Local Lords’ Estates ShareLog Revenue per Worker in 2017
Local Lords’ Estates Share * Log Revenue per Worker in 2017
Geographical Controls (* Log Revenue per Worker in 2017)
Industry FEs
Local Lords’ Estates Share * Industry FEs
Observation (Firm)

0.0116(0.0135)-0.0576∗∗∗(0.00360)
0.00619(0.00388)
Yes
No
No
80473

0.00484(0.0131)-0.0244∗∗∗(0.00357)
0.00467(0.00376)
Yes
Yes
No
80473

-0.0243∗∗∗(0.00361)
0.00345(0.00417)
Yes
Yes
Yes
80473

Panel C: Exiter (Outcome: Exiter Dummy)
Sample: Full in 1993
Local Lords’ Estates ShareLog Revenue per Worker in 1993
Local Lords’ Estates Share * Log Revenue per Worker in 1993
Geographical Controls (* Log Revenue per Worker in 1993)
Industry FEs
Local Lords’ Estates Share * Industry FEs
Observation (Firm)

0.00486(0.0159)-0.0103∗∗∗(0.00386)
0.00140(0.00423)
Yes
No
No
85310

0.000869(0.0158)-0.0156∗∗∗(0.00392)
0.00265(0.00421)
Yes
Yes
No
85310

-0.0151∗∗∗(0.00396)
0.000511(0.00476)
Yes
Yes
Yes
85310

Robust standard errors are in parentheses. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
∆ indicates the variable in 2017 minus 1993. Panel A uses ﬁrms that change their cells within our
sample area. In Panel B (C), the outcome variable is a dummy variable that takes the value one if
a ﬁrm exists only in 2017 (1993). Geographical Controls contain Distance from the Center (Castle),
Mean of Altitude, and S.D. of Altitude.

33

Table A.27: Selection Channel (Local Randomization)
(1)

(2)

(3)

Panel A: Sorting of Movers∆ Local Lords’ Estates Zone
Log Revenue per Worker in 1993

0.0105(0.00834)
No
2436

Industry FEs
Observation (Firm)

0.00955(0.0105)
Yes
2436

Panel B: Entrant (Outcome: Entrant Dummy)
Sample: Full in 2017-0.0452∗

Local Lords’ Estates Zone
Log Revenue per Worker in 2017
Local Lords’ Estates Zone * Log Revenue per Worker in 2017
Geographical Controls (* Log Revenue per Worker in 2017)
Industry FEs
Local Lords’ Estates * Industry FEs
Observation (Firm)(0.0268)-0.0116(0.0356)
0.0128∗(0.00759)
Yes
No
No
7491

-0.0464∗(0.0265)-0.0194(0.0350)
0.0137∗(0.00752)
Yes
Yes
No
7491

-0.00929(0.0340)-0.0219(0.0352)
0.0130(0.00813)
Yes
Yes
Yes
7491

Panel C: Exiter (Outcome: Exiter Dummy)
Sample: Full in 1993
Local Lords’ Estates Zone
Log Revenue per Worker in 2017
Local Lords’ Estates Zone * Log Revenue per Worker in 1993
Geographical Controls (* Log Revenue per Worker in 1993)
Industry FEs
Local Lords’ Estates * Industry FEs
Stories in 1991 * Industry FEs
Observation (Firm)

0.0223(0.0354)0.0139(0.0421)-0.00164(0.00916)
Yes
No
No
No
8206

0.0181(0.0355)-0.00866(0.0430)-0.00165(0.00924)
Yes
Yes
No
No
8206

-0.376(0.278)-0.00853(0.0433)-0.00215(0.0103)
Yes
Yes
Yes
No
8206

Robust standard errors are in parentheses. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
∆ indicates the variable in 2017 minus 1993. For stories, we use the number of stories aboveground in2011 and 1991, respectively. Panel A uses ﬁrms that change their located cells within our sample area
for the local randomization analysis. In Panel B (C), the outcome variable is a dummy variable thattakes the value one if a ﬁrm exists only in 2017 (1993). Geographical Controls contains Distance fromthe Center (Castle), West of the Yamanote Line, Mean of Altitude and S.D. of Altitude.

34

Table A.28: The Eﬀect on Buildings in These Recent 25 Years (OLS)
(1)

(2)

(3)

(4)

Panel A: Number of Buildings in 1986 (N: 9566)
Local Lords’ Estates Share-14.22∗∗∗ -12.52∗∗∗ -11.27∗∗∗-11.29∗∗∗(1.729)(1.669)(1.695)(1.688)Panel B: Number of Buildings in 2001 (N: 9567)
Local Lords’ Estates Share-13.61∗∗∗ -12.01∗∗∗ -11.00∗∗∗-10.97∗∗∗(1.731)(1.697)(1.716)(1.712)
Panel C: Number of Buildings in 2011 (N: 9542)
Local Lords’ Estates Share-13.06∗∗∗ -11.61∗∗∗ -10.39∗∗∗-10.36∗∗∗(1.675)(1.654)(1.651)(1.652)
Panel D: Stories (including underground) in 1986 (N: 9566)
Local Lords’ Estates Share
0.193
0.203
0.276
0.238
(0.199)(0.203)(0.190)(0.185)
Panel E: Stories (including underground) in 2001 (N: 9567)
Local Lords’ Estates Share
0.453
0.509∗
0.619∗∗
0.574∗∗(0.301)(0.307)(0.278)(0.270)Panel F: Stories (including underground) in 2011 (N: 9542)
Local Lords’ Estates Share
0.914∗∗
0.997∗∗
1.043∗∗∗
0.964∗∗∗(0.386)(0.390)(0.349)(0.332)
Panel G: Number of Buildings >= 30 Stories (including underground) in 1986 (N: 9329)
Local Lords’ Estates Share
0.0132∗∗0.0125∗∗0.0144∗∗0.0137∗∗(0.00604) (0.00604) (0.00601)(0.00571)
Panel H: Number of Buildings >= 30 Stories (including underground) in 2001 (N: 9472)
Local Lords’ Estates Share
0.0291∗∗∗ 0.0280∗∗∗ 0.0300∗∗∗
0.0297∗∗∗(0.0106)(0.0107)(0.0105)(0.0105)
Panel I: Number of Buildings >= 30 Stories (including underground) in 2011 (N: 9542)
Local Lords’ Estates Share
0.0412∗∗0.0401∗∗0.0400∗∗0.0376∗∗(0.0161)(0.0166)(0.0159)(0.0152)
Distance from the Center (Castle)
Yes
Yes
Yes
Yes
Mean of Altitude
No
Yes
Yes
Yes
S.D. of Altitude
No
Yes
Yes
Yes
Lon and Lat Controls
No
No
Yes
Yes
Earthquake Risk
No
No
No
Yes
Standard errors are in parentheses. We allow a within-300 m correlation in the error
terms. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
See Table 1 for the deﬁnitions of the variables.

35

Table A.29: The Eﬀect on Buildings in These Recent 25 Years (Local Randomization)
(1)

(2)

(3)

Panel I: Number of Buildings in 1986 (N: 351)
Local Lords’ Estates Zone
-8.518∗-9.449∗∗-9.461∗∗(4.919)(4.036)(3.809)Panel A: Number of Buildings in 2001 (N: 352)
Local Lords’ Estates Zone
-11.75∗∗∗ -12.66∗∗∗-12.39∗∗∗(4.531)(3.876)(3.799)Panel B: Number of Buildings in 2011 (N: 351)
Local Lords’ Estates Zone
-10.06∗∗ -11.16∗∗∗-10.60∗∗∗(4.446)(3.866)(3.656)
Panel C: Stories (including underground) in 1986 (N: 351)
Local Lords’ Estates Zone
0.696
0.639
0.272
(0.748)(0.658)(0.595)
Panel D: Stories (including underground) in 2001 (N: 352)
Local Lords’ Estates Zone
1.535∗
1.314
0.490(0.918)(0.855)(0.697)
Panel E: Stories (including underground) in 2011 (N: 351)
Local Lords’ Estates Zone
2.605∗∗∗
2.819∗∗∗
2.361∗∗(0.891)(0.847)(1.025)
Panel F: Number of Buildings >= 30 Stories (including underground) in 1986 (N: 348)
Local Lords’ Estates Zone
0.02070.00649-0.00830(0.0202) (0.00974)(0.00756)
Panel G: Number of Buildings >= 30 Stories (including underground) in 2001 (N: 349)
Local Lords’ Estates Zone
0.0420∗0.0353∗0.0177(0.0242) (0.0190)(0.0164)
Panel H: Number of Buildings >= 30 Stories (including underground) in 2011 (N: 351)
Local Lords’ Estates Zone
0.124∗∗
0.144∗∗
0.140∗∗∗(0.0498) (0.0601)(0.0538)
Distance from the Center (Castle)
No
Yes
Yes
West of the Yamanote line
No
Yes
Yes
Mean of Altitude
No
No
Yes
S.D. of Altitude
No
No
Yes
Earthquake Risk
No
No
Yes
Standard errors are in parentheses. We allow a within-300 m correlation in the error
terms. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
See Table 1 for the deﬁnitions of the variables.

36

Table A.30: Business and Residential Zones

Business Zones(1)
Panel A: Number of Lots in 1872
Local Lords’ Estates Share(2)-15.01∗∗∗ -14.26∗∗∗(1.044)(1.057)
Panel B: Number of Lots in 2008-2011
Local Lords’ Estates Share-29.88∗∗∗ -26.30∗∗∗(4.415)(4.430)Panel C: Number of Buildings in 2011
Local Lords’ Estates Share-13.80∗∗∗ -11.57∗∗∗(2.600)(2.624)
Panel D: Stories (aboveground) in 2011
Local Lords’ Estates Share
3.747∗∗∗ 3.827∗∗∗(0.973)(0.851)Panel E: Number of Buildings >= 30 Stories in 2011
Local Lords’ Estates Share
0.148∗∗∗ 0.154∗∗∗(0.0473) (0.0437)
Panel F: Log Land Price in 2012
Local Lords’ Estates Share
0.252
0.125
(0.167)(0.127)Distance from the Center (Castle)
Yes
Yes
Mean of Altitude
Yes
Yes
S.D. of Altitude
Yes
Yes
Lon and Lat Controls
No
Yes
Earthquake Risk
No
Yes
N in Panel A
726
726
N in Panel B
1006
1006
N in Panel C
1018
1018
N in Panel D
1018
1018
N in Panel E
1018
1018
954
N in Panel F
954

Residential Zones
(3)

(4)

-11.68∗∗∗(1.143)-11.66∗∗∗(1.284)-24.50∗∗∗(4.196)-17.98∗∗∗(4.512)-16.89∗∗∗(2.928)-14.27∗∗∗(2.929)0.519(0.400)0.630(0.438)0.0163(0.0169)

0.00895(0.0210)0.342∗∗∗(0.0496)
Yes
Yes
Yes
No
No
572
1519
1738
1738
1738
1720

0.171∗∗∗(0.0344)
Yes
Yes
Yes
Yes
Yes
572
1519
1738
1738
1738
1720

Standard errors are in parentheses. We allow a within-300 m correlation in theerror terms. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
See Table 1 for the deﬁnitions of the variables.
Columns (1) and (2) (columns (3) and (4)) use only cells for which more thanhalf the area is used for business (residential) purposes.

37

Table A.31: Quantile Regressions on Firm Productivity (OLS Sample)
Log Revenue / Worker
Percentiles
10

25

50

75

90

(1)

(2)

(3)

(4)

(5)

0.0356∗(0.0182)

0.0662∗∗(0.0269)

0.0506∗∗∗(0.0144)0.0860∗∗∗(0.0206)

0.0193(0.0148)0.0499∗∗(0.0198)

0.0283∗(0.0153)0.0247(0.0271)0.0294∗∗(0.0120)0.0385∗∗(0.0160)

0.0303∗∗(0.0121)0.0196(0.0164)

Results in 2017
Panel A: Baseline Results with Geographical Controls
Local Lords’ Estates Share 0.0327∗
0.0250∗0.0182(0.0191) (0.0135)(0.0153)
Panel B: Panel A with Industry Fixed Eﬀects
Local Lords’ Estates Share 0.0359∗∗ 0.0519∗∗∗ 0.0459∗∗∗(0.0158) (0.0111)(0.0108)
Panel C: Panel B with Controlling for Stories
Local Lords’ Estates Share
0.02030.0387∗∗∗ 0.0259∗∗∗(0.0157) (0.0105) (0.00927)
Results in 1993
Panel D: Baseline Results with Geographical Controls
Local Lords’ Estates Share 0.00572-0.0100
0.00231(0.0164) (0.0138)(0.0134)
Panel E: Panel D with Industry Fixed Eﬀects
Local Lords’ Estates Share 0.00532
0.0177∗0.0165(0.0125) (0.0104)(0.0102)
Panel F: Panel E with Controlling for Stories
Local Lords’ Estates Share 0.00451
0.01640.0113(0.0114) (0.0105) (0.00991)

Robust standard errors are in parentheses. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
In Panels A and C, we control for the geographical variables used in the main speciﬁcation, Distance from the Center (Castle), Mean of Altitude, and S.D. of Altitude. In
Panels B and D, we also include two-digit-level industry ﬁxed eﬀects, corresponding
to Figure 9(a). We add the mean of stories (aboveground) as an additional control
in Panels C and F, corresponding to Figure 9(b).
The number of observations (ﬁrms) in Panels A–C (Panels D–F) is 80473 (85310).

38

Table A.32: Quantile Regressions on Firm Productivity (Local Randomization)
Log Revenue / Worker
Percentiles
10

25

50

75

90

(1)

(2)

(3)

(4)

(5)

0.115∗∗(0.0512)0.130(0.0804)

0.135∗∗∗(0.0343)

0.187∗∗∗(0.0276)0.0290(0.0372)

0.0737∗(0.0383)-0.0754(0.0477)0.0411(0.0845)0.0708∗∗(0.0292)

0.0959∗∗∗(0.0365)0.0595∗∗(0.0288)

0.0989∗∗∗(0.0359)

Results in 2017
Panel A: Baseline Results with Geographical Controls
Local Lords’ Estates Zone 0.0188
0.0766∗∗
0.0657(0.0458) (0.0336) (0.0432)
Panel B: Panel A with Industry Fixed Eﬀects
Local Lords’ Estates Zone 0.0168 0.0750∗∗∗ 0.111∗∗∗(0.0358) (0.0287) (0.0260)
Panel C: Panel B with Controlling for Stories
Local Lords’ Estates Zone -0.0474-0.01290.0295(0.0375) (0.0296) (0.0290)
Results in 1993
Panel D: Baseline Results with Geographical Controls
Local Lords’ Estates Zone 0.00147-0.0268-0.0511(0.0353) (0.0370) (0.0434)
Panel E: Panel D with Industry Fixed Eﬀects
Local Lords’ Estates Zone 0.0271
0.006700.0305(0.0298) (0.0231) (0.0280)
Panel F: Panel E with Controlling for Stories
Local Lords’ Estates Zone 0.0222
0.006230.0214(0.0221) (0.0261) (0.0286)

Robust standard errors are in parentheses. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
In Panels A and C, we control for the geographical variables used in the main
speciﬁcation, Distance from the Center (Castle), Mean of Altitude, S.D. of Altitude, West of the Yamanote Line, and Earthquake Risk. In Panels B and D, wealso include two-digit-level industry ﬁxed eﬀects, corresponding to Figure 9(c).
We add the mean of stories (aboveground) as an additional control in Panels C
and F, corresponding to Figure 9(d).
The number of observations (ﬁrms) in Panels A, B, and C (Panels D, E, and F)
is 7491 (8206).

39

Table A.33: Using the Number of Lots in 1872 as the Treatment Variable
(1)

(2)

Panel A: Number of Lots in 2008-2011 (N: 5477)
Number of Lots in 1872
1.739∗∗∗
1.448∗∗∗(0.248)(0.251)Panel B: Number of Buildings in 2011 (N: 5407)
Number of Lots in 1872
0.998∗∗∗
0.594∗∗∗(0.158)(0.155)
Panel C: Stories (aboveground) in 2011 (N: 5407)
Number of Lots in 1872
-0.158∗∗∗-0.0955∗∗∗(0.0407)(0.0346)
Panel D: Number of Buildings >= 30 Stories in 2011 (N: 5407)
Number of Lots in 1872
-0.00609∗∗∗-0.00459∗∗∗(0.00184)(0.00167)
Panel E: Log Land Price in 2012 (N: 5078)
Number of Lots in 1872
-0.0390∗∗∗-0.0127∗∗(0.00797)(0.00564)
Distance from the Center (Castle)
Yes
Yes
Mean of Altitude
Yes
Yes
S.D. of Altitude
Yes
Yes
Lon and Lat Controls
No
Yes
Earthquake Risk
No
YesStandard errors are in parentheses. We allow a within-300 m
correlation in the error terms. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗p < 0.01.
See Table 1 for the deﬁnitions of the variables. We use Local
Lords’ Estates Share as an instrumental variable.

40