An Empirical Analysis ofDisasters on Regional Economy
Case Study of 2000 Flood Disaster in Japanby use of regional GDP data

Naoyuki Yoshino, Ph.D
Dean, Asian Development Bank Institute (ADBI)
Professor Emeritus, Keio University, Japan

Umid Abidhadjaev, Ph.D
Research Consultant on Infrastructure, ADBInstitute1Y=F(Kp, L, Kg, D)

2

Economic Effects of disaster(1) Effects on Supply Side (Production)
L= Labor
Kp = Private Capital
Kg = Infrastructure
D = Disaster(GDP) Y =Agriculture
Manufacture
Service

F (Kp, L, Kg, D)(2) Effect of Disaster on Demand Side
Aggregate Demand of the Region
Y = C + I + G + EXP – IMPTransmission of Natural Disaster Decline in consumption (C) Decline in Investment (I) Decline in Exports (EXP) Overall Decline in GDP

Trans-log Production Function5Macro Estimation of Japan: Y=F(Kp,L,Kg)
1956-60 1961-65 1966-70 1971-75 1976-80 1981-85
Direct Effect (Kg)

0.696

0.737

0.638

0.508

0.359

0.275

Indirect Effect (Kp)

0.453

0.553

0.488

0.418

0.304

0.226

Indirect Effect (L)

1.071

0.907

0.740

0.580

0.407

0.317

1986-90

1991-95

1996-00

2001-05

2006-10

0.215
0.195
0.193

0.181
0.162
0.155

0.135
0.122
0.105

0.114
0.091
0.132

0.108
0.085
0.125

Direct Effect (Kg)
Indirect Effect (Kp)
Indirect Effect (L)2002 Flood6

Not “U” type dynamics,
not statistically significant

Flood: September 2000

U” type dynamics,
but not statistically significant

Water
KomakiAnalysis of 19 cities“U” type dynamics

IwakuraHaruhiand statistically significant

AICHI PREFECTURE
JAPAN

NagoyaNishshinToyotaToyoakeTokaiObuKariyaChita
Takahama

Tokoname

Yahagi river

Okazaki

Anjo

Handa
HekinanNishioKota

Toyohashi

1 Anjo2 Chita
3 Handa4 Haruhi
5 Iwakura
6 Kariya
7 Komaki
8 Kota9 Nagoya
10 Nishio
11 Nishshin
12 Obu13 Okazaki
14 Takahama
15 Tokai16 Tokoname
17 Toyoake
18 Toyohashi
7
19 Toyota

Graphical explanation of the modelGDP

Y non-affectedδ,
Difference-in-Difference
Coefficient
Y non-affected,
beforeY affected

Time

Flood

Y affected, before∆𝒀𝒊𝒕 = 𝜶𝒊 + 𝝋𝒕 + 𝜹 𝑫𝒇𝒍𝒐𝒐𝒅 × 𝑫𝒂𝒇𝒕𝒆𝒓𝒊𝒕+ 𝝐 𝒊𝒕∆𝑌𝑖𝑡 - GDP growth rate; 𝛼𝑖 - sum of autonomous and region specific rate of growth; 𝜑𝑡 - year specificgrowth effect; 𝐷𝑓𝑙𝑜𝑜𝑑 × 𝐷𝑎𝑓𝑡𝑒𝑟 - dummy variable indicating that observation belong to affected𝑖𝑡group after flood period; δ- difference in difference coefficient; 𝜖 𝑖𝑡 - error term.

Not “U” type dynamics,
not statistically significant

Water
KomakiIwakuraU” type dynamics,
but not statistically significant“U” type dynamics
and statistically significant

HaruhiAICHI PREFECTURE
NagoyaNishshinToyotaToyoake
Tokai
Obu

Takahama

Tokoname

OkazakiKariya

Chita

Anjo

Handa
HekinanNishioKota

Toyohashi

1 Anjo2 Chita
3 Handa4 Haruhi
5 Iwakura
6 Kariya
7 Komaki
8 Kota9 Nagoya
10 Nishio
11 Nishshin
12 Obu13 Okazaki
14 Takahama
15 Tokai16 Tokoname
17 Toyoake
18 Toyohashi
19 Toyota

Difference in difference estimation
coefficients, （million Japanese Yen)
Variable
DiD_1998*City
DiD_1999*City
DiD_2000*City
DiD_2001*City
DiD_2002*City
DiD_2003*City
DiD_2004*City
DiD_2005*City
DiD_2006*City
DiD_2007*City
DiD_2008*City
DiD_2009*City
DiD_2010*City
DiD_2011*City
DiD_2012*City
DiD_2013*City
DiD_2014*City
GDP_Prefecture
_cons
N
r2
F

1 Anjo2 Chita

3 Handa4 Haruhi

5 Iwakura

20434.352
47734.918
40106.972
43708.874
73114.89*
75675.62*
106219.4***
150032.87***239151.15***
256661.56***103791.02***54733.342
86913.752**
132401.57***301452.53***
273028.54***
189025.32***.00112838***
203963.91***875
0.169564
9.885042

20217.742
23325.139
36643.733
44822.471-11249.093
4468.9847
2017.696-22016.285-4258.4268
9124.8808-49773.431
114540.81***100956.34**
103937.16***69329.751*-17445.791
7653.2476.00108419***
193436.09***875
0.034211
1.7148836654.248
28372.087
6895.1827
32234.741
28564.366
5722.5355-1721.6099
41655.427
80697.768**
95121.835**
21068.957
49424.582
45915.636
14062.713
85118.236**
92004.894**
65471.362*.00111904***
195889.8***875
0.0271711.352153

97035.893**
98166.949**
83852.516**
72895.231*
27927.281
67093.444*
57473.857
79766.134**
105511.8***136508.27***30365.093
63805.519
49744.488
83423.113**
107993.56***117224.73***78609.523*.00116891***
205375.1***875
0.0485122.468289-3931.4871-792.32798-7569.5478-2768.5402-15292.491-12701.166-8806.4561-10108.217-9904.793-12325.069-13145.828-11944.99-10132.977-5254.5601-9036.835-10703.308-15470.573.00108831***186576.36***875
0.000306
0.014811

Difference in difference estimation
coefficients, mln. JPY
4 KASUGAi City

LehmanShock

180000Flood

160000140000120000100000
80000
60000
40000
20000
0
1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

11

Kasugai City (Only 2 years impact)
GDP
%
Year
• 96738• 85988• 40846• 79942-11.1%-52.4%+95.7%

2000
2001
2002
2003

12

Difference in difference estimation
coefficients, million. JPY
Agricultural Region: Big Drop
It took 3 years for the recovery
Iwakura
0
1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

-6000
-8000
-10000-12000

Flood

-4000

－35％

-14000-16000-18000Agriculture

Lehman
Shock

-2000

2010

2011

Difference in difference estimation
coefficients, (million JPYen)
Variable
DiD_1998*City
DiD_1999*City
DiD_2000*City
DiD_2001*City
DiD_2002*City
DiD_2003*City
DiD_2004*City
DiD_2005*City
DiD_2006*City
DiD_2007*City
DiD_2008*City
DiD_2009*City
DiD_2010*City
DiD_2011*City
DiD_2012*City
DiD_2013*City
DiD_2014*City
GDP_Prefecture
_cons
N
r2
F

6 Kariya

7 Komaki

8 Kota9 Nagoya

10 Nishio-26571.579-14574.012
36527.193
51485.729
35221.495
6101.3027
36045.384
40443.531
28839.231
212318.64***
83448.972**
54104.093
34998.01
62653.738
123823.82***
250070.48***143511.85***.00107969***202905.8***875
0.096445
5.167467

49656.273
32466.25
49028.611
76405.078*
36063.62
28416.159
58588.036
129821.43***89806.145**
107818.87***-7815.8008-35692.81-55207.546-1169.5441
8526.7281
16620.008
53812.022.00112519***206185.82***875
0.04301
2.175786

5269.5621
28427.946
56954.11886930.991**
98255.833**
151568.88***167459.89***
193726.09***
210114.55***226885.95***
169392.34***
175429.55***
133756.89***160363.5***
146453.17***
225863.13***207863.98***.00110035***
188520.27***875
0.148796
8.462678-122128.83***-265427.14***-399787.84***-988127.34***-1195450.9***-1276363.4***-967076.44***-928771.82***-804326.47***-701078.62***-1393814.8***-2108818.1***-2091046***-1794032.4***-1875063.4***-1642416.9***-1500546.4***.00109117***487551.19***869
0.877915
345.5905-31538.9-143104956.4535448.8937632.1
59205.29
62725.9393576.882**
98064.717**
123405.81***-48507.2-44306.7-62398.5-73411.055*-8697.4689253.766**
48435.43.00110279***203122.93***875
0.055887
2.865771

Difference in difference estimation
coefficients, million. JPY
Services sector : 4 years decline
Nagoya0-1000000-1500000-2000000-2500000

2000

2001

-23%

2002

2003

2004

2005

2006

2007

2008

2009

LehmanShock-500000

1999

Flood

1998

2010

2011

Nagoya City (continuous decline)
ΔGDP
%
Year
-400810
2000-988956 -146.7 ％ 2001
-1196442 -20.9 ％ 2002
-1277420 -6.7
2003
-967073 -24.2
2004-927767 -4.0
2005-802147 -13.5
2006-697420 -13.0 %2007
16

Not “U” type dynamics,
not statistically significant
Water
KomakiU” type dynamics,
but not statistically significant

Iwakura“U” type dynamics
and statistically significant

HaruhiAICHI PREFECTURE

Cities1 AnjoNagoya

2 ChitaNishshin

3 Handa4 Haruhi
5 Iwakura

Toyota6 Kariya
Toyoake

7 Komaki

Tokai
Obu

8 KotaYahagi river

9 Nagoya
10 Nishio

Takahama

Tokoname

Okazaki

KariyaChita

11 Nishshin
12 ObuAnjo

13 Okazaki
14 TakahamaHanda

15 Tokai
Hekinan

16 Tokoname

Nishio17 Toyoake

Kota

18 Toyohashi
19 Toyota
Toyohashi

17

Map source: SATO Teruko (20Difference in difference estimation
coefficients, mln. JPY
Variable
DiD_1998*City
DiD_1999*City
DiD_2000*City
DiD_2001*City
DiD_2002*City
DiD_2003*City
DiD_2004*City
DiD_2005*City
DiD_2006*City
DiD_2007*City
DiD_2008*City
DiD_2009*City
DiD_2010*City
DiD_2011*City
DiD_2012*City
DiD_2013*City
DiD_2014*City
GDP_Prefecture
_cons
N
r2
F

16 Tokoname

17 Toyoake

18 Toyohashi

19 Toyota-8562.73-18242.6-19822.1
2732.018
3760.841
11312.48
29788.14
48059.6148248.44
23603.12
12321.03
7616.929-1421.19
6972.406
16225.48
19343.31
25750.66.00109351***
188680.04***875
0.008451
0.412617

6226.992
6968.685
8887.869
9695.406
1935.691
3705.513
7771.163
14405.79
28231.28
30785.76
23778.38
25467.527850.532151.0222025.9320326.55
31818.67.00109661***
187790.34***875
0.005903
0.287489

88268.296**
92331.453**
95117.057**
75976.756*
42784.553334.34
97731.712**
94531.555**
109501.98***
143163.04***
1342.834-32717.3-29897.2
3184.474-2010638040.06
49406.01.00118027***
218905.54***875
0.053797
2.752503

587035.52***676380.53***362390.57***705729.36***
1191326.6***902530.03***
1019870.9***
1273758.2***
1735146.9***
2131338.1***
724527.74***223900.63***
42400.69
606963.58***
1372324.8***1406327.6***
1662908.5***.00143915***245345.62***875
0.830496
237.1963

Lehman
Shock

Difference in difference estimation
coefficients, million JPYen (Exports)
Only 1 year damage: Lehman was bigger
Toyota3 years
2500000

Manufacturing
Exports

2000000

Flood

1500000

1000000

Toyota city

500000

－１３％
0
1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

Characteristics of Each City are different
Agriculture, Services, Export Manufacturing
GDP damages and GDP recoveries

Flood Prevention: Impact on GDP
1, Agricultural Sector --- GDP decline (-35%)
2, Services Sector --- GDP decline (-23%)
3, Domestic Manufacturing – Small decline in GDP
4, Export Oriented Manufacturing -- Small decline

Flood Effects  15-20% decline in
GDP in the region
Flood Impacts for about 3 years
Same Methods can be applied to various disasters
 Need for Disaster Prevention

 Ex Post Policy: such policies as Low
interest government loans, emergency loans

Fiscal Disaster Prevention
based on Expected Damages
1, Forward Looking Finance

1-1, Reserves should be set up
1-2, Expected Damage  Compute present value
1-3, Fiscal support to prevent possible damages

2, Ex Post Finance
2-1, Issue of Disaster bond (UN-Disaster bond)
2-2, Domestic Purchase of Disaster bond
2-3, Gradual return from future tax revenues

Japanese Bullet Train24

Impact of Kyushu Shinkansen Rail on
CORPORATE TAX revenue during 1st PHASE OF OPERATION period{2004-2010} , mln. JPY (adjusted for CPI, base 1982)
1
9
8
2

1
9
8
3

1
9
8
4

Variable
Treatment2
Number of tax
payers

1
9
8
5

1
9
8
6

1
9
8
7

1
9
8
8

1
9
8
9

Regression 1-4772.54[-0.2]
5.8952514*[1.95]

Treatment3

1
9
9
0

1
9
9
1

1
9
9
2

1
9
9
3

1
9
9
4

Regression 2

5.8957045*[1.95]-15947.8[-0.87]

Treatment5

1
9
9
5

1
9
9
6

1
9
9
7

1
9
9
8

Regression 35.896112*[1.95]

1
9
9
9

2
0
0
0

2
0
0
1

2
0
0
2

Regression 45.8953585*[1.95]

2 2
0 0
0 0
3 4

2
0
0
5

2
0
0
6

Regression 55.8629645*[1.91]-13250.4[-1.06]

Treatment7-6883.09[-0.7]

TreatmentCon
Constant-665679[-1.35]-665418[-1.35]-665323[-1.35]-665358[-1.35]-28030.8[-0.65]-658553[-1.32]

N
R2
F

799
0.269215
1.934589

799
0.269281
2.106448

799
0.269291
2.074548

799
0.2692412.100607

799
0.269779
8.497174

2
0
0
7

2
0
0
8

2
0
0
9

2
0
1
1

2
0
1
2

2
0
1
3

COMPOSITION OF
GROUPSGroup2
Group5
Kagoshima
Kagoshima
Kumamoto
Kumamoto
FukuokaGroup3
Oita
Kagoshima
Miyazaki
Kumamoto
FukuokaGroup7
Kagoshima
Kumamoto
FukuokaOita
Miyazaki
Saga
Nagasaki

Note: Treatment2 = Time Dummy {1991-2003} x Group2. etc. t-values are in parenthesis. Legend: * p<.1; ** p<.05; *** p<.01.
Clustering standard errors are used, allowing for heteroscedasticity and arbitrary autocorrelation within a prefecture,
but treating the errors as uncorrelated across prefectures
25

2
0
1
0

GroupCon
Kagoshima
Kumamoto
FukuokaOsaka
Hyogo
Okayama
Hiroshima
YamaguchiImpact of Kyushu Shinkansen Rail on
CORPORATE TAX revenue during 2nd PHASE OF OPERATION period{2011-2013} , mln. JPY (adjusted for CPI, base 1982)
1
9
8
2

1
9
8
3

1
9
8
4

Variable
Treatment2
Number of tax
payers

1
9
8
5

1
9
8
6

1
9
8
7

1
9
8
8

1
9
8
9

Regression 1
72330.012**[2.2]
5.5277056***[3.13]

Treatment3

1
9
9
0

1
9
9
1

1
9
9
2

1 19
9 94
9
3

Regression 2

5.5585431***[3.14]
104664.34*[2]

Treatment5

1
9
9
5

1
9
9
6

1
9
9
7

1
9
9
8

Regression 3

5.558603***[3.14]

1
9
9
9

2
0
0
0

2
0
0
1

2
0
0
2

Regression 45.5706545***[3.14]

20
0
6Regression 55.9640287***[3.07]

80998.365**[2.34]

TreatmentCon

N
R2
F

2
0
0
5

82729.673**[2.1]

Treatment7

Constant

2 2
0 0
0 0
3 4

-568133.98**[-2.07]-573747.28**[-2.08]-574245.87**[-2.08]-576867.56**[-2.09]179632[1.58]-642138.87**[-2.1]

611
0.350653
5.062509

611
0.352058
5.486197

611
0.352144
5.351791

611
0.352874
5.431088611
0.364088
16.55518

2
0
0
7

2
0
0
8

2
0
1
0

2
0
1
1

2
0
1
2

2
0
1
3

COMPOSITION OF
GROUPSGroup2
Group5
Kagoshima
Kagoshima
Kumamoto
Kumamoto
FukuokaGroup3
Oita
Kagoshima
Miyazaki
Kumamoto
FukuokaGroup7
Kagoshima
Kumamoto
FukuokaOita
Miyazaki
Saga
Nagasaki

Note: Treatment2 = Time Dummy {1991-2003} x Group2. etc. t-values are in parenthesis. Legend: * p<.1; ** p<.05; *** p<.01.
Clustering standard errors are used, allowing for heteroscedasticity and arbitrary autocorrelation within a prefecture,
but treating the errors as uncorrelated across prefectures
26

2
0
0
9

GroupCon
Kagoshima
Kumamoto
FukuokaOsaka
Hyogo
Okayama
Hiroshima
YamaguchiTotal tax revenue, mln. JPY
600000500000300000200000mln. JPY

400000Group 7
Group 5

Group 3

100000Group 20Previous period Construction[1982-1990][1991-2003]

Operation 1[2004-2010]27

Operation 2Group 8[2011-2013]

The Southern Tagalog Arterial Road
(STAR Highway), Philippines, Manila

28