Impact of Natural Disaster on Industrial
Agglomeration: A Case of the Great Kanto
Earthquake in 1923
Tetsuji Okazaki（The University of Tokyo）
Asuka Imaizumi（JSPS）
Kaori Ito（Tokyo University of Science）
1
Background of the paper
•
Development of spatial economics （Fujita 1988; Krugman 1991;
Venables 1996; Fujita, Krugman and Venables 1999） and its impact
on economic history research (Kim 1995, 1998; Crafts and Mulatu
2005, 2006; Roses 2003)
•
Historical economic geography project
– Testuji Okazaki, Asuka Imaizumi and Kaori Ito, “Impact of Natural
Disaster on Industrial Agglomeration: A Case of the Great Kanto
Earthquake in 1923”
– Yutaka Arimoto, Asuka Imaizumi, Kaori Ito and Kentaro Nakajima,”An
information theoretic approach to identify spatial patterns of industrial
agglomerations”
– Asuka Imaizumi, Kaori Ito, Tomohiro Machikita and Tetsuji Okazaki, “The
determinants of size distribution of plants in the early stage of
agglomeration and industrial development: Japan, 1902-1919”
– Yutaka Arimoto, Kentaro Nakajima and Tetsuji
Okazaki ”Agglomeration or selection ?: The case of the Japanese silk
reeling industry, 1909-1916”
2
Historical sources of spatial data on Japan
• Location data of each factory
– Kojo Tsuran (Directory of Factories)
• 1902 (issued in 1904)
• 1919 (issued in 1921)
• 1935 (issued in 1937)
– Zenkoku Seishi Kojo Chosa (Census of Silk Reeling Factories)
various issues from 1895-1932
• Historical administrative boundary
– Historical Administrative Boundary web GIS database (by
Murayama Lab. Tsukuba Univ.)
• 1902
• 1919
• 1935
→city-(ward)-town-village level boundary
3
Distribution of factories in 1902: Colored
by density on a city-town-village basis
4
Distribution of factories in 1935: Colored
by density on a city-town-village basis
5
Motivation of the paper and related literature
• Mechanisms determining geographical distribution of
economic activities
– New Economic Geography (NEG)
• First nature of a region is not the necessary and sufficient condition
• Possibility of multiple equilibria
– Empirical studies on multiple equilibria
• Natural experiment using a temporary shock
– Davis and Weinstein (2002), Brakman, Garresten and
Schramm (2004), Miguel and Roland (2006)
• Natural experiment using a persistent shock
– Redding and Strum(2005), Redding, Strum and Wolf (2007),
Nakajima(2007)
6
Strategy of the research
• Focusing on a temporary shock by the Great Kanto
Earthquake which hit Tokyo in 1923
• Expanding the scope of empirical analysis
– Geographical distribution of industrial activities in a relatively
small area (Tokyo prefecture)
• Similarity of first nature
→Identifying the role of externality and scale economy (NEG
factors)
– Industry-level analysis
• Revealing the difference in relative importance of the first nature
and the second nature by industry
– Temporary shock by a natural disaster
• Pure exogeneity
7
Outline of the paper
• Shock by the Great Kanto Earthquake
• Overtime change in industrial agglomeration
• Testing the long-run impact of the Great Kanto
Earthquake
8
9
10
11
Human damage
person, %
Prefecture
Population just before
the Earthquake
Death
Missing
Total
Ratio to the population
Total
11,743,100
91,344
13,275
104,619
0.89
Tokyo
4,035,700
59,593
10,904
70,497
1.75
Tokyo City
2,265,300
58,104
10,556
68,660
3.03
The other area
1,770,400
1,489
348
1,837
0.10
1,379,000
29,614
2,245
31,859
2.31
Yokohama City
446,600
21,384
1,951
23,335
5.23
The other area
932,400
8,230
294
8,524
0.91
Chiba
1,347,200
1,373
47
1,420
0.11
Saitama
1,353,800
280
36
316
0.02
Shizuoka
1,626,300
450
42
492
0.03
602,000
20
0
20
0.00
1,399,100
14
1
15
0.00
Kanagawa
Yamanashi
Ibaraki
12
13
Damage to buildings
Number of buildings just before the
Earthquake
Completely burnt
and
destroyed
2,284,200
464,909
20.4
826,600
328,646
39.8
Tokyo City
483,000
305,146
63.2
The other area
343,600
23,500
6.8
274,300
115,353
42.1
Yokohama City
98,900
72,408
73.2
The other area
175,400
42,945
24.5
Chiba
262,600
13,372
5.1
Saitama
244,900
4,562
1.9
Shizuoka
289,100
2,257
0.8
Yamanashi
117,000
562
0.5
Ibaraki
269,700
157
0.1
Prefecture
Total
Tokyo
Kanagawa
Percentage
14
Variation of damage within Tokyo City by ward
Completely
destroyed
and burntl
Percentage
483,000
305,146
63.2
Kojimachi Ward
11,590
6,821
58.9
Kanda Ward
30,910
27,620
89.4
Nihonbashi Ward
23,190
21,616
93.2
Kyobashi Ward
31,880
29,290
91.9
Shiba Ward
38,640
17,167
44.4
Azabu Ward
19,320
906
4.7
Akasaka Ward
11,590
2,186
18.9
Yotsuya Ward
15,940
766
4.8
Ushigome Ward
26,080
515
2.0
Koishikawa Ward
32,360
1,663
5.1
Hongo Ward
28,500
7,463
26.2
Shitaya Ward
45,400
33,791
74.4
Asakusa Ward
63,760
59,325
93.0
Honjo Ward
59,890
55,274
92.3
Fukagawa Ward
43,950
40,743
15
92.7
Total buildings
Tokyo City
Total
16
Map of Tokyo prefecture (before 1932)
Minaniadachi
county
Nishitama county
Kitatoshima county
Kitatama county
Toyotama
county
Tokyo city
Minamikatsushika
county
Minamitama county
Ebara county
参考1　東京府(1932年まで）
17
Map of Tokyo city (before 1932)
Mimamiadachi
county
Kitatoshima county
Shitaya
Koishikawa
Hongo
Ushigome
Kanda
Yotsuya Kojimachi
Toyotama
county
Akasaka
Asakusa
Mimamikatsushika
county
Honjo
Nihonbashi
Kyobashi
Fukagawa
Azabu
Shiba
Ebara county
参考2　東京市周辺部（
1932年まで）
18
Estimation of the scale of damage: Comparison
with Kobe Earthquake in 1995
• Estimated amount of damage by the Great Kanto
Earthquake
– 5.5 billion yen at 1923 price
→6,168 billion yen at 1995 price
=62.1% of that by Kobe Earthquake
• Ratio to GNP
– Great Kanto Earthquake 35.4%
– Kobe Earthquake
2.1%
19
Proportion of the population of Tokyo city
Figure 1 Proportion of the population of Tokyo Prefecture and Tokyo City
0.120
0.100
0.080
Tokyo Prefecture
Tokyo City
0.060
0.040
0.020
0.000
1893
1898
1903
1908
1913
1918
1923
1928
1933
1938
Source：Statistics Bureau of the Ministry of Internal Affairs and Communications ed. (2006);
Statistical Yearbook of Tokyo Prefecture, various issues．
Note：From 1893 to 1918, Otsu-type de facto population．From 1923 to 1938, de facto population.
Due to the adustment of the civil register, the population of Tokyo City incontinuously declined in
1909. Hence, we adjusted the population of Tokyo City before 1909, using the ratio of the de facto
l i
f T k Ci i 1908
h
l i
f T k Ci i 1908 i h T k Ci
20
Changes In geographic distribution in industrial workers
Number of workers
1922
1923
1936
1922
1923
1936
183,521
119,012
376,718
100.00
100.00
100.00
Kojimachi Ward
2,335
1,671
4,008
1.27
1.40
1.06
Kanda Ward
5,984
1,435
7,676
3.26
1.21
2.04
Nihonbashi Ward
2,075
552
3,105
1.13
0.46
0.82
Kyobashi Ward
13,914
2,154
12,810
7.58
1.81
3.40
Shiba Ward
15,684
6,456
23,955
8.55
5.42
6.36
Azabu Ward
2,567
2,486
4,019
1.40
2.09
1.07
Akasaka Ward
421
568
844
0.23
0.48
0.22
Yotsuya Ward
675
834
934
0.37
0.70
0.25
Ushigome Ward
2,838
3,216
4,668
1.55
2.70
1.24
Koishikawa Ward
6,300
6,835
6,641
3.43
5.74
1.76
Hongo Ward
2,388
1,611
3,397
1.30
1.35
0.90
Shitaya Ward
3,227
1,827
6,564
1.76
1.54
1.74
Asakusa Ward
3,471
866
9,486
1.89
0.73
2.52
Honjo Ward
23,206
7,613
31,582
12.64
6.40
8.38
Fukagawa Ward
13,525
2,176
12,670
7.37
1.83
3.36
Total
Tokyo City
Percentage
21
Correlation between the earthquake damage and
change in the worker share from 1922 to 1923
Figure 4-A Correlation between the damage by the Earthquake and the change
in the worker share from 1922 to 1923
Growth rate of worker share
from 1922 to 1923
0.100
0.080
ρ=-0.706
0.060
0.040
0.020
0.000
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Proportion of completely burnt and
destroyed buildings
-0.020
-0.040
-0.060
-0.080
22
Testing the persistence of the shock
by the Earthquake (Davis and Weinstein 2002)
sit  i  it
it  1  it it  1
sit  1  sit  (   1)it  [it  1   (   1)it  1]
si1936  si1923  (   1)( si1923  si1922 )  ei
ρ=1：The influence of the temporary shock was persistent
ρ<1：The influence of the temporary shock faded out at least in the
long-run
23
Instrumental variable for sij1923-sij1922
• Burn
– The ratio of the buildings totally burnt of
destroyed to the buildings just before the
Earthquake
24
Power of IV (1)
Dependent variable：si1923-si1922
BURN
-1.758
Si1922-1915
0.057
R2
0.728
Obs.
(-0.162) ***
(0.040)
97
Note: White heteroschedasticity robust standard errors are in parentheses.
*** statistically significant at 1% level.
25
Control variables
• sij1922-sij1915 : Trend of deurbanization of industry i
• Density1923 : Level of congestion just after the Earthquke
• Indareai: Ratio of the area that was designated as
“industrial zone” by the City Area Architecture Law
in ward i
• Comareai : Ratio of the area that was designated as
“commercial zone” by the City Area Architecture
Law in ward i
• Readjusti : Ratio of the area where the division of land
was readjusted by the government policy
26
27
Estimation results for the manufacturing
industry total
Dependent variable: sij1936-sij1923
(1)
(2)
sij1923-sij1922
-0.580
(0.109)
sij1922-sij1915
-0.105
(0.125)
-0.013
(0.354)
-0.146
(0.116)
-0.703
(0.284)
**
Indarea
1.768
(0.487)
***
Comarea
1.190
(0.597)
**
Readjust
1.449
(4.520)
-0.211
(0.218)
Density1923
Const.
Number of obs.
R2
-0.003
(0.092)
***
(3)
-0.609
(0.098)
-0.121
(0.108)
-0.851
(0.232)
0.316
(0.134)
***
***
97
97
97
0.454
0.528
0.445
28
Summary of estimation results
ρ=0.420 for the manufacturing industry as a whole, in case we do
not control for policy variables
→More than half of the influence of the Earthquake had
disappeared by 1936
• ρis close to 1, in case we control for policy variables
•
→Influence of the temporary shock of the Earthquake was not
persistent, but the recovery of the spatial distribution of industries
can be basically attributed to policy intervention
29
Testing the persistence of the shock
by the Earthquake by industry
sij1936  sij1923   ( i  1)( sij1923  sij1922 ) * Industry i  eij
i
30
Estimation results by industry
Dependent variable: sij1923-sij1936
(1)
(2)
(sij1923-sij1922)*Textile
-0.752
(0.147)
(sij1923-sij1922)*Machinery and Metal
-0.057
(0.273)
(sij1923-sij1922)*Chemical
-0.487
(0.201)
(sij1923-sij1922)*Foods
-0.676
(sij1923-sij1922)*Miscellaneous
sij1922-sij1915
-0.772
(0.159)
-0.468
(0.252)
-0.085
(0.256)
0.578
(0.545)
**
-0.521
(0.201)
**
-0.113
(0.408)
(0.180)
***
-0.709
(0.167)
***
-0.187
(0.330
-0.807
(0.311)
**
-0.852
(0.323)
**
-0.032
(0.621)
-0.104
(0.133)
-0.120
(0.116)
-0.164
(0.119)
-0.895
(0.241)
-0.686
(0.276)
**
Indarea
1.772
(0.557)
***
Comarea
1.287
(0.570)
**
Readjust
-0.638
(4.289)
-0.232
(0.234)
Density1923
Const.
Number of obs.
R2
-0.059
(0.094)
***
(3)
0.333
(0.138)
***
***
**
97
97
97
0.409
0.488
0.441
*
31
Summary of estimation results by industry
• Persistence of influence differed across industries
– ρ is close to 1 for machinery and metal industry, even if we do
not control for policy variables
– ρ is smaller for textile, food and miscellaneous industries
32
33
Concluding remarks
•
The Great Kanto Earthquake of 1923 gave a serious shock to the Japanese
economy, 16.9 times larger than Kobe Earthquake of 1995, in terms of the
ratio of damage to GNP
•
Investigating the mechanisms determining the geographic
distribution of industries, using that event as a natural experiment
•
Testing the persistence of the influence of the shock on the distribution of
workers
– For the manufacturing industry as a whole, the influence of the shock was not
persistent, but it was basically due to the policy intervention
→Power of spontaneous recovery would not be large
→Suggesting multiple equilibria
– Persistence differed across industries
• Influence was particularly persistent for machinery and metal industry
• That industry was composed of numerous small and medium-sized firms
• Inter-linkage of many firms might be a source of the multiple equilibria
34