economic consequence analysis, prof. adam rose, usc

Ex Ante Estimation of Economic

Consequences of Disasters

Adam Rose

Center for Risk and Economic Analysis of Terrorism Events

CREATE

October 27, 2016

2

Questions

• In what context are ex-ante econ loss evaluations useful?

>>Where events are infrequent and data are sparse

• How should gov’ts undertake them and in what form?

>>Best when standardized across disasters

>>Range of models, depending on accuracy needed

• How can ex-ante evaluations complement the gathering of

ex-post economic loss data?

>>Provide framework for analysis; fill in gaps

• Is the collection of ex-post data still necessary with the

increasing precision of ex ante economic modelling?

>>Ex-post can serve as validation checks

3

Overview

• Broad ECA Framework Imperative for Accuracy

- resilience

- extreme behavioral responses

- complex supply-chain linkages

- remediation

- mitigation and spillovers

• Overview

- CREATE ECA Framework

- example applications: resilience, behavioral, supply-chain

- implications for decision-making

- E-CAT Tool

4

Economic Consequences of

Bioterrorism Attacks (gross output impacts in billions of 2007 dollars)

Attack Target

Loss of Life

Ordinary

Business

Interruption

Behavioral

Linkage

Resilience

Total Gross

Output

Stadium −57.2 −0.5 −16.2 8.5 −65.4

Urban Center -2.2 −8.2 4.4 −6.0

Airport −1.0 −0.1 −220.0 119.5 −101.5

Foot & Mouth −5.4 −40.8 4.2 −42.0

Water Service −27.3 −2.6 26.7 −3.2

5

CREATE Economic Consequence

Analysis Framework

6

Economic Resilience

• Static:

- General Definition: Ability of a system to maintain

function when shocked.

- Econ Definition: Efficient use of remaining resources

at a given point in time to produce as much as possible.

• Dynamic

- General: Ability & speed of a system to recover.

- Economic: Efficient use of resources over time for

investment in repair and reconstruction, including

expediting the process & adapting to change.

o Metric: averted losses as % of potential losses

7

Background on Economic Resilience

• Two major perspectives:

1. Include everything done to reduce losses, pre- and

post-disaster (focus is mitigation of property damage)

2. Limit to actions implemented after the disaster hits

(acknowledging that resilience is a process; things can

be done to advance to build resilience capacity)

- e.g., emergency drills, back-up generators, alternative suppliers

- however, these are not implemented until after the disaster hits

• The latter perspective may strike some as odd:

How can you reduce property damage post-disaster?

8

New Hazard Loss Estimation Paradigm

• Focus shifts to the flow of goods and services

emanating from the property, or capital stock

(typically measured in terms of GDP or employment,

often referred to as Business Interruption, or BI)

• BI just begins at the point of the disaster &

continues until an entity has recovered

• Thus BI is more complicated in many ways than

addressing property damage, because it includes:

- behavioral considerations

- policy considerations

9

Measuring Econ Resilience of 9/11

• 95% of over 1,100 WTC area firms relocated after 9/11

• If all of firms in the WTC area went out of business, direct

business interruption (BI) loss would = $58.4B

• If all relocation were immediate, then BI = $0

• Businesses relocated 2 to 4 months, BI = $16.1B

• Resilience Metric: Avoided Loss ÷Max Potential Loss

$42.3B ÷ $58.4B = 72%

10

Supply-Chain Example: Port Valuation

• Recent CREATE studies

- 90-day closure of Port Arthur/Port Beaumont [USCG]

- 2-day tsunami closure of Ports of LA/LB [USGS]

• Standard approach for estimating economic

impact of a port: direct econ activity X multiplier

- Direct economic activity = Port revenue

- PA/PB: $220 million X 5.9 = $1.3 billion

• But the standard approach misses the value of the

cargo & its contribution to rest of the economy

11

Estimating Total Economic Impacts of a Port Disruption, Cargo Damages & Terminal Downtime

12

CGE Modeling Overview

• Computable General Equilibrium Analysis:

Model of the entire economy based on decisions by

individual producers & consumers in response to price

signals within limits of available K, L, I, M, N.

(The economy as a set of intercnnected supply chains)

• USCGE Model:

- 58 Economic Sectors

- 3 Factor Payment types plus Taxes

- 9 Household Income Brackets

- Other Institutions and Trade

13

Resilience to Port Disruptions (90-day Port Arthur Disruption; Avoided BI Losses)

Strategic Petroleum Reserve 2.4%

Ordinary Inventories of All Goods 17.0

Conservation by Customers 3.0

Import Ship Rerouting 23.1

Export Diversion (Replace Imports) 7.0

Production Rescheduling 25.4

Total Resilience (not additive) 67.0%

14

Results for Port Arthur/Beaumont (90-day Disruption; Business Interruption Loss)

• Comparison of estimates:

- Standard estimate: $1.3 billion

- SOA approach (w/o resilience): $14.8 billion

- SOA approach (w/ resilience): $4.8 billion

• Supply chain effects increase impacts 10-fold

• Resilience lowers impacts 67% (relatively low-cost)

15

Behavioral Linkages

• Off-site responses associated with behavioral

changes (business, household, investor, worker)

• Emanates from social amplification of risk &

stigma effects (media coverage, rumor)

• Fear feeds on itself and spreads

• Translates into direct and indirect BI losses

• Can be 2 to 3 orders of magnitude higher

16

Behavioral Linkage Examples

• 9/11 led to a 2-year reduction in air travel

• Workers fear of riding the subway/bus

• Business fear of staying open after dark

• Investor fear of taking high risk

• General avoidance behavior

• Gov’t premature shutdown or evacuation

17

RDD Attack Example

Direct Economic Losses

• Casualties: $26.2 million for labor input

(modeled as decrease in L productivity)

• Capital Damage: relatively minor

(capital simply off limits for 30 days)

• Business interruption: $1.4 billion of lost output

(recalibration of technical efficiency parameter)

Direct Behavioral Effects

(Burns and Slovic Experiment)

• Consumer/tourist risk perceptions

- 15 to 23% price discount (subset of goods)

- mid-range of stigma-related WTP literature

• Employee risk perceptions

- >25% risk premium in affected area

- much higher than WTP literature

• Investor risk perceptions (non-survey)

- 20% rate of return premium

- mid-range of property value studies, factoring in Tobin’s Q

19

Behavioral Results & Conclusions

• Psychological impacts of disasters are measurable

(fear & uncertainty from SAR and Stigma)

• Can translate them into changes in econ behavior

• For terrorist attacks may be especially high in L-R

- 19.4 times as high as ordinary direct effects

- 14.9 times as high as total ordinary effects

• Policy Implications

- gov’t needs to provide assurances of safety

- media has a responsibility in reporting

20

E-CAT Objectives

• Develop a standardized capability to estimate

economic consequences of 30+ types of threats

- includes a comprehensive set of impact categories

- standardization facilitates comparisons

• Transition the research into a user-friendly, fast

software tool for high-level decision-makers

- risk mgt: resource allocation across multiple threats

- rapid response: estimates for remediation/aid/recovery

21

E-CAT Analytical Stages

Step 1:

Enumeration Tables

Step 2:

Direct Impact

Step 3:

User Interface Variables

Step 4:

CGE Analysis

Step 5:

Reduced Form

Analysis

Step 6:

Uncertainty Analysis

Step 7:

E-CAT Software

1. Enumeration Tables – Qualitative Direct Impact estimates identified

from historical data, literature, or expert judgment

2. Lower- and upper-bound Direct Impact values estimated for each

category above the “Low Influence” threshold

3. User Interface Variables identified: (Magnitude, Time of Day, Duration, Economic Structure, Location, Clean Up,

Behavioral Avoidance & Aversion, Resilience Relocation, Substitution, Recapture)

4. Randomized draws of 100 variable combinations converted to CGE

inputs and run to estimate GDP and employment impacts

5. Reduced-form equation estimated with OLS & quantile regression

6. Uncertainty distributions generated with reduced-form results

7. Reduced-form equations and uncertainty analysis combined into the

user-friendly interface of the E-CAT tool

22

E-CAT Tool User Interface

• Threat selection page

Economic Consequence Analysis Tool (E-CAT)User Interface Version 2.0

Technological Accidents /

Infrastructure FailuresNatural Threats

Terrorism /

Intentional Acts

Uncertainty Display

Options

Go!

National Center for

Risk and Economic Analysis of Terrorism Events

23

E-CAT Tool Results

• Earthquake – Default Options (Step 1)

Economic Impacts: Mean

(all in $2012) 5 % Qu a n tile

Richter 2 5 % Qu a n tile

5 0 % Qu a n tile

7 5 % Qu a n tile

Distribution Charts: 9 5 % Qu a n tile

No Resilience

Resilience - Recapture Resilience - Relocation

DefinitionNo Resilience

Definition

Behavioral - Avoidance Behavioral - Aversion

Economic Structure Decontamination

National_AvgDefintion

Select value between 5-7.8 Richter

Duration Location

7.8Defintion

Day timeDefintion

Input Area: Input values in yellow boxes

Economic Consequence Analysis Tool

(grey boxes are non-applicable)

Magnitude Time of Day

Threat: Earthquake Option 1: Input Single Parameter Estimate

Results Area GDP Loss Employment Loss

billion dollars percent thousand jobs percent

119.46 0.78 18.97 0.01

116.23 4.96

116.68

117.57

125.24

144.08

0.76

0.77

0.77

0.82

0.94

9.47

18.88

41.66

165.34

0.00

0.01

0.01

0.03

0.13

0.0

0.2

0.4

0.6

0.8

1.0

0 20 40 60 80 100 120 140 160

Pr(

Y<

= y

)

GDP Loss, Y

Cumulative Distribution of GDP Loss (Value)

0.0

0.2

0.4

0.6

0.8

1.0

0.00 0.20 0.40 0.60 0.80 1.00

Pr(

Y<

= y

)

GDP Loss%, Y

Cumulative Distribution of GDP Loss (Percent)

0.0

0.2

0.4

0.6

0.8

1.0

0 50 100 150 200

Pr(

Y<

= y

)

Employment Loss, Y

Cumulative Distribution of Employment Loss (Value)

0.0

0.2

0.4

0.6

0.8

1.0

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14

Pr(

Y<

= y

)

Employment Loss%, Y

Cumulative Distribution of Employment Loss (Percent)

Main Menu Print ResultsReset Default

24

E-CAT Tool Results

• Earthquake – 6.0 Richter Magnitude (Step 2)




5 0 % Qu a n tile

7 5 % Qu a n tile


No Resilience


DefinitionNo Resilience

Definition



National_AvgDefintion


Duration Location

6.0Defintion

Day timeDefintion








6.41 0.04 2.48 0.00

6.00 0.62

6.01

6.09

6.49

7.29

0.04

0.04

0.04

0.04

0.05

1.06

1.96

3.86

10.72

0.00

0.00

0.00

0.00

0.01

0.0

0.2

0.4

0.6

0.8

1.0

0 1 2 3 4 5 6 7 8

Pr(

Y<

= y

)

GDP Loss, Y


0.0

0.2

0.4

0.6

0.8

1.0

0.00 0.01 0.02 0.03 0.04 0.05 0.06

Pr(

Y<

= y

)

GDP Loss%, Y


0.0

0.2

0.4

0.6

0.8

1.0

0 2 4 6 8 10 12

Pr(

Y<

= y

)

Employment Loss, Y


0.0

0.2

0.4

0.6

0.8

1.0

0.00 0.00 0.00 0.01 0.01 0.01

Pr(

Y<

= y

)

Employment Loss%, Y



25

E-CAT Tool Results

• Earthquake – Relocation (high)




5 0 % Qu a n tile

7 5 % Qu a n tile


Lower_B


DefinitionUpper_B

Definition



ManufacturingDefintion


Duration Location

6.0Defintion

Day timeDefintion








2.19 0.01 4.75 0.00

2.10 2.98

2.11

2.13

2.23

2.30

0.01

0.01

0.01

0.01

0.01

3.36

3.74

5.32

9.68

0.00

0.00

0.00

0.00

0.01

0.0

0.2

0.4

0.6

0.8

1.0

2 2 2 2 2 2 2

Pr(

Y<

= y

)

GDP Loss, Y


0.0

0.2

0.4

0.6

0.8

1.0

0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01

Pr(

Y<

= y

)

GDP Loss%, Y


0.0

0.2

0.4

0.6

0.8

1.0

0 2 4 6 8 10 12

Pr(

Y<

= y

)

Employment Loss, Y


0.0

0.2

0.4

0.6

0.8

1.0

0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.01

Pr(

Y<

= y

)

Employment Loss%, Y



26

Summary

• In what context are ex-ante econ loss evaluations useful?

>>Where events are infrequent and data are sparse

• How should gov’ts undertake them and in what form?

>>Best when standardized across disasters

>>Range of models, depending on accuracy needed

• How can ex-ante evaluations complement the gathering of

ex-post economic loss data?

>>Provide framework for analysis; fill in gaps

• Is the collection of ex-post data still necessary with the

increasing precision of ex ante economic modelling?

>>Ex-post can serve as validation checks

27

Selected ECA Case Studies

Study Topic Threat Direct Impact

Location Impact Scope Net Impact

Rose et al. (2016) tsunami California tsunami

California Coast

California & U.S.

-$0.63B (US)

(GDP)

Wing et al. (2015) severe winter

storm ARkStorm California California -$236.4B

(GDP)

Rose and Wei

(2013) seaport disruption port shutdown/

tanker accident

Port Beaumont &

Port Arthur, Texas Port Metro Area

& US -$2.1B (Port) -$4.2B (US)

(gross output)

Oladosu et al.

(2013) agroterrorism

foot and mouth

disease San Joaquin

Valley, CA United States -$0.09B

(GDP)

Geisecke et al.

(2012) radiological

dispersal device dirty bomb Los Angeles

Financial District Los Angeles County -$12.8B

(10-year)

(GDP)

Rose et al. (2011) earthquake Verdugo

Earthquake Los Angeles

City Los Angeles

County -$0.35B

(gross output)

Rose et al. (2011) earthquake ShakeOut Earthquake

San Andreas Fault Southern California -$67B (gross output)

Rose et al. (2009) airliner as weapon 9/11 attacks on

WTC New York

City NY Metro Area and

US -$109B (US)

(GDP)

Rose et al. (2007) electricity

blackout attack on electric

system Los Angeles

City Los Angeles

County -$2.84B

(gross output)

Rose et al. (2007) water outage

attack on water system

Los Angeles

City Los Angeles County -$2.25B

(gross output)

28

Economic Consequence Publications

• Rose, A., M. Avetisyan, W. Burns, H. Rosoff, and P. Slovic. 2016. “The Role of Behavioral Responses in the Total

Economic Consequences of Terrorist Attacks on U.S. Air Travel Targets,” Risk Analysis, forthcoming.

• Rose, I. Sue Wing, D. Wei and A. Wein. 2016. “Economic Impacts of a California Tsunami,” Natural Hazards Review,

published on-line thus far.

• Prager F., D. Wei, and Rose. 2016. “Total Economic Consequences of an Influenza Outbreak in the United States,”

Risk Analysis, published on-line thus far.

• Rose, A. and C. Huyck. 2016. “Improving Catastrophe Modeling for Business Interruption Insurance Needs,” Risk

Analysis, published on-line thus far.

• Rose, A. 2015. “Macroeconomic Consequences of Terrorist Attacks: Estimation for the Analysis of Policies and

Rules," in C. Mansfield and V.K. Smith (eds.), Benefit Transfer for the Analysis of DHS Policies and Rules,

Cheltenham, UK: Edward Elgar.

• Rose, A. and E. Krausmann. 2013. “An Economic Framework for the Development of a Resilience Index for Business

Recovery,” International Journal of Disaster Risk Reduction 5(October): 73-83

• Rose, A. and D. Wei. 2013. “Estimating the Economic Consequences of a Port Shutdown: The Special Role of

Resilience,” Economic Systems Research 25(2): 212-32.

• Geisecke, J., A. Rose, P. Slovic et al. 2012. "Assessment of the Regional Economic Impacts of Catastrophic Events:

A CGE Analysis of Resource Loss and Behavioral Effects of a Radiological Dispersion Device Attack Scenario," Risk

Analysis 32: 583-600.

• Rose, A., S. Liao and A. Bonneau. 2011. “Regional Economic Impacts of a Verdugo Earthquake Disruption of Los

Angeles Water Supplies: A Computable General Equilibrium Analysis,” Earthquake Spectra 27(3): 881-906.

• Rose, A. 2009. "A Framework for Analyzing and Estimating the Total Economic Impacts of a Terrorist Attack and

Natural Disaster,” Journal of Homeland Security and Emergency Management 6: Article 4.

• Rose, A., G. Oladosu, B. Lee and G. Beeler Asay. 2009. "The Economic Impacts of the 2001 Terrorist Attacks on the

World Trade Center: A Computable General Equilibrium Analysis," Peace Economics, Peace Science, and Public

Policy 15: Article 6.

economic consequence analysis, prof. adam rose, usc

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