Destruction, Disinvestment, and Death Economic and Human Losses Following Environmental Disaster

8/9/2019 Destruction, Disinvestment, and Death Economic and Human Losses Following Environmental Disaster

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Destruction, Disinvestment, and DeathEconomic and Human Losses Following Environmental Disaster

Jesse K. Anttila-Hughes Solomon M. HsiangUniversity of San Francisco Princeton & UC Berkeley

June 10th, 2013ADB, Manila

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Motivation

Environmental disasters produce major social costs

Anttila-Hughes & Hsiang Losses Following Environmental Disaster

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Motivation

Environmental disasters produce major social costs→

297,000 deaths & $193B in damage in 2010 (Swiss Re, 2011)


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Motivation



Anecdotal evidence, humanitarian agency reports, and case studiessuggest that damages that accrue during disasters’ aftermath aresubstantial compared to immediate destruction, but harder to

document


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Motivation




documentFew well-identified empirical studies of post-disasters outcomes exist,especially at the micro level


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Motivation

Environmental disasters produce major social costs→ 297,000 deaths & $193B in damage in 2010 (Swiss Re, 2011)


documentFew well-identified empirical studies of post-disasters outcomes exist,especially at the micro level

Recent results from the environment & development literature

suggest that even mild aggregate shocks can have very detrimentaldevelopment impacts, especially on human capital formation

Lagged indirect damages from severe aggregate shocks such asdisasters might have particularly negative effects on development


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Question

Do disasters influence economic development in ways that emerge

only after exposure to the physical event stops?

How large are lagged effects relative to immediate ones?


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Question




What mechanisms drive lagged losses?


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Question





We systematically and quantitatively examine the effect of a specifictype of disaster (typhoons) on a specific set of outcomes (householdeconomic and health survey data) in a specific context (Philippines,1979-2008)




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Question






Our results may generalize, with caveats, to some of the 2.3 billionpeople exposed to tropical cyclone risk.


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Question






Our results may generalize, with caveats, to some of the 2.3 billionpeople exposed to tropical cyclone risk.

As well as inform the literature on the future costs of climate change


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Contributions

We find:


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Contributions

We find:

Post-disaster losses (human & economic) dramatically exceed

immediate losses


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Contributions

We find:


immediate losses

Post-disaster deaths are likely caused by deteriorating economicconditions


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Contributions

We find:


immediate losses


Specifically, we find that in the year following a typhoon:


C

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Contributions

We find:


immediate losses


Specifically, we find that in the year following a typhoon:household incomes decline


C ib i



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Contributions

We find:


immediate losses



households compensate by reducing expenditures


C ib i

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Contributions

We find:


immediate losses




purchase of nutritious foods declines dramatically“investments” (eg. health, education) generally fall the most


C t ib ti

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Contributions

We find:


immediate losses





infant mortality rises sharply


C t ib ti

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Contributions

We find:


immediate losses





infant mortality rises sharply

overwhelmingly femalestructure exhibits an “economic fingerprint”risk is highest for infants competing with [male] siblings


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Background and data


Prior literature

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Prior literature

Country-level regressions on estimated immediate damageseg. Strömberg (JEP, 2007); Loayza et al. (WB, 2009); Noy (JDE, 2009)

Damages reporting endogenous (Kahn - ReStat, 2005)

Hazard exposure measures, when used, are coarse Quote

Aggregation may obscure more subtle effects

Microeconomic analyses of non-catastrophic environmental losseseg. Townsend (JEP, 1995); Maccini & Yang (AER, 2009); Schlenker &Roberts (PNAS, 2009); Hidalgo et al. (ReStat, 2010)

Impact of capital destruction may differ from the impact of absentinputs or productivity changes

Adaptive responses may differ by event type and intensity


Our approach: physical model microeconometrics

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Our approach: physical model, microeconometrics



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We build a physical model to estimate time-varying incidence of aspecific set of disasters (typhoons) using historical data on theirlocations and wind speeds



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We merge our region-specific time-varying measures of typhoonexposure with time-varying household survey data for those regions



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We merge our region-specific time-varying measures of typhoonexposure with time-varying household survey data for those regions

We then apply a quasi-experimental approach to make causalinferences about the effects of typhoon exposure on economic and

health outcomes


What is a typhoon?

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What is a typhoon?

Typhoon - A tropical cyclone in the West Pacific. They are the same

phenomena as hurricanes/tropical storms (Atlantic) and cyclones (Indian);but different from tornados or thunderstorms.


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Source: NASA

Typhoon data

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Typhoon data

We estimate the maximum wind speed experienced at each location

(3.2×

3.2 km) for every typhoon using the Limited Information CycloneReconstruction and Integration for Climate and Economics (LICRICE)developed in Hsiang (PNAS, 2010). National damages vs. wind speed

2,246 storms (72,901 observations for 6hr intervals) in the West

Pacific basin during 1950-2008.411 storms passed over the Philippines during 1979-2008 (13.7storms/yr).

Take annual maximum and spatial average to aggregate over 81

administrative units.No explicit modeling of rain/flood/surge, but effects are captured tothe extent that they are correlated with winds.


Typhoon reconstruction (LICRICE)

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Typhoon reconstruction (LICRICE)

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$

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!

% #$'

!

"

#

$

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!

% #$'

$

#$

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($

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)*+*,-

./+01 2345 -6**5 0+ -7,809*

)*+*,-

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-+/,)

% ; #? @

-+/,)

A ; ' B)C-D

) C -




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Watch LICRICE Flipbook: Super Typhoon JoanJoan flipbook


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Typhoon climatology

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yp gy

View boxplot


Identification strategy

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gy

We may interpret typhoon incidence causally because:

Typhoon trajectory and intensity are stochastic, generating exogenousvariation in the timing of exposure

Typhoons are observed remotely (by satellite), so measurements areplausibly unbiased by local effects

Typhoon exposure is well defined in time and intensity, so treatment

effects and their lags are unambiguousHouseholds not exposed to typhoons allow us to control for temporaland spatial effects unrelated to typhoons (i.e., year and region fixedeffects)

Sorting on (conditioned) treatment is still a possible threat to the validityof our identification strategy→ where possible we restrict sample to non-migrants→ test and find little reason to believe sorting exists or is driving our result


Economic data

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The Family Income and Expenditure Survey (FIES) is a nationallyrepresentative cross-section administered every 3 years: 1985-2006.

174,896 households matched with typhoon exposure

HHs: 5.2 members, 63% electrified, 55% “strong walls”, 7% w/ car

HH heads: 85% male, 47.6 yrs old, 64% completed primary school

We find no evidence of typhoon-induced migration/sorting Table

Timing of FIES data collection

Health data

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The Demographic and Health Survey (DHS) is a nationallyrepresentative cross-section administered every 5 years: 1993-2008.

Fertility, child and health data for women age 15-49Restrict sample to 24,841 non-migrant mothers(Kudamatsu, Persson, and Strömberg, 2011)

Women: 28.7 yrs old, 48% married, 2 children born

Reconstruct mother-by-year panel of child birth/death (265,430 obs.)

Survey Timing

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Results: Economic Losses


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Post-disaster income losses


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0 1 2 3 4

Income loss timing

(% per m/s)

Years after storm season

5−10 15−20 25−30 >35−5

0

5

10

15

20

Wind speed (m/s)

t y p h o o n

Income loss in Lag=1

(%)

−0.2

0

0.2

0.4

Robustness


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Households adjust by reducing expenditure


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−5

0

5

10

15

20

25

30

35

40

Wind speed (m/s)

0-5 5-10 10-15 15-20 20-25 25-30 30-35 >35

medical

transport &

communicationeducation

food

fuel E x p e n d i t u r e

r e d u c t i o n s ( % )


Households adjust by reducing expenditure (food)

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−5

0

5

10

15

20

25

30

35

40

Wind speed (m/s)

0-5 5-10 10-15 15-20 20-25 25-30 30-35 >35

E x p e n d i t u r e

r e d u c t i o n s ( % )

meat

fish &

marine

dairy & eggs

fruit

cereal


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Results: Infant Mortality




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Post-disaster infant mortality


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0 1 2 3 4

0

40

80

Infant female deaths

(per million HH per m/s)

0−10 15−20 25−30 >35

0

1000

2000

3000

Infant female deaths in Lag = 1

(per million HH)

Years after storm season Wind speed (m/s)

t y p h o o n

Male response Robustness

What’s causing this post-disaster infant mortality?

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Observations

1 Income, food consumption & health spending decline post-typhoon

2 Infant mortality rises post-typhoon

3 Infant mortality is exclusively in female population (recall, eg.Bhalotra, JDE 2010)

Hypothesis: Post-disaster infant mortality represents “economic deaths,”not “exposure deaths”

Testable predictions

1 Mortality response should reflect ‘when’, ‘where ’, and ‘who ’ of

economic response2 Physical exposure to typhoon not necessary to trigger mortality

3 Resource competition exacerbates mortality


Infant mortality mirrors economic response: “Where?”

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ILC

CGY

CLZ

SLZ

BIC

WVS

CVS

EVS

WMD

NMD

SMD

(slope = 0.91)0

1

2

0 1 2

Reduction in food expenditure

(% per m/s)

Lost income (% per m/s)

ILC

CGY

CLZ

SLZBIC

WVS

CVS

EVS

WMD

NMD

SMD

(slope = 68)

0

100

200

0 1 2

Female Infant Mortality

(deaths per 1M HH per m/s)

Lost income (% per m/s)

The magnitude of local income responses predicts local expenditure andmortality responses in the regional cross-section.


Infant mortality mirrors economic response: “Who?”



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20 40 60 80

0

0.4

0.8

1.2

% p

e r m / s

Income centile

20 40 60 80

Expenditure centile

Expenditure reduction

(one year lag)

0

400

800

d e a t h s p e

r m i l l i o n H H ( p e r m / s )

15 35 55 75

SES centile

95

Income reduction

(one year lag)

Female infant mortality

(one year lag)

Lagged responses appear uniform across income/expenditure/SESdistributions. Model specification


Infant mortality mirrors economic response: “Who?”

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20 40 60 80

0

% p

e r m / s

Income centile

20 40 60 80

Expenditure centile

Expenditure reduction

(six year cumulative)

0

400

800

d e a t h s p e r m i l l i o n H H ( p e r m / s )

15 35 55 75

SES centile

95

Income reduction


Female infant mortality


0.4

0.8

1.2

Cumulative responses are larger for low income/expenditure/SEShouseholds.


Half of affected infants are never exposed to the typhoon

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0

4

8

12

16

-12 -6 0 6

9 months

12 18 24 30 36 42 48

Months relative to typhoon strike

Cumulative female infant deaths (per million HH per m/s)

Births resulting

in mortality

Mortality

exposed

in-utero

Month oftyphoon

conceived after typhoon

Fetal loss Both genders


Sibling competition increases post-disaster mortality risk

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Effect of wind speed on female infant mortality(lagged, deaths per million HH per m/s)

If first born 35.32[23.48]

Additional risk of having 75.70**any older siblings [25.35]

If has only older sisters 75.80[78.97]

Additional risk of having 53.18any older brothers [67.54]

If has only older brothers 121.2*

[57.1]Additional risk of having 0.41

any older sisters [40.1]

Standard errors in brackets. *p


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Implications and Discussion


Post-disaster losses > 15× immediate lossesYear of typhoon Year after

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2

4

6

8

P e r c e n t o f i n c o m e

( 1 9 7 9 − 2 0 0 0 )

0.39% 743 6.59% 11,261

(official estimate) (this study)

economic

loss

(national est.)

economic

loss

(avg. HH)

“economic”

mortality

(infant)

“exposure”

mortality

(all ages)

4

8

12

16

T h o u s a n d d e a t h s ( 1 9 7 9 − 2 0 0 8 )

00

Average effects in an average year

Conceptual implications for policy Exposure by country

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Environmental policy - Substantial losses are mediated by

human-to-human dynamics, not just environment-to-human dynamics.

Development policy - Average exposure has a large economic effect, andmay influence outcomes years later.

Global climate policy - Adaptation to typhoons remains difficult, even forpopulations with centuries of experience. Evidence of adaptation

Disaster relief policy - Interventions that focus on post-disasteroutcomes may have large social gains.


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“Elvira Soriano, 25, and her child Rea, 1, sit on a wooden bed withtheir belongings after their house was destroyed by Typhoon Megi.”EPA/DENNIS M. SABANGAN

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Extra Slides


Regressing GDP on observed damages



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[T]he exogeneity issue can potentially be fully overcome by producing an index of disaster intensity that depends only on the physical characteristics of the disaster... The collection of suchdata from primary sources and the construction of acomprehensive index for the all the different disaster types are

beyond the scope of this paper but may be worth pursing infuture research.

-Ilan Noy, Journal of Development Economics, 2009

Back


Aggregate economic damages - Philippines (EM-DAT)

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8

10

12

14

16

10 15 20 25 30wind speed (m/s)

Log(Reported Damages)

Back


S thC t b tSarangani

Basilan

Typhoons in Provinces of the Philippines: 1950−2008

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0 20 40 60

Annual maximum wind speed (m/s)

Batanes

CagayanIsabelaKalinga

ApayaoIlocos Norte

Mountain ProvinceIfugao

Abra

Camarines NorteIlocos Sur

Aurora

QuirinoCatanduanes

Nueva Vizcaya

BenguetNueva Ecija

Camarines Sur

La UnionQuezon

Pangasinan

RizalBulacan

AlbaySamar

Zambales

Metropolitan ManilaLagunaTarlac

Eastern SamarPampanga

Northern Samar

BataanMasbate

Sorsogon

BiliranCavite

BatangasMarinduque

Oriental Mindoro

RomblonLeyte

Occidental Mindoro

AklanCapizIloilo

AntiqueDinagat IslandsSouthern Leyte

CebuGuimaras

Surigao del NorteNegros Occidental

Bohol

Agusan del NorteSurigao del SurNegros Oriental

PalawanCamiguin

Agusan del Sur

SiquijorMisamis Oriental

Misamis Occidental

BukidnonCompostela ValleyLanao del Norte

Davao del NorteLanao del Sur

Davao Oriental

Zamboanga del NorteZamboanga del Sur

North CotabatoShariff Kabunsuan

Zamboanga Sibugay

Davao del SurMaguindanao

Tawi−Tawi

SuluSultan Kudarat

South Cotabato

Data Assets Income All exp. Food exp. Female Mortality

FIES data is balanced on observables

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(1) (2) (3) (4) (5) (6) (7)

Single Household head characteristics

Total family Completed Completedfamily dwelling Married Male primary secondary

VARIABLES size (%) (%) (%) Uneducated school school

Max windspeed 5-10 m/s 0.0594 -0.81 0.30 -0.30 1.02 -3.06** -0.45[0.0603] [1.27] [0.53] [0.42] [0.81] [1.27] [0.97]

10-15 m/s 0.1480* -1.12 0.68 -0.17 0.67 -2.69* -0.24[0.0781] [1.60] [0.66] [0.65] [0.85] [1.55] [1.27]

15-20 m/s 0.1205 -0.68 0.50 0.25 0.83 -3.00* -1.04

[0.0864] [1.77] [0.65] [0.63] [0.86] [1.64] [1.39]20-25 m/s 0.1968* -0.91 0.70 0.76 0.96 -3.26 0.40[0.1000] [1.84] [0.77] [0.78] [1.02] [2.05] [1.68]

25-30 m/s 0.0748 -0.15 0.28 0.85 0.24 -1.41 2.64[0.1099] [1.87] [0.81] [0.81] [1.06] [2.20] [1.88]

30-35 m/s 0.0808 0.81 0.44 0.53 0.97 -2.14 2.59[0.1139] [2.03] [1.05] [1.04] [1.11] [2.09] [1.82]

35+ m/s 0.1046 -0.49 0.67 1.55* 0.73 -1.21 3.13[0.1171] [2.09] [0.98] [0.90] [1.16] [2.29] [1.94]

Observations 142,789 142,789 142,789 142,789 142,789 142,789 142,789R-squared 0.0145 0.02 0.01 0.01 0.08 0.14 0.24

Out of 49 parameter estimates, six have p


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Back to FIES data


Income response by sector

% change SE N



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% change SE N

Entrep. income -0.28** [0.11] 96,989Crop farming / gardening income -0.29 [0.21] 52,193Trade income -0.18 [0.18] 30,479Livestock / poultry income -0.46 [0.44] 17,158

Gambling winnings 0.28 [0.46] 10,776Fishing income -0.40 [0.24] 10,258Manufact. income 0.08 [0.42] 8,715Transport / storage income 0.15 [0.26] 7,855Services income -0.10 [0.45] 7,011

Forestry / hunting income -0.44 [0.71] 2,537N.A. / entrep. income -1.04 [0.96] 1,454Construct. income -1.56 [1.24] 870

Back to income response


Male infant deaths

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0 1 2 3 4

−100

−50

0

50

100

150

Infant male mortality(deaths per million HH per m/s)

0−10 15−20 25−30 >35

−2000

−1000

0

1000

2000

3000

Wind speed (m/s)

t y p h o o n

Infant male mortality in lag=1(deaths per million HH)

Years after storm season

Back to health results


(1) (2) (3) (4) (5)

I f t I f t I f t I f t I f t

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Infant Infant Infant Infant Infantfemale female female female female

VARIABLES mortality mortality mortality mortality mortality

Max wind speed, T=0 (m/s) -26.15 -18.11 20.31 17.13 23.01(16.68) (18.25) (17.30) (18.22) (23.34)

T + 1 30.44* 27.68* 63.48*** 68.15*** 74.68***(14.98) (14.92) (19.43) (20.85) (21.12)

T + 2 -7.044 -17.00 17.85 22.80 30.34(8.896) (9.758) (15.67) (16.31) (17.99)

T + 3 15.15 2.132 36.05** 31.74** 39.53**(13.02) (14.65) (14.29) (13.92) (16.20)

T + 4 5.903 1.003 39.29 35.65 42.83(15.87) (18.45) (22.65) (25.66) (25.25)

Observations 265,446 265,446 265,446 265,446 265,446

Year FE Y Y Y YRegion FE Y YLagged temp., precip. Y YMother FE Y


30

Typhoon impacts on birth rates

in utero typhoon no in-utero typhoon

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-70

-60

-50

-40

-30

-20

-10

0

10

20

30

-3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

C u m u a t

i v e b i r t h s p e r m i l l i o n h o u s e h o l d s ( p e r m / s )

Months r elative to typhoon impact

Female

Male

t y p h o o n

in utero typhoonexposure

no in-utero typhoonexposure


Birth-rate adjusted male vs. female infant mortality

in-utero no in-utero



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-300

-200

-100

0

100

200

300

400

500

600

700

800

-12 -9 -6 -3 0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48

I n f a n t

d e a t h s p e r a n n u a l

m i l l i o n b i r t h s ( p e r m / s )

Months relative to typhoon impact

Females

Males

t y p h o o n

typhoonexposure

typhoonexposure


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Post-disaster losses > immediate losses (by ×15) Back

Year of typhoon

(EM-DAT estimate)

Year after

(this study)


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2

4

6

8

P e r c e n t o f i n c o m e

( 1 9 7 9 − 2 0 0 0 )

0.39% 743 6.59% 11,261

(EM DAT estimate) (this study)

economic

loss

(national est.)

economic

loss

(avg. household)

“economic”

mortality

(infant)

“exposure”

mortality

(all ages)

4

8

12

16

T h o u s a n d d e a t h s

( 1 9 7 9 − 2 0 0 8 )

00

Average effects in an average year

Evidence of Adaptation



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ILC

CGYCLZ

SLZ

BIC

WVS

CVS

EVS

NMD

(slope = −0.04)

10 20 30

Income lost (%) per 1 m/s in wind speed exposure

climatological wind speed (m/s)

0

1

2

WMD

SMD

Back to policy discussion

Adaptation has limited impact on average cost

Average income lost from previous year’s storm(regional coefficient × climatology)

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ILC

CGY

CLZ

SLZ

BIC

WVS

CVS

EVS

WMD

NMD

SMD

0

5

10

15

20

%

5 10 15 20 25 30

climatological wind speed (m/s)

(regional coefficient × climatology)


Average exposure by country

Distribution of tropical cyclone climates

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>0 5 10 15 20 25 30

10

20

30

40

50

Climatological wind speed (m/s)

C o u

n t r i e s

Distribution of tropical cyclone climates

Taiwan

Madagascar

Philippines

Bangladesh

Japan

Haiti

United States

Vietnam

PanamaZimbabweThe Maldives

Nicaragua


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Are assets present the year following a typhoon? Exposure dist.

7 closed toilet


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0−5 5−10 10−15 15−20 20−25 25−30 30−35 >35

0

1

2

3

4

5

6

Wind speed (m/s)

electricity

strong walls

cartelevision

refrigerator

P r o b a b i l i t y c a p i t a l i s m i s s i n g ( % )


Linking data files60

http://find/


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0

20

40

1980 1990 2000

W i n d s p e e d i n p r o v i n c e ( m / s )

Year


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Linking data files

Demographic & Health Surveys60


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0

20

40

1980 1990 2000


Year


Linking data files

Reconstructed mother-by-year panel of child births & deaths60

http://find/


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0

20

40

1980 1990 2000


Year


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Back


Dependent variable is Income (%)

(1) (2) (3) (4) (5) (6)

year add add add region prov

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year add add add region prov.Wind F.E. prov. HH temp. treat. collapse

(m/s) F.E. controls precip.

t 0.26 -0.10 0.02 -0.00 0.27 -0.01[0.26] [0.14] [0.15] [0.18] [0.16] [0.15]

t − 1 -0.88*** -0.33*** -0.35*** -0.39*** -0.58*** -0.39***[0.19] [0.09] [0.09] [0.10] [0.14] [0.14]

t − 2 1.04*** 0.01 0.00 -0.16 -0.17 0.02[0.32] [0.10] [0.08] [0.12] [0.14] [0.14]

t − 3 0.16 -0.14 -0.17 0.04 -0.22 0.29[0.17] [0.14] [0.13] [0.15] [0.15] [0.18]

Obs. 142,789 142,789 142,779 142,779 174,896 367R2 0.32 0.38 0.57 0.57 0.55 0.95

Standard error in brackets: clustered by region (1-5), Conley/HAC (6).

*p


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October 1970

Hour: 60

115 120 125 1300

5

10

15

20

0

10

20

30

40

50

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Super Typhoon Joan (Sening)

Max wind (m/s)

October 1970

25

60

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October 1970

Hour: 72

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Max wind (m/s)

October 1970

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October 1970

Hour: 84

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Max wind (m/s)

October 1970

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October 1970

Hour: 96

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Max wind (m/s)

October 1970

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Hour: 108

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Max wind (m/s)

October 1970

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Hour: 120

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Max wind (m/s)

October 1970

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Hour: 132

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Max wind (m/s)

October 1970

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Hour: 144

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Max wind (m/s)

October 1970

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Hour: 168

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Destruction, Disinvestment, and Death Economic and Human Losses Following Environmental Disaster

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Transcript of Destruction, Disinvestment, and Death Economic and Human Losses Following Environmental Disaster