An Empirical Analysis of Disasters on Regional Economy Case Study of 2000 Flood Disaster in Japan by use of regional GDP data

Transcription

1 An Empirical Analysis of Disasters on Regional Economy Case Study of Flood Disaster in Japan by 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, ADBInstitute

2 Y=F(Kp, L, Kg, D)

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

4 () Effect of Disaster on Demand Side Aggregate Demand of the Region Y = C + I + G + EXP IMP Transmission of Natural Disaster Decline in consumption (C) Decline in Investment (I) Decline in Exports (EXP) Overall Decline in GDP

5 Trans-log Production Function 5

6 Macro Estimation of Japan: Y=F(Kp,L,Kg) Direct Effect (Kg) Indirect Effect (Kp) Indirect Effect (L) Direct Effect (Kg) Indirect Effect (Kp) Indirect Effect (L) Flood 6

7 Komaki Iwakura Haruhi Nagoya Nishshin Toyoake Tokai Obu Chita Kariya Takahama Tokoname Anjo Handa Hekinan Nishio Kota Water AICHI PREFECTURE JAPAN Toyota Okazaki Yahagi river Toyohashi Not U type dynamics, not statistically significant Flood: September U type dynamics, but not statistically significant Analysis of cities U type dynamics and statistically significant Anjo Chita 3 Handa 4 Haruhi 5 Iwakura 6 Kariya 7 Komaki Kota Nagoya Nishio Nishshin Obu 3 Okazaki 4 Takahama 5 Tokai 6 Tokoname 7 Toyoake Toyohashi 7 Toyota

8 Flood GDP Graphical explanation of the model Y non-affected δ, Difference-in-Difference Coefficient Y non-affected, before Y affected Y affected, before Time Y it = α i + φ t + δ D flood D after it + ε it Y it - GDP growth rate; α i - sum of autonomous and region specific rate of growth; φ t - year specific growth effect; D flood D after it - dummy variable indicating that observation belong to affected group after flood period; δ- difference in difference coefficient; ε it - error term.

9 Not U type dynamics, not statistically significant Iwakura Komaki Water U type dynamics, but not statistically significant U type dynamics and statistically significant Haruhi Tokoname Nagoya Nishshin Toyoake Tokai Obu Chita Kariya Takahama Anjo Handa Hekinan Nishio AICHI PREFECTURE Toyota Okazaki Kota Toyohashi Anjo Chita 3 Handa 4 Haruhi 5 Iwakura 6 Kariya 7 Komaki Kota Nagoya Nishio Nishshin Obu 3 Okazaki 4 Takahama 5 Tokai 6 Tokoname 7 Toyoake Toyohashi Toyota

10 Difference in difference estimation coefficients, (million Japanese Yen) Anjo Chita 3 Handa 4 Haruhi 5 Iwakura Variable DiD_*City ** DiD_*City ** DiD_*City ** DiD_*City * DiD_*City 734.* DiD_3*City * * DiD_4*City 6.4*** DiD_5*City 53.7*** ** -.7 DiD_6*City 35.5*** ** 55.*** DiD_7*City *** ** 365.7*** DiD_*City 37.*** DiD_*City *** DiD_*City 63.75** 56.34** DiD_*City 34.57*** 337.6*** ** DiD_*City *** 63.75* 5.36** 73.56*** DiD_3*City 73.54*** ** 74.73*** DiD_4*City 5.3*** * 76.53* GDP_Prefecture.3***.4***.4***.6***.3*** _cons 363.*** 3436.*** 5.*** 5375.*** *** N r F

11 Flood Difference in difference estimation coefficients, mln. JPY 6 4 KASUGAi City Lehman Shock

12 Kasugai City (Only years impact) GDP % Year % % % 3

13 Flood Lehman Shock - Difference in difference estimation coefficients, million. JPY Agricultural Region: Big Drop It took 3 years for the recovery Iwakura % -6 - Agriculture

14 Difference in difference estimation coefficients, (million JPYen) 6 Kariya 7 Komaki Kota Nagoya Nishio Variable DiD_*City *** DiD_*City *** -43 DiD_*City *** DiD_*City * 63.** -7.34*** DiD_*City ** -545.*** DiD_3*City *** *** 55. DiD_4*City *** *** DiD_5*City *** 376.*** -77.*** 3576.** DiD_6*City ** 4.55*** *** 64.77** DiD_7*City 3.64*** 7.7*** 65.5*** -77.6*** 345.*** DiD_*City 344.7** *** -334.*** DiD_*City *** -.*** DiD_*City *** -46*** DiD_*City *** *** * DiD_*City 33.*** *** *** DiD_3*City 57.4*** *** *** ** DiD_4*City 435.5*** *** *** GDP_Prefecture.76***.5***.35***.7***.7*** _cons 5.*** 65.*** 5.7*** 4755.*** 3.3*** N r F

15 Flood Lehman Shock Difference in difference estimation coefficients, million. JPY Services sector : 4 years decline Nagoya % - -5

16 Nagoya City (continuous decline) ΔGDP % Year % % % 7 6

17 Not U type dynamics, not statistically significant Iwakura Komaki Haruhi Water U type dynamics, but not statistically significant U type dynamics and statistically significant AICHI PREFECTURE Cities Nagoy a Nishshi n Anjo Chita 3 Handa 4 Haruhi Tokai Obu Toyoake Toyota Yahagi river 5 Iwakura 6 Kariya 7 Komaki Kota Nagoya Tokoname Chita Takahama Kariya Anjo Okazak i Nishio Nishshin Obu 3 Okazaki Hand a Hekinan Nishio Kota 4 Takahama 5 Tokai 6 Tokoname 7 Toyoake Toyohashi Toyota Toyohashi 7 Map source: SATO Teruko (

18 Difference in difference estimation coefficients, mln. JPY 6 Tokoname 7 Toyoake Toyohashi Toyota Variable DiD_*City ** *** DiD_*City ** *** DiD_*City ** *** DiD_*City * *** DiD_*City *** DiD_3*City *** DiD_4*City ** 7.*** DiD_5*City ** 7375.*** DiD_6*City *** *** DiD_7*City *** 333.*** DiD_*City *** DiD_*City *** DiD_*City DiD_*City *** DiD_*City *** DiD_3*City *** DiD_4*City *** GDP_Prefecture.35***.66***.7***.435*** _cons 6.4*** 77.34*** 5.54*** *** N r F

19 Flood Lehman Shock Difference in difference estimation coefficients, million JPYen (Exports) Only year damage: Lehman was bigger 5 5 Toyota Manufacturing Exports 3 years 5-3% Toyota city

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

21 Flood Prevention: Impact on GDP, Agricultural Sector --- GDP decline (-35%), Services Sector --- GDP decline (-3%) 3, Domestic Manufacturing Small decline in GDP 4, Export Oriented Manufacturing -- Small decline Flood Effects 5-% 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

22 Fiscal Disaster Prevention based on Expected Damages, Forward Looking Finance -, Reserves should be set up -, Expected Damage Compute present value -3, Fiscal support to prevent possible damages, Ex Post Finance -, Issue of Disaster bond (UN-Disaster bond) -, Domestic Purchase of Disaster bond -3, Gradual return from future tax revenues

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24 Japanese Bullet Train 4

25 Impact of Kyushu Shinkansen Rail on CORPORATE TAX revenue during st PHASE OF OPERATION period {4-}, mln. JPY (adjusted for CPI, base ) Variable Regression Regression Regression 3 Regression 4 Regression 5 Treatment [-.] Number of tax payers 5.554* * 5.6* * * [.5] [.5] [.5] [.5] [.] Treatment [-.7] Treatment [-.6] Treatment7-63. [-.7] TreatmentCon -3. [-.65] Constant [-.35] [-.35] [-.35] [-.35] [-.3] N R F Note: Treatment = Time Dummy {-3} x Group. etc. t-values are in parenthesis. Legend: * p<.; ** p<.5; *** p<.. Clustering standard errors are used, allowing for heteroscedasticity and arbitrary autocorrelation within a prefecture, but treating the errors as uncorrelated across prefectures 5 COMPOSITION OF GROUPS Group Group5 Kagoshima Kagoshima Kumamoto Kumamoto Fukuoka Group3 Oita Kagoshima Miyazaki Kumamoto Fukuoka Group7 Kagoshima Kumamoto Fukuoka Oita Miyazaki Saga Nagasaki GroupCon Kagoshima Kumamoto Fukuoka Osaka Hyogo Okayama Hiroshima Yamaguchi

26 Impact of Kyushu Shinkansen Rail on CORPORATE TAX revenue during nd PHASE OF OPERATION period {-3}, mln. JPY (adjusted for CPI, base ) Variable Regression Regression Regression 3 Regression 4 Regression 5 Treatment 733.** [.] Number of tax payers *** *** *** *** 5.647*** [3.3] [3.4] [3.4] [3.4] [3.7] Treatment * [] Treatment ** [.] Treatment7.365** [.34] TreatmentCon 763 [.5] Constant ** ** ** ** ** [-.7] [-.] [-.] [-.] [-.] N R F COMPOSITION OF GROUPS Group Group5 Kagoshima Kagoshima Kumamoto Kumamoto Fukuoka Group3 Oita Kagoshima Miyazaki Kumamoto Fukuoka Group7 Kagoshima Kumamoto Fukuoka Oita Miyazaki Saga Nagasaki Note: Treatment = Time Dummy {-3} x Group. etc. t-values are in parenthesis. Legend: * p<.; ** p<.5; *** p<.. Clustering standard errors are used, allowing for heteroscedasticity and arbitrary autocorrelation within a prefecture, but treating the errors as uncorrelated across prefectures GroupCon Kagoshima Kumamoto Fukuoka Osaka Hyogo Okayama Hiroshima Yamaguchi

27 mln. JPY Total tax revenue, mln. JPY Group 7 Group 5 Group 3 Group Previous period [-] Construction [-3] Operation [4-] Operation Group [-3] 7

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