181
May 2023

Path-dependence in the location of businessagglomeration: Case of postwar land requisition

Keisuke Takano(TDB Center for Advanced Empirical Research on Enterprise and Economy,
Hitotsubashi University; Center for Spatial Information Science, The Universityof Tokyo)

Path-dependence in the location of business agglomeration: Case of postwar land
requisition

Keisuke Takano
TDB Center for Advanced Empirical Research on Enterprise and Economy, Hitotsubashi University
2-1, Naka, Kunitachi, Tokyo, Japana183056c [at] r.hit-u.ac.jp

ABSTRACTIs the spatial distribution of business activity uniquely determined? This paper sheds light on
this question by investigating the impact of land requisition by the US Army during the postWW2 period in Yokohama City, Japan. Using the newly digitized location information of firms
before and after WW2, this study examines whether the forced relocation of firms persistently
altered the distribution of business activities. I show that land requisition reduces the size ofincumbent agglomerations, and this negative effect lasted even after the occupied land was
retroceded. I additionally show that the within-city relocation and the failure of public
investment drove the decline of the agglomeration. My findings imply the location of economic
agglomeration is not necessarily uniquely determined.

Keywords: path dependence, multiple equilibria, urban land use, requisition
JEL code: R12, R52, R58, N95

11. Introduction
Agglomeration of industry and population is a crucial driver of urban economic growth.
However, owing to its place-based nature, it often faces local negative shocks, such as manmade and natural disasters and other various economic shocks. Therefore, it is important tounderstand the shock sensitivity of agglomeration — whether and how agglomeration bounces
back from shocks (Martin & Sunley, 2015).
By exploiting the local negative shocks as natural experiments, the empirical literature
has tested the multiple equilibria in the location of economic activities predicted by the seminal
theoretical literature in spatial economics like Fujita et al. (1999). So far as known, they haveshown that the physical shocks destroying structures and killing people only modestly shifted
the spatial distribution of economic activities (Glaeser, 2022). A location with advantageous
fundamentals makes the economic agents move back despite the destruction (e.g., Davis &Weinstein, 2002). Even under multiple equilibria, agglomeration recovers because of the selffulfilling expectation and makes a negative shock undone (Takeda & Yamagishi, 2023).
Furthermore, the damaged location sometimes experiences greater growth because a shock can
be a catalyst for creative destruction through the reduction of demolition costs and thereplacement of obsolete systems (Hornbeck & Keniston, 2017) or be a driver of policy
intervention for urban renewal (Hu & Wang, 2019).
Although there are a few investigations that clearly found empirical evidence of multiple
equilibria by exploiting the local shock as a natural experiment (e.g., Redding et al., 2011; Xu& Itoh, 2018), their implications for the whole urban economy are limited because they focusedon a specific industrial sector like transportation. One of the focal points in testing multiple
equilibria in an urban economy beyond a specific sector is initial heterogeneity in the first or
second-nature geographies (e.g., Cuberes, 2009). While the majority of empirics assessing theimpact of a local shock on outcomes of urban economy (e.g., population, business location)2

have rejected multiple equilibria, it might be driven by the context such that the ex-anteheterogeneity like the geographical constraint to relocate the agglomeration guarantees
uniqueness of equilibrium (Lee & Lin, 2018; Lin & Rauch, 2022). In this respect, there is stillroom to examine if the single equilibrium in the distribution of economic activities indeed holds
even though the initial condition is relatively homogeneous across locations.
To address this issue, I conduct the empirical analysis using the combination of the alliedbombings of Japanese cities during WW2 and postwar urban land requisition in Yokohama,
one of the Japanese biggest trading port cities, as exogenous variations. The bombingscompletely destroyed Yokohama’s developed area. The remarkable difference in the recovery
process from other Japanese cities was that land in the central business district (CBD) in
Yokohama was almost unavailable for 5–15 years after the end of WW2 due to occupation by
the US Army. At its peak, the area of requisitioned land reached 16 million square meters. It
was 62% of the total requisitioned areas all over Japan. Since the Army also occupied about
90% of port facilities, trade sectors as Yokohama’s key industrial sectors hardly relaunch theirbusiness under the requisition until 1950.
The examination of Yokohama’s case in this study has several features in terms of data
and the design of empirical analysis. To examine these questions, I newly digitize the location
information of firms collected from city business directories and construct block-level panel
data on the number of offices in commercial districts in Yokohama between 1930 and 1974.
By utilizing this dataset, I can track the dynamics of the location of business agglomerations
on a spatially detailed scale. The within-city analysis relying on the combination of two
exogenous shocks (bombings and requisition) has several advantages for identification. First,
I can test multiple equilibria under the condition that the ex-ante heterogeneity across blocks
is small. The topography of Yokohama’s commercial area is almost flat and hardly has
variation. In addition, the bombings in advance of requisition destroyed all the urban structures3

in the area. In this sense, there were few hampers in the formation of alternative agglomeration.
Second, the primary purpose of the requisition was based on the military strategies of the US
Army and not necessarily on economic and institutional reasons. In this sense, the land
requisition can be plausibly uncorrelated with local socioeconomic features. Third, therequisition was compelling for all economic agents in Yokohama.
The empirical findings in this study suggest that the postwar requisition dispersed
economic activities from the CBD, which was not undone even in the 1970s. These results
imply that postwar Yokohama’s CBD underwent a different path of postwar growth from theaverage trend in Japanese cities. For an in-depth understanding of this negative association, I
empirically test several anecdotes in line with theoretical findings in spatial economics. I showthat an alternative core of business activities has persistently developed outside the incumbent
CBD since the 1960s. This might be the consequence of cumulative causation (Ottaviano &Puga, 1998) triggered by the lockout of offices from CBD. Although forcedly relocatedbusiness activities should have moved back to the original location after the lifting of therequisition as with other Japanese cities, CBD has not recovered its superior position in the
prewar period. Unlike Davis & Weinstein (2002; 2008), this result implies that the location of
agglomeration cannot be determined by the location fundamentals alone. I also show thatpublic investment such as land readjustment mitigates the negative association while
uninvested requisitioned plots faced a more severe situation. This might correspond to thetheoretical prediction by Allen & Donaldson (2022) where multiple stable equilibria tend to
emerge if lagged agglomeration effects by the history of investment is strong. In other words,
the exogenous change in the allocation of investment by the requisition might have shifted
equilibrium distribution to another steady state.
This study contributes to several strands of literature. The primary literature focuses onshock sensitivity in the geographical distribution of economic activities. Originating from the4pioneering work by Davis & Weinstein (2002), the empirics of spatial economics haveexamined whether agglomeration bounces back from exogenous shocks like wartime
destructions (Davis & Weinstein, 2008; Miguel & Roland, 2011; Feigenbaum et al. 2022;
Takeda & Yamagishi, 2023), natural disasters (Imaizumi et al. 2016; Hornbeck & Keniston,
2018; Hu & Wang, 2019; Okazaki et al., 2019; Siodla, 2021), and political shocks (Redding etal., 2011; Ahlfeldt et al. 2015).
Although the literature above employs credible identification and estimation strategies,
the findings are mixed. Related to the heterogeneous initial condition, the difference in thespatial unit across the empirical investigations might make the variation in these findings (Lin& Rauch, 2022). While the majority of empirics have focused on the inter-city change in the
distribution of economic activities, intra-city level investigations are still scarce. I fill this gap
in the literature by the block-level analysis within a city. In addition, by exploiting the case of
Yokohama, I can analyze the role of the institution in the recovery process of agglomeration.
As a related empirical finding, Brakman et al. (2004) showed that the damages brought by
WW2 had a permanent negative impact on the city size in east Germany unlike the west, andthey guessed that this decline in the east arose from a dysfunction of the market by the socialist
system. Relying on an alternative approach, Bosker et al. (2007) also confirmed the multiple
equilibria in the location of agglomeration in the same context. Focusing on the requisition as
an alternative form of economic dysfunction, I complement this literature by empirically testing
if their guess holds on a within-city scale.
This study also contributes to the empirical literature regarding the restrictions on urban
land use and the persistence of land use. Physical obstacles to land use due to topographic
features (Saiz, 2010; Harari, 2020) and excessive land use regulations (e.g., Turner et al., 2014;
Glaeser & Gyourko, 2018) hamper economic activities through various channels. Landrequisition in Yokohama was only a temporary restriction on urban land use. Nevertheless, its

5

long-term effects might be preserved in light of recent empirical findings suggesting that
historical urban land use (policy) has persistent effects (Banerjee & Iyer, 2005; Brooks & Lutz,
2019; Allen & Leonard, 2021; Yamasaki et al., 2021; Miranda, 2022). Therefore, this study
can also be regarded as an empirical examination of the long-term impacts of land-use
restriction by exploiting the requisition as a natural experiment.
The remainder of this paper is organized as follows. Section 2 describes the historicalbackground of Yokohama City and its postwar requisition. Section 3 explains the data andeconometric specifications. Section 4 presents the results of the study, and Section 5 concludes.

6

2. Historical Background
2.1 History of Yokohama until WW2
Yokohama is located 30 km south of Tokyo and is one of the core cities in the Greater
Tokyo Area1. Yokohama has a population of approximately 3.7 million, and it is the secondlargest city in Japan. Until the middle of the 19th century, during the Edo period, Yokohama
was just a small fishing village. However, it experienced sudden and rapid economic growthsince Yokohama Port was designated as a port city open for trade with the West in 1859. Afterthe opening, economic activities were concentrated in five coastal wards: Naka, Nishi, Minami,
Kanagawa, and Tsurumi. Figure 2.1 shows the location of these wards, the commercial districts
in 1947, CBD (a hinterland of Yokohama Port surrounded by the Ohoka River and Nakamura
River), and two commercial districts.
Due to its proximity to Tokyo and the agglomeration of the spinning industry formed in
its hinterland, Yokohama was one of the largest port cities in Japan in the early 20th century.
It formed the commercial agglomeration of financial, wholesale, retail, and trade sectors
centering around Yokohama Port, as well as manufacturing agglomeration. Although the 1923
Great Kanto Earthquake hit Yokohama and caused serious damage, its economic
agglomeration soon recovered, and it achieved a larger level of growth2. In 1940, Yokohama’s
population reached nearly 1 million.
Due to its crucial role in economic activities and munition production, Yokohama was animportant target of the allied bombing during WW2. Particularly, two bombings on April 18
and May 29, 1945, destroyed Yokohama’s urban function. As shown in Figure 2.2, the burned
area by the bombings reached 42 % of the urban area, and approximately 11,900 buildings

1

The description of historical context in this section largely relies on Yokohama City (1981;
1999; 2000; 2019), and Hattori & Saito (1983).
2
For example, Okazaki et al. (2019) empirically showed that the earthquake achieved creative
destruction in Yokohama City through the upgrade of machine technology and/or the survivalof efficient firms.
7were destroyed. The number of sufferers was approximately 4 hundred thousand and at least
19000 people were killed.

2.2 Postwar land requisition by the US ArmySoon after the end of WW2, the US Army began the countrywide occupation of Japan inadvance of the official unconditional surrender. Yokohama was selected as the core occupationsite for the mission. This was because of the proximity to the US Air Force base in aneighboring city and the intention of the Japanese government to defend the national polity,
centering on the emperor (and his residence in Tokyo). In this sense, Yokohama can be seen
as a location of interest in both the US and Japan. Along with the establishment of theheadquarters of the US Eighth Army and conducting military government in the Far East, theoccupation of Yokohama became full-fledged.
The US Army requisitioned a large part of Yokohama’s CBD. In 1952, at the peak of theoccupation, the area of requisitioned land reached 16 million square meters, which was 62% of
the total requisitioned area all over Japan. Additionally, because approximately 90% of port
facilities were also occupied, trade sectors in Yokohama hardly relaunch their business.
Plyarticular, the requisition share was largest in Naka Ward, the CBD of Yokohama City.
Approximately 74% of plots in Naka were requisitioned by the army.
During the requisition, citizens and firms in Yokohama were forced to move out of the
CBD, which was completely transformed into a location for military activities. Figure 2.3
shows the usage of requisitioned plots and buildings around Naka and Yokohama Port in thelate 1940s. The surviving buildings were military offices, dormitories of officers, postexchanges, and clubs. In the burned area, new military facilities such as barracks, airstrips, andparking areas for military vehicles were built.

8

As shown in Figure 2.4, while coastal districts (so-called Kannai) and parts of port
facilities were lifted around 1950, the requisition continued in inland districts because of the
Korean War. Since the US Eighth Army was the main troop during the war, Yokohama has
played a crucial role as a key supply base. Along with the armistice in 1952, a large part of the
US forces moved to neighboring cities like Yokosuka and the requisition was gradually lifted.

2.3 Delay in post-requisition reconstruction
Despite a postwar construction boom throughout Japan, reconstruction in the formerrequisitioned areas progressed very slowly. Since the requisitioned plots were returned in astate of greenfield without any construction, firms in the CBD had to rebuild their places forbusiness from scratch. Additionally, because of occupation, infrastructure facilities such as
roads were too dilapidated to restart economic activities, and the shortage of public budget
made it difficult to implement large-scale repairs. This difficulty in reconstruction resulted in
the relocation of offices to other regions, which led to the shrinkage of commercial
agglomeration in the CBD.
The main destinations of the relocated offices were other commercial districts in
Yokohama City. Particularly, the relocated or newly established offices were concentrated in
the blocks outside the CBD in Naka Ward (called the “Noge Area”). Noge was a historicalcommercial area and one of the few blocks that escaped the requisition. Despite the fatal
damage caused by the bombings, the black market in Noge began to grow soon after WW2 andformed the largest busy street in Yokohama by the end of the 1940s. After the end of therequisition, many restaurants and bars continued their businesses.
The other destination was the district surrounding Yokohama Station. A large part of the
district was marshy until the 1920s, and urban development began after the completion oflandfills. Despite good access to the railway, the western entrance of the station remained9

undeveloped for decades until the beginning of the 1950s. However, due to the development
project by a private railway company and the postwar land readjustment project by thegovernment, a new commercial area was constructed. After the completion of these projects,
the district established its position as an alternative CBD. The highest point of land price in
Yokohama City has been in front of Yokohama Station since the 1960s, while the highest point
was in the requisitioned CBD in the prewar period (Suzuki, 2022).
The relocated firms’ destinations were not limited to Yokohama City. The US Armyoccupied approximately 90% of the port facilities of Yokohama and utilized them as a key
military base for the Korean War. This led to the outflow of trading companies, maritime
industries, wholesale agents, and the financial sector from Yokohama City. After leaving only
the subsidiaries in Yokohama, the headquarters were closed and relocated to Tokyo. As
described by Yokohama City University (1957), while the number of wholesale agents per1000 retail companies was 140 in 1933, it decreased to 57 in 1950.
The shortage in the update of urban infrastructure and land use due to policy intervention
spurred the relocation of economic activities. The primary policy for postwar reconstruction in
Japanese cities was land readjustment projects and infrastructure investments such as roads,
parks, and sewage systems. While Yokohama was also regarded as a target of this project andthe development plan was devised, it could not contain the requisitioned area, and target
regions were only to fragment on a small scale. As a result, the obsolete urban structure andcomplicated land ownership relationship remained in the CBD due to the lack of readjustment,
which hampered post-requisition reconstruction.

10

3. Methodology
3.1 Data
3.1.1 Office location
I constructed a dataset of the block (chome) -level number of offices spanning 45 years,
covering the pre-war and post-war periods. The dataset was procured from the businessdirectories published by the Yokohama Chamber of Commerce (YYC) in 1930, 1939, 1955,
1959, 1965, and 19743 (YYC, 1930, 1939, 1955, 1959, 1965, 1974). The directories cover basic
information such as company name, address, and representative name for the members of the
YYC and some other registered firms. These directories were digitized and aggregated at theblock level45. The aggregated dataset covers commercial districts in five coastal wards: Naka,
Nishi, Minami, Kanagawa, and Tsurumi. The definition of commercial districts was based on
postwar reconstruction urban planning in 1947 (Yokohama City, 2000).
3.1.2 Requisitioned regions
I digitized a map showing the requisitioned regions (Yokohama City, 1954). Figure 3.1
shows the original version of the map. The regions were categorized according to theirrequisition status in 1954. The regions colored red and yellow were already (or to be) lifted,
whereas those colored pink were still occupied by the US Army. This map confirms thatapproximately half of the CBD area was requisitioned. From the digitized map, I calculated,3

In the 1980s, there were alternative big events in/near the CBD area affecting commercial
activities like the closure of a large shipbuilding yard (Mitsubishi Jukogyo Yokohama Zosenjo)
and the waterfront development projects (Yokohama Minatomirai 21). Thus, I do not include
the observations from the directories published more recently because these events might
distort the evaluation of the effects of requisition.
4
Although the industrial classification is available for each office in the directories, it isdifficult to conduct consistent industrial-level analysis because the classification relied on the
product (e.g., textile, food) in the prewar period but on business type (e.g., manufacturing,
wholesale) in the postwar period.
5
In the process of aggregation, I relied on the block division in 1959.
11

for example, the binary variable taking a value of one if a block was in a requisitioned region
and the share of a requisitioned area in each block.
3.1.3 Postwar land readjustment project
A map of the target regions of the land readjustment project is available for Yokohama
City (1986). This map was digitized to calculate, for example, the binary variable taking a valueof one if a block was in a readjusted region and the share of a readjusted area in each block.
3.1.4 Demographic feature
I used the population variable in the initial period as a control variable to consider thedifference in the level of economic activities among the blocks. The block-level population
data of Yokohama City are available from the 1935 National Census of the pre-war period(Yokohama City, 1936). The population share of each block is calculated.

3.2 Econometric specifications
The main hypothesis of this study is that the postwar land requisition persistently shrankbusiness agglomeration. As the anecdotes in the previous section show, the CBD of Yokohama
experienced the outflow of commercial and trading-relevant activities and the emergence of analternative CBD under the land requisition by the US Army. Unlike other Japanese cities, thisadditional constraint might have hampered the postwar reconstruction of the CBD and madethe CBD’s centripetal force permanently lower, while citizens could not have clear prospects
for post-requisition reconstruction. In sum, I hypothesize that requisition incurred long-run
negative effects on commercial activities in the CBD.
Using the number of offices in each block as an outcome, I evaluate the effects of land
requisition in the CBD by estimating the difference-in-differences (DD) method. By combining
DD with fixed-effect panel estimation, I controlled for various time-invariant and unobservablefactors. The outcome takes a non-negative and small integer value, and contains many zeros.

12Additionally, the distribution of outcomes was highly right-skewed6. Thus, it is assumed that
it follows a negative binomial distribution7. The baseline specifications are as follows:! [#!" ] = exp[)" + +! + ,-.! × 012345," 6 + 7 !" 8],(1)

where #!" is the number of offices in block : in period 3; )" is the time fixed effect that controls
for macroeconomic trends common to all blocks; +! is the individual (block) fixed effect that
controls for time-invariant block-specific factors such as geographic characteristics (e.g., area,
terrain, proximity to port) and historical features before 1930. ,-.! × 012345," is a variable ofinterest, in which ,-.! is a dummy variable taking a value of 1 if block : was in a requisitioned
region in the CBD and 0, otherwise, and 012345," takes a value of 1 during the postwar period
and 0, otherwise. Thus, the treatment group consists of blocks inside the requisitioned regions,whereas the control group is composed of those in commercial districts outside the regions.
7 !" is a set of control variables, including the interaction term between the population share ofeach block and the year dummies. The main hypothesis was supported if 6 < 0.
While ,-.! takes a value of 1, even if only 1% of block :’s area is requisition by its
definition, the intensity of requisition varies across blocks. Thus, we also use an alternative
treatment variable, ,-._ℎ! , which takes a value of 1, if the share of the requisitioned area
exceeds 50% of block :’s total area.

6

See Figure A.1 and Table A.1 for the descriptive statistics of the (in)dependent variables.
The blocks with no office in 1930 were dropped from the dataset to avoid the zero-inflation.
After that, the share of zero dropped to 9.1% of all observations.

713

4. Descriptive analysis
4.1 Time series variation in the number of offices
To compare the trend of change in the number of offices between the treatment group(inside the requisitioned regions in the CBD) and the control group (outside), I show the timeseries variation from 1930 to 1974 in Figure 4.1. On average, in 1930 and 1939, the blocks inthe treatment group had nearly twice as many offices as those in the control group. Additionally,
the trend of change in the number of offices appeared parallel between groups.
However, in 1955 and 1959, the trend observed during the pre-war period was reversed.
The number of offices in the treated blocks was lower than that in the untreated blocks.
Although treated blocks gradually recovered their absolute size after 1965, their superior
position compared with untreated blocks did not return to the pre-war level because theuntreated blocks experienced simultaneous growth.
4.2 Spatial distribution of offices
Figure 4.2 geographically visualizes the rate of change in the number of offices between
1939 and 1955 around the CBD, Noge area, and Yokohama Station. The cold-colored blocks
decreased the number of offices, whereas the number of warm-colored blocks increased. The
shaded area represents the requisite area of the CBD. Since the shaded blocks were immediately
after the turnback or were still occupied in 1955, it can be observed that these blocks
experienced a great decline. In contrast, the blocks in the Noge area and neighboring
Yokohama Station (peripheral blocks) had the largest growth rate.
I also visualize the rate of change between 1939 and 1974 in Figure 4.3. In the former
requisitioned blocks, some blocks experienced growth, while others did not, and it was difficult
to find a clear spatial pattern. In contrast, growth in peripheral blocks was consistently observedin 1974.

14

Similar trends are observed in the density map. Figure 4.4 shows the density of offices(the number of offices divided by a block’s area) for each period. In 1939, only blocks in the
CBD formed commercial agglomerations. The business district, made up of the trading andfinancial industries, was located in the coastal blocks, while the inland blocks shaped ashopping district. However, in 1955, the CBD lost its density, while alternative agglomeration
emerged in the Noge area. Although the density in the coastal blocks seemed to recover until
1974, the inland blocks could not regain their density. Instead, newly formed agglomerations
can be observed around the Yokohama Station.

15

5. Results
To test the effect of the requisition (and its duration) on the number of firms, I first show
the estimation results for the specifications described in Section 3.2. For an in-depth
understanding of the obtained results, I also present the results of an additional analysis. Finally,
I conducted a series of robustness checks to examine the stability of the obtained results.

5.1 Baseline results
Table 5.1 shows the baseline results to test the main hypothesis (postwar land requisition
shrank business agglomeration). In Column (1), even when controlling for the block and year
fixed effect, the coefficient of the treatment variable, req×postwar is significantly negative.
This result implies a negative association between requisition and the number of offices.
Column (2) also shows the estimation results, including the population share of each block as
an additional control variable. The value of the estimated coefficient hardly changed. To
distinguish the long-term impacts of the requisition, I also show the result excluding theobservations in the 1950s in Column (3). The estimated coefficient is still negatively significant
while the absolute value becomes smaller than the Columns (1) and (2).
Column (4) shows the result using the alternative treatment variable req_h taking a valueof one if the share of the requisitioned area exceeds 50% of the block’s total area. The estimatedcoefficient is indeed negatively significant and its absolute value is larger than those obtainedin Columns (1) and (2) using a baseline treatment variable. Column (5) the result excluding theobservations in the 1950s. It can be confirmed that the negative association is indeed significant.
These results imply that the negative association is stronger when the requisition intensity ishigher, which lasted even after the end of the requisition.
For each treatment variable, I report the incidence rate ratio (IRR) calculated as the
exponential of the estimated regression coefficient. In the case of count data regression, IRR

16

represents the rate of change in the dependent variable for one unit increase in an independent
variable when other variables are constant (Hilbe, 2011). In the estimation results using all
observations, IRRs for treatment variables range from 0.388 to 0.477. This result suggests thatthe number of offices decreased to 38.8 ~ 47.7% of that before the requisition averaged overan entire period, controlling for the trend of outcome observed in the untreated blocks,
individual and time fixed effects. Even excluding the observations in the 1950s, IRRs are lessthan 0.7, which implies the long-lasting decline of the incumbent agglomeration.
Figure 5.1 shows the event study plot using 1939 as the reference period. Specifically, I
estimate time-varying treatment effects 6$" based on the following specification:(2)![#!" ] = exp[)" + +! + ∑"%$&'& ,-.! × ABC_D" 6$" + 7 !" 8],where ABC_D" is a year dummy. The estimated coefficient for 1930 was almost zero andstatistically insignificant. Thus, there is no convincing evidence that the parallel trend
assumption is violated. The absolute values of the coefficients in 1955 and 1959 are larger thanthe baseline estimate in Table 5.1, whereas those in 1965 and 1974 are smaller. While thenegative association was stronger in the 1950s, when some of the requisitioned blocks wereimmediately after the turnback or still occupied, this association was mitigated with the marchof time. However, it is noteworthy that the negative shock of requisition does not seemcompletely undone because the estimates after 1965 are still significantly negative. Overall,
the obtained results support the main hypothesis. In Figure 5.3, I calculate IRR for eachestimated coefficient of Eq. (2) shown in Figure 5.2 to capture the time series variation in thechange of rate in the number of offices. While the number of offices decreased to 30 ~ 40% ofthat before the requisition in the 1950s, it recovered to approximately 65% after the 1960s.

5.2 Additional analysis
From the discussion in Section 2.3, the main reasons for the delay in post-requisition
reconstruction are as follows: (1) within-city relocation of agglomeration; (2) outflow of trade17

relevant industries to neighboring cities; and (3) low usability of the requested area due to theabsence of the integrative land readjustment project. Because the dataset does not cover the
information of offices outside Yokohama, I conduct an additional analysis focusing on withincity relocation and the low usability of requisitioned regions in this section.
5.2.1 Within-city relocation of agglomeration
The main results imply that the requisitioned blocks experienced a prolonged decline in
the agglomeration of commercial activities. In contrast, from the descriptive analysis of thespatial distribution of offices in Section 4.2, the area around Yokohama Station has emerged
as an alternative CBD since the 1960s. To link these findings, I conducted an empiricalexamination based on the following DID specification:! [#!" ] = exp[E" + F! + 2353:1G! × 012345," 6 ( + 7 !" H],(3)

where 2353:1G! takes a value of 1 if block : is in the districts neighboring Yokohama Station(specifically Minami-saiwai, Kita-saiwai, Tsuruya, and Takashima-dori). One caveat is that the
number of blocks in these districts is only eight, so the ratio between the treated and untreatedunits is quite unbalanced. Note that the dataset for this examination does not include
observations of blocks in the requisitioned area.
Table 5.2 show the estimation results of Eq. (3). As shown in Column (1), the coefficientof the treatment variable yoko×after is significantly positive. This result implies that the blocks
around Yokohama Station experienced growth in business activities in the post-war period.
Column (2) shows the estimation results including the population share of each block as anadditional control variable. The value of the estimated coefficient hardly changed.
I also show an event study plot using 1939 as the reference period in Figure 5.3. The
estimated coefficient in 1930 was negative but barely insignificant. In the postwar period,
however, the coefficients became positive, although those in the 1950s were insignificant.

18

From these results, the blocks around Yokohama Station have grown at an accelerated pacesince the 1960s, compared with other blocks.

5.2.2 Role of land readjustment project
As described in Section 2.3, although Yokohama City was a target of the postwar land
readjustment project, the government could not conduct it on an integrative scale because of
the requisition. Figure 5.4 shows the digitized map of the readjusted regions, together with amap of commercial districts and requisitioned regions in the CBD. It can be confirmed that not
all requisitioned blocks in the CBD were covered. To test the difference in post-war
reconstruction depending on policy intervention, I estimate the following specification:! [#!" ] = exp[)"(( + +!(( + ,-.! × 012345," I$ + ,-.! × 012345," × ,-5A!" I) +7 !" 8(( ],(4)where ,-5A!" takes a value of 1 if block : was the target of the readjustment project. Since themajority of the project was completed in 1965, ,-5A!" takes a value of 1 in 1965 and 1974 foreach targeted block. A single term ,-5A!" is included in 7 !" . If the availability of the readjusted
land really matters in the reconstruction, I hypothesize that I) is significantly different fromzero and even positive.
Table 5.3 shows the estimation results of Eq. (4). In Column (1), the estimated coefficientof the baseline treatment variable, the interaction term between req and after, is still negatively
significant, and its magnitude is larger than the baseline results in Column (2) of Table 5.1. Bycontrast, the triple interaction term, req×postwar×read is positively significant. As Column(2) shows that this association becomes sharper when using the alternative treatment variable.
These results imply that the negative association between requisition and economic activities
was mitigated by the improvement in the quality of land, although the requisition did notcompletely undo a negative shock.
19

5.3 Robustness check5.3.1 Alternative outcome
As shown in Section 4.1, the decline in the requisitioned CBD was not only absolute but
also relative. To capture this relative decline more explicitly, I also conduct a linear DD using
the share variable as an outcome, defined as follows8:
2ℎ5,-!" = #!" ⁄∑* #*" .
(5)
Figure A.2 shows the time series variation of the office share from 1930 to 1974 for thetreatment and control groups. During the prewar period, the office share was larger in thetreated (requisitioned) groups, and the share was almost invariant in both groups. As in the caseof the number of offices, the trend of office share seems to be similar between groups. In the
1950s, however, the untreated (non-requisitioned) blocks became dominant. Although thetreated blocks partially recovered their position after the 1960s, they could not bounce back to
the level observed in the prewar period.
This observation can also be confirmed by regression DD. Figure A.3 shows the event
study plot using the share variable as the outcome. The treatment variable in 1930 was negative
but insignificant. Thus, I cannot find convincing evidence that the parallel trend assumption isviolated even when relying on the share variable. The treatment variables in the postwar period
have a similar trend to that observed when using the baseline specification (Figure 5.1). Indeed,
the negative shock of the requisition does not seem to be completely undone.
5.3.2 Excluding blocks in Tsurumi Ward

8

Another motivation of the analysis relying on the share variable is to check the sensitivity ofthe parallel trend to the model specification. See, for example, Fredriksson & de Oliveira(2019) for details of the scale dependence in the parallel trend assumption. Note that the
distribution of the outcome remains right-skewed even after taking the share.
20

In the baseline analysis, the dataset included five coastal wards: Yokohama City, Naka,
Nishi, Minami, Kanagawa, and Tsurumi. However, compared to other wards, Tsurumi
historically has commercial regions with many manufacturing plants. This mixed land use
might have made Tsurumi unsuitable for inclusion in the control group. To address thisproblem, I also conducted DD, excluding the blocks in Tsurumi. Table A.2 shows theestimation results. It can be found that the exclusion of the observation in Tsurumi hardly alters
the main results shown in Table 5.1.

5.3.3 Alternative definition of treatment group
The treatment variable in the baseline specification is defined as the requisitioned blockin the CBD. However, as shown in Figure 3.1, the requisitioned regions were not limited to the
CBD, and some other blocks outside the CBD were also occupied in the postwar period(although the duration of the requisition was generally shorter outside the CBD). To capture
the overall impact of the requisition in Yokohama City, I estimate the following specification.
! [#!" ] = exp[K" + L! + ,-._5MM! × 012345," 6 (( + 7 !" N],(6)

where ,-._5MM! takes a value of 1 if block : was in a requisitioned region. I also estimate thefollowing triple differences (DDD) to evaluate the heterogeneity in the effect of the requisition
between the CBD and the periphery:![#!" ] = exp[K"( + L!( + ,-._5MM! × 012345," 6$(( + ,-._5MM! × 012345," × OPA! 6)(( +(7)7 !" N( ],where OPA! takes a value of 1 if block : was in the CBD. The interaction term between OPA! and012345," is included in 7 !" .
Table A.3 shows the estimation results. As shown in Column (1), compared to thebaseline results in Table 5.1, the estimated coefficient of req_all×postwar is marginally smaller
than that of the baseline treatment variable req_all×postwar but almost unchanged. Column

21

(2) shows the result when introducing the triple interaction term req_all×postwar×cbd, and it
can be confirmed the estimated coefficient of this term is also negative. This result suggeststhat the negative association between the requisition and economic activities was stronger
inside CBD.

5.3.4 Excluding blocks in the main destinations of relocation
As shown in Sections 4.2 and 5.2.1, the peripheral blocks of CBD experienced growth ineconomic activities despite the decline in CBD. However, this result implies an issue whenestimating DD as a violation of SUTVA (the stable unit treatment value assumption) at thesame time. To address this problem, I also conducted DD excluding the blocks in the main
destinations of relocation, the blocks in the Noge Area, and around Yokohama Station. Table
A.4 shows the estimation results. Although the absolute value of the estimated coefficient issmaller than that in the baseline results (see Table 5.1), it is still negatively significant, as shownin Column (1). Additionally, Column (2) implies that the magnitude of negative association isalmost same as the baseline when using the blocks whose share of requisitioned area exceeds
50% as the treated units.

5.3.5 Controlling for market potential variable
In addition to the population share, I also estimate the specification, including a control
variable serving as a proxy for market potential (MP). The MP variable is defined as follows:
QR! = ∑+∈-! ln[U:2:31,2+ ],

22

(8)

where V! represents a set of tram stops in 1932 within 1 km from block : ’s centroid;
U:2:31,2+ is the number of passengers getting off at tram stop W9. I included the interaction
term between MP and year dummies as the control variables. One caveat when including MP
is the considerably strong correlation with the treatment variable. The value of the correlation
coefficient is 0.75, which exceeds the threshold (|correlation|>0.7), where the model estimationis severely distorted by multicollinearity in general (Dorman et al. 2013). Thus, the results
obtained should be interpreted with caution. Table A.5 shows the estimation results. It can befound that inclusion of the MP variable does not alter the main results shown in Table 5.1.

U:2:31,2+ is average value of the observation on October 6th, 1932 and that on November
8th, 1933. See Yokohama City (1940) for more detail.

923

6. Conclusion
This study addresses whether the spatial distribution of business activity has multiple
equilibria. To empirically test this question, I exploit the combination of the allied bombings
during WW2 and postwar urban land requisition by the US Army in Yokohama as exogenous
variations. The recent literature implies that the mixed empirical findings regarding multiple
equilibria might be governed by the ex-ante heterogeneity in the first and second-naturegeography. The combination of these shocks and within-city analysis enables me to test thequestion under the condition that the heterogeneity across blocks is smaller. This is because I
can examine the impact of exogenous shift of business activities and its persistency under thecondition that the urban structures built on the plain were almost destroyed by the bombing.
I construct block-level panel data on the number of offices in the commercial district of
Yokohama covering the prewar and postwar periods. Using fixed-effect difference-indifferences, I find that the requisitioned blocks experienced a decline in economic activities.
The results also imply that the negative shock was not completely undone, even 30 years after
the end of WW2. This result is contradictory to the majority of findings in existing empirics
showing that the economic activities moved back to the original location and agglomeration
recovered despite the negative shock.
I also empirically test several anecdotes regarding the causes of this decline. First, theblocks around Yokohama Station, the main destination of relocated offices, experienced
growth in business activities in the postwar period. Growth began in earnest after the 1960s.
This coincided with the completion of the development project at the station. Second, theresults imply that the land readjustment project in the postwar period as a policy intervention
to improve land usability mitigated the negative shock, whereas the requisitioned blocks
without the intervention faced a more severe situation.

24

This study has several implications. Relevant to land use restriction, the unavailability ofland is advantageous in business activities and incurs long-term economic decline, even if it is
temporary. Although the requisition in Yokohama is indeed an extreme case, the results showed
the harmful aspect of the excessive regulation of urban land use, as discussed in the literatureon urban economics.
From the perspective of regional economic resilience, findings suggest that prolongationof negative shock cancels out the mechanism of creative destruction and the self-organizedrecovery of agglomeration. Another implication regarding resilience is that a policy
intervention for reconstruction plays a crucial role in the absence of a self-organized process
in the recovery of agglomeration. Considering these implications, a reconstruction policy
should be implemented as soon as possible after a shock by eliminating tangible and intangible
barriers.

25

Acknowledgments
We thank Olga Demidova, Shota Fujishima, Shinya Fukui, Mizuki Kawabata, Hiroshi
Kumanomido, Naoto Mikawa, Mariko Nakagawa, Kentaro Nakajima, Chihiro Shimizu,
Atsushi Yamagishi, and Junichi Yamasaki for their comments and suggestions. An earlyversion of this paper was presented at the CSIS DAYS 2021, the 61th ERSA Congress, and at
the 36th Applied Regional Science Conference (ARSC). We thank the participants of these
conferences for their helpful comments and suggestions.

Declaration of Interest Statement
This study was supported by the Japan Society for the Promotion of Science (JSPS) under
KAKENHI Grant Numbers 22K13411, and Obayashi Foundation.

26

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28

Figure 2.1: Location of the coastal wards and CBD

Notes: The red area represents the commercial area in 1947. The purple area represents the CBD.

29

Figure 2.2: Damage by the allied bombings in Yokohama City

Notes: The black areas represent urban areas. The red-hatched area represents the damaged area. This map isavailable from the Yokohama Archives of History.

30

Figure 2.3 Usage of requisitioned plots

Notes: The source of this map is GHQ (1949).

31

Figure 2.4 Process of land retrocession

1950

1955

1960

Note: The red area represents the requisitioned area for each period. Yokohama City (1997) was the source of the
maps.

32

Figure 3.1 Requisitioned regions

Note: The original version is in Yokohama City (1954). The blue area was retroceded after 1954. The area coloredwith light blue was retroceded before 1954.

33

Figure 4.1 Time series variation of the number of offices

Notes: The blue line represents the average number of offices in the treatment group in each period and the redlines represent those in the control group. Arrow indicates the standard error of each observation.

34

Figure 4.2 Rate of change in the number of offices (1939-1955)

Note: The hatched area represents the requisite areas in the CBD. The dashed line represents the National Railway
routes.

35

Figure 4.3 Rate of change in the number of offices (1939-1974)

Note: The hatched area represents the requisite areas in the CBD. The dashed line represents the National Railway
routes.

36

Figure 4.4 Density map of offices

1939

1955

1974

37

Table 5.1 Baseline results
(1)
(2)
(3)
−0.768*** (0.097) −0.741*** (0.098) −0.381*** (0.095)(4)(5)
Req×postwar
Req_h×postwar−0.947*** (0.098) −0.470*** (0.102)
IRR
0.464
0.477
0.683
0.388
0.625
Block FE
Yes
Yes
Yes
Yes
Yes
Year FE
Yes
Yes
Yes
Yes
Yes
Control
None
Population
Population
Population
Population
Observations
1,920
1,920
1,280
1,920
1,280
Notes: The estimation results of negative binomial regression formulated in Eq. (1) are reported. The dependent
variable is the number of offices. Req takes a value of 1 if a block was in a requisitioned region in the CBD and0, otherwise. Postwar takes a value of 1 during the postwar period and 0, otherwise. Req_h takes a value of 1 if
the share of the requisitioned area exceeds 50% of a block’s total area. In Columns (3) and (5), the observationsin 1955 and 1959 are excluded. The incidence rate ratios for the regression coefficients of treatment variables,
Req×postwar and Req_h×postwar, are reported in IRR. The levels of statistical significance are as follows: ***1%, ** 5%, and * 10%. Standard errors were clustered at the block level.

38

Figure 5.1 Event study plot for the baseline result

Notes: The estimation results of negative binomial regression formulated in Eq. (2) are reported. The dependent
variable is the number of offices. Each point represents a point estimate of the regression coefficient of the timevarying treatment variable req×dum_y. The arrow for each point represents the 95% confidence interval for eachpoint estimate.

39

Figure 5.2 Incidence Rate Ratios for estimated coefficients of treatment variables

Notes: The estimation results of negative binomial regression formulated in Eq. (2) are reported. The dependent
variable is the number of offices. Each point represents the incidence rate ratio (exponential of point estimate of
the regression coefficient) of the time-varying treatment variable req×dum_y.

40Table 5.2 Estimation results (within-city relocation of agglomeration)(1)(2)
Station×postwar 0.935*** (0.274) 0.978*** (0.274)
IRR
2.547
2.659
Block FE
Yes
Yes
Year FE
Yes
Yes
Control
None
Population
Observations
1,134
1,134
Notes: The estimation results of negative binomial regression formulated in Eq. (3) are reported. The dependent
variable is the number of offices. Station takes a value of 1 if a block was in the neighborhood of the Yokohama
Station and 0, otherwise. Postwar takes a value of 1 during the postwar period and 0, otherwise. The incidence
rate ratios for the regression coefficients of treatment variable Station×postwar are reported in IRR. The blocks
in the requisitioned regions in CBD are excluded from the observations. The levels of statistical significance areas follows: *** 1%, ** 5%, and * 10%. Standard errors were clustered at the block level.

41

Figure 5.3 Event study plot (within-city relocation of agglomeration)

Notes: The dependent variable is the number of offices. Each point represents a point estimate of the regression
coefficient of the time-varying treatment variable station×dum_y. The arrow for each point represents the 95%
confidence interval for each point estimate. The blocks in the requisitioned regions in CBD are excluded from theobservations.

42

Figure 5.4 Readjusted regions and commercial districts

Note: The green area represents the target area of the readjustment project. The red area represents the commercial
area. The hatched area represents the required area.

43

Table 5.3 Estimation results (land readjustment project)(1)(2)
Req×postwar−0.796*** (0.104)
Req×postwar×read
0.318* (0.175)
Req_h×postwar−1.07*** (0.112)
Req_h×postwar×read
0.490*** (0.185)
Block FE
Yes
Yes
Year FE
Yes
Yes
Control
Population
Population
Observations
1,920
1,920
Notes: The estimation results of negative binomial regression formulated in Eq. (4) are reported. The dependent
variable is the number of offices. Req takes a value of 1 if a block was in a requisitioned region in the CBD and0, otherwise. Postwar takes a value of 1 during the postwar period and 0, otherwise. Req_h takes a value of 1 if
the share of the requisitioned area exceeds 50% of a block’s total area. Read takes a value of 1 if a block was thetarget of the readjustment project. The levels of statistical significance are as follows: *** 1%, ** 5%, and * 10%.
Standard errors were clustered at the block level.

44Appendix
Table A.1 Descriptive statistics
n
mean
sd
min
No. of offices
1920 7.361 14.365
0Req
1920 0.409
0.492
0
Req_h
1920 0.291
0.454
0
Read
1920 0.095
0.293
0Population share
1920 0.003
0.007
0

45

max
213
1
1
1
0.074

Figure A.1 Distribution of the number of offices46

Figure A.2 Time series variation of the office share

Notes: The blue line represents the average number of offices in the treatment group in each period and the redlines represent those in the control group. Arrow indicates the standard error of each observation.

47

Figure A.3 Event study plot for the baseline result using the office share

Notes: The dependent variable is the office share. Each point represents a point estimate of the regression
coefficient of the time-varying treatment variable station×dum_y. The arrow for each point represents the 95%
confidence interval for each point estimate.

48

Table A.2 Estimation results (excluding blocks in Tsurumi Ward)
(1)
(2)
(3)
Req×postwar −0.786*** (0.096) −0.789*** (0.100)
Req_h×postwar−0.990*** (0.099)
Block FE
Yes
Yes
Yes
Year FE
Yes
Yes
Yes
Control
None
Population
Population
Observations
1,914
1,914
1,914
Notes: The levels of statistical significance are as follows: *** 1%, ** 5%, and * 10%. Standard errors wereclustered at the block level.

49

Table A.3 Estimation results (alternative definitions of treatment group)(1)(2)
Req_all×postwar−0.741*** (0.089) −0.390** (0.162)
Req_all×postwar×CBD−0.545*** (0.203)
Block FE
Yes
Yes
Year FE
Yes
Yes
Control
Population
Population
Observations
1,920
1,920
Notes: The levels of statistical significance are as follows: *** 1%, ** 5%, and * 10%. Standard errors wereclustered at the block level.

50

Table A.4 Estimation results (excluding blocks in the main destinations of relocation)(1)(2)
Req×postwar −0.693*** (0.101)
Req_h×postawar−0.944*** (0.112)
Block FE
Yes
Yes
Year FE
Yes
Yes
Control
Population
Population
Observations
1,685
1,344
Notes: The levels of statistical significance are as follows: *** 1%, ** 5%, and * 10%. Standard errors wereclustered at the block level.

51

Table A.5 Estimation results (controlling for market potential variable)(1)(2)
Req×postwar −0.807*** (0.131)
Req_h×postwar−0.988*** (0.118)
Block FE
Yes
Yes
Year FE
Yes
Yes
Control
Population, MP Population, MP
Observations
1,908
1,908
Notes: The levels of statistical significance are as follows: *** 1%, ** 5%, and * 10%. Standard errors wereclustered at the block level.

52