1. About modelling supply versus demand shocks: A disaster in disaster studies ?
Jan Oosterhaven
University of Groningen
Input-Output Workshop, Osnabrück, March 2017

2. Basics: demand shock Basics: supply shock P in same directionQ
Demand
Supply shock P Q
P in same direction
P mitigates Q-shock
P in opposite direction
P mitigates Q-shock
CGE models have finite price elasticities. What about IO ?

3. Leontief IO-models Ghoshian IO-models
Supply
Demand
Q-model
P-model
Demand
Supply
Q-model
P-model
Source: Oosterhaven (Southern Econ. J. 1996)
Demand-driven Q-model & Cost-push P-model
Infinite supply and zero demand (price) elasticity
=> No mitigating price-effect
Supply-driven Q-model & Demand-pull P-model
Infinite demand and zero supply (price) elasticity
=> No mitigating price-effect

4. Impacts of disasters and boycotts
1st regional supply shock: direct effect =
Loss of capital and labour (- production capacity)
Loss of infrastructure or boycott (- trade capac.)
=> forward: spatial and technical substitution (+), if not replaceable: large forward impacts (-/-)
2nd regional demand shock: direct effect =
Loss of intermediate and final demand (-)
=> backward: further demand reduction (-)
Terrorist attack = (spatial) demand shift (-/+)

5. Existing modelling approaches
Ideal: interregional, interindustry CGE model
Problem: complex, data hungry => little used
Multi-regional IO model: simple => often used
Problem: Only OK for short run impacts of final demand shocks/shifts, such as terrorist attacks
Intermediate demand shocks => double counting
Consumption demand shocks: Type II, same problem
Supply shocks: not possible
Hypothetical extraction of MRIO cross ?
Implicit: combines fixed technical and interregional trade coefficients with full foreign import substitution
However, extracted row = only direct backward impacts, not forward impacts on purchasing industries

6. Add the supply-driven model for forward impacts?
Contradictory to demand-driven MRIO model with its perfectly substitutable single homogenous output
Implausible: single homogeneous input => perfect input substitutability: cars without gas, factories without labor !
No: what is needed (Oosterhaven, JRS, 1988):
Allocation coefficients from the Ghosh model
Reciprocal technical coefficients for irreplaceable inputs
Partial import & export substitution for replaceable inputs
Our solution: combine some of above elements with
Minimum info gain with endogenous MRIOT row and column totals instead of fixed totals (as in RAS), as that simulates Back-to-Business-as-Usual, with max. flexibility

7. Our NLP modeling approach
Actors try to maintain supplier and client relations
Fixed technical coefficients (Walras-Leontief prod.f.)
Fixed preference coefficients (W-L utility function)
Source: Oosterhaven & Bouwmeester (J. Reg. Sc. 2015)

8. Demand equals supply (per regional industry)
Min. regional consumption ( = disaster-specific)
Max. regional value added ( = region-specific)
First test: does (1)-(6) reproduce the base inter-regional IO table ? Yes, for our hypothetical IRIOT

9. Pre-disaster Interregional IO Table Local intermediate consumption
Local intermediate consumption
Local final cons.
For. exports
Total
R1, I1
R1, I2
R2, I1
R2, I2
Reg. 1
Reg. 2
R1, Industry 1 15 10 5 6 22 27
100
R1, Industry 2 11 29 9 4 68 14
150
R2, Industry 1 8 35 49 47
200
R2, Industry 2 7 38 43
102 39
250
For. imports I1 13 16 20 21 93
For. imports I2 3 33
Value added 80 91
138
356
146
208
128
Other totals:
Foreign imports
126
National consumption
354
Source: Oosterhaven & Bouwmeester (J. Reg. Sc. 2015)
Next, more important tests:
2 full regional production stops: (1)-(6) +
2 full interregional trade stops: (1)-(6) +

10. Local intermediate consumption
Post-disaster IRIOT after full production stop in Region 2
Local intermediate consumption
Local final cons.
For. exports
Total
R1, I1
R1, I2
R2, I1
R2, I2
Reg. 1
Reg. 2
R1, Industry 1 19 13 21 95
R1, Industry 2 14 38 55 50 11
167
R2, Industry 1
R2, Industry 2
For. imports I1 22 37 90
For. imports I2 5 4 34 49
Value added 45 89
134 99
141 32
Other totals:
Foreign imports
139
National consumption
241
Yellow cross & +/+ foreign intermediate imports  Hyp. Extract.
Extra +/+ domestic intermediate imports in R1 (-/- with HE)
Extra +/+ foreign and domestic final imports in R2 (0 with HE)

11. Local intermediate consumption
Post-disaster IRIOT after full production stop in Region 2
Local intermediate consumption
Local final cons.
For. exports
Total
R1, I1
R1, I2
R2, I1
R2, I2
Reg. 1
Reg. 2
R1, Industry 1 19 13 21 95
R1, Industry 2 14 38 55 50 11
167
R2, Industry 1
R2, Industry 2
For. imports I1 22 37 90
For. imports I2 5 4 34 49
Value added 45 89
134 99
141 32
Other totals:
Foreign imports
139
National consumption
241
Opposing supply, demand and substitution effects of R2 => R1
=> Decrease in output of Industry 1 in R1, larger direct effects -/-
=> Increase in output of Industry 2 in R1, larger subst. effects +/+
Plus intra-reg. final sales and foreign exports of R1 -/-, to help R2

12. Conclusion of hypothetical IRIOT
Most important assumption not yet mentioned:
Prices react such that supply = demand
=> All changes are measured in base-year prices
Findings: a plausible combination of:
Demand & supply & spatial substitution effects => partial import & partial export substitution
CGE type results without explicit prices/markets
Next: real life app. for 2013 German floods

13. V1 Vr Data: use-regionalized German MRSUT for 2007 U11 Y11 U1r Y1r e1
Region 1
..… Region r
RoW
Total
Products
Industries
Final Demand
Product
U11
Y11
U1r
Y1r e1 g1
Industry V1 x1
Region r …..
Ur1
Yr1
Urr
Yrr er gr Vr xr
URoW,1
YRoW,1
URoW,r
YRoW,r m●
GDP W1 Wr w● y1 yr e●
Source: Többen (PhD, Groningen, 2017)

14. Elbe & Danube floods of 2013
Basically the same NLP model, but German MRSUT instead of an IRIOT
Extra: regional product supply = p. demand
Less: min. local final demand = government aid scenario => negative indirect impacts (especially on services) become negligable, -/- trade balance
Less: max. local value added = top business cycle scenario => positive substitution effects much smaller => net negative impacts become larger

15. Sensitivity for fixed ratio assumptions
The MRSU model explicitly assumes fixed regional industry market shares in regional product supply, which is mostly implicitly done in IRIO tables
The German use-regionalized MRSUT allows assuming cell-specific intermediate and final demand trade origin shares
Fixing both ratios together => Type I IRIO & MRSU model assumptions

16. Impact of adding fixed ratios
National and regional 2013 Elbe and Danube flooding multipliers
All of Germany
Bayern
Sachsen
Sachsen-Anhalt
Thüringen
Base NLP model
1.110
1.139
1.041
1.000
1.046
+ fixed market shares
1.193
1.306
1.083
1.010
1.057
+ fixed trade origins
1.334
1.207
1.176
1.070
1.125
+ both fixed shares
1.966
1.588
1.832
1.586
1.420
Source: Oosterhaven & Többen (Spatial Econ. An. 2017)
Base NLP model: all multipliers small = high resilience
Fixed trade origin shares > fixed industry market shares
Very large over-estimation of indirect effects with
Type I IRIO and MRSU model assumptions

17. Conclusion and evaluation
Outcomes 2013: high resilience of German economy
IRIO & MRSU models grossly over-estimate indirect disaster impacts
Outcomes NLP  spatial substitution CGE,
but without explicit prices/markets
NLP approach = simple & plausible