About modelling
supply versus demand shocks:
A disaster in disaster studies ?
Jan Oosterhaven
University of Groningen
Input-Output Workshop, Osnabrück, March 2017
Basics: demand shock
P
Basics: supply shock
Demand shock
P
Demand
Supply
Supply
shock
Q
• P in same direction
• P mitigates Q-shock
Q
• P in opposite direction
• P mitigates Q-shock
• CGE models have finite price elasticities. What about IO ?
Leontief IO-models
Ghoshian IO-models
Supply
Demand
P-model
P-model
Demand
Supply
Q-model
Q-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
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 (-/+)
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
• 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
Our NLP modeling approach
Actors try to maintain supplier and client relations


Min.  ij  zijrs ln zijrs zijrs , base  1   all other MRIOT cells
rs
(1)
Fixed technical coefficients (Walras-Leontief prod.f.)

r
zijrs  aijs x sj and v sj  c sj x sj
(2)
Fixed preference coefficients (W-L utility function)

r
yirs  pi s y s
Source: Oosterhaven & Bouwmeester (J. Reg. Sc. 2015)
(3)
Demand equals supply (per regional industry)
rs
rs
r
r
z

y

e

x
 j ij  i i i
s
s
(4)
Min. regional consumption ( = disaster-specific)
y s   s ( r ) y s , base
(5)
Max. regional value added ( = region-specific)
v s   s v s , base
(6)
First test: does (1)-(6) reproduce the base interregional IO table ? Yes, for our hypothetical IRIOT
Pre-disaster Interregional IO Table
Local intermediate consumption
Local final cons.
For.
R1, I1
R1, I2
R2, I1
R2, I2
Reg. 1
R1, Industry 1
15
10
5
6
22
15
27
100
R1, Industry 2
11
29
9
4
68
14
15
150
R2, Industry 1
10
8
35
29
22
49
47
200
R2, Industry 2
6
7
38
43
15
102
39
250
For. imports I1
8
13
16
20
15
21
93
For. imports I2
3
3
6
10
4
7
33
Value added
47
80
91
138
Total
100
150
200
250
Other totals:
Foreign imports
Reg. 2 exports
Total
356
146
126
208
128
National consumption
354
Source: Oosterhaven & Bouwmeester (J. Reg. Sc. 2015)
Next, more important tests:
 x 0
rs
rs
z

y
2 full interregional trade stops: (1)-(6) +  ij ij
i 0
 2 full regional production stops: (1)-(6) +

r
i
i
Post-disaster IRIOT after full production stop in Region 2
Local intermediate consumption
Local final cons.
For.
R1, I1
R1, I2
R2, I1
R2, I2
Reg. 1
R1, Industry 1
19
13
0
0
21
21
21
95
R1, Industry 2
14
38
0
0
55
50
11
167
R2, Industry 1
0
0
0
0
0
0
0
0
R2, Industry 2
0
0
0
0
0
0
0
0
For. imports I1
13
22
0
0
19
37
90
For. imports I2
5
5
0
0
4
34
49
Value added
45
89
0
0
Total
95
167
0
0
Other totals:
Foreign imports
139
Reg. 2 exports
Total
134
99
141
32
National consumption
• 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)
241
Post-disaster IRIOT after full production stop in Region 2
Local intermediate consumption
Local final cons.
For.
R1, I1
R1, I2
R2, I1
R2, I2
Reg. 1
R1, Industry 1
19
13
0
0
21
21
21
95
R1, Industry 2
14
38
0
0
55
50
11
167
R2, Industry 1
0
0
0
0
0
0
0
0
R2, Industry 2
0
0
0
0
0
0
0
0
For. imports I1
13
22
0
0
19
37
90
For. imports I2
5
5
0
0
4
34
49
Value added
45
89
0
0
Total
95
167
0
0
Other totals:
Foreign imports
139
Reg. 2 exports
Total
134
99
141
32
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
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
Data: use-regionalized German MRSUT for 2007
..… Region r
Region 1
Product
Industry Product
Final
Demand
U11
Y11
Products
Industries
Final
Demand
U1r
Y1r
RoW Total
e1
V1
GDP
g1
g1
x1
Ur1
RoW
Total
Industries
Yr1
Industry
Region r …..
Region 1
Products
URoW,1 YRoW,1
W1
0
x1
y1
Urr
Yrr
er
gr
Vr
xr
gr
URoW,r YRoW,r
m●
Wr
0
w●
xr
yr
e●
Source: Többen (PhD, Groningen, 2017)
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
Sensitivity for fixed ratio assumptions
1. The MRSU model explicitly assumes fixed
regional industry market shares in regional
product supply, which is mostly implicitly
done in IRIO tables
2. The German use-regionalized MRSUT
allows assuming cell-specific intermediate
and final demand trade origin shares
3. Fixing both ratios together => Type I
IRIO & MRSU model assumptions
Impact of adding fixed ratios
National and regional 2013 Elbe and Danube flooding multipliers
All of
Germany
Bayern
Sachsen
Sachsen- Thüringen
Anhalt
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
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