Transcript In endogenous business cycle

Non-equilibrium dynamic models and impacts
Patrice Dumas (CIRED,CIRAD)
Stéphane Hallegatte (World Bank)
Michael Ghil (ENS, Paris, & UCLA)
Workshop on Coupled Climate-Economics Modelling and Data Analysis
22 - 23 November 2012
ENS, Paris
Outline
Endogenous business cycles
Dynamics of reconstruction and extreme events
economic consequences
Endogenous business cycles
A tale of two theories: the "real" cycle and
the endogenous cycle theories
In the real cycle theory, fluctuations from exogenous "real" (i.e. not
monetary) shocks (changes in productivity or in energy prices,
fiscal shock).
Aside from exogenous shocks, the economic system is stable:
–
all markets are at equilibrium, no involuntary unemployment
–
Deviation from equilibrium are damped more or less rapidly
–
Acting on the economy (recovery policy), is not usefull
In endogenous business cycle (EBC) models, cyclical behavior
originates from endogenous instabilities in the economic system
Several instabilities have been proposed:
· profitability-investment instability
· delays in investment
· income distribution
Acting on the economy can have positive effects, stabilizing it or shifting its mean state.
NEDyM (Non-equilibrium Dynamic Model)
Represents an economy with one producer, one consumer, one
goods that is used both to consume and invest.
Based on the Solow (1956) model, in which all equilibrium
constraints are replaced by dynamic relationships that involve
adjustment delays.
The NEDyM equilibrium is neo-classical and identical to that in the
original Solow model. If the parameters are changing slowly,
NEDyM has the same trajectories as the Solow model.
Because of market adjustment delays, NEDyM model dynamics
exhibits Keynesian features, with transient trajectory segments,
in response to shocks.
NEDyM possesses endogenous business cycles!
Hallegatte, Ghil, Dumas & Hourcade (J. Econ. Behavior & Org., 2008)
Hopf bifurcation:
from stable equilibrium, to limit cycle,
to chaotic behavior
inv = 1.7 (purely periodic)
inv = 2.5 transition to chaos
(irregular behavior)
inv = 10 irregular orbit
(kinky torus)
inv = 20 very asymmetric
business cycle (relaxation
oscillation)
Endogenous dynamics: an alternative explanation for
business cycles
Interpretation of the instability
Business cycles originate from the profit-investment relationship
(oscillations with a 5.5-year period)
higher profits > more investment > larger demand > higher profits
Business cycles are limited in amplitude by three processes:
Increase in labor costs when employment is high
Constraints in production and the consequent inflation in goods
prices when demand increases too rapidly
Financial constraints on investment
Main flaw in the model business cycle:
the amplitude of price oscillations
Correction: accounting for quantities in behavioral
equations
Calibration of NEDyM
Need to calibrate the endogenous business cycle
Kalman filter shows good result on « identical
twins » experiments
But much harder on real data
=> start from a representative cycle?
Economic consequences of extreme events
Direct vs indirect losses
Direct losses:

Casualties and injuries

Direct economic losses (i.e., value of what has been destroyed or damaged)
Indirect losses:
– (1) Business interruption, supply-chain disruption, and propagations
– (2) Lost production during reconstruction
– (3) Demand surge and increased reconstruction costs
– (4) A macro-economic feedback?
– (5) Long-term consequences on economic growth
Other costs:

Emergency costs (Katrina: $8 billion)

Political destabilization

Psychological trauma and social network disruption

Long-term impact on specific sectors (e.g., tourism)
Data on the Northridge earthquake
From Tierney (1997)
Survey by the Disaster Research
Center, at the University of Delaware.
Conducted with a representative,
randomly-selected sample of
businesses (1100) in the cities of Los
Angeles and Santa Monica, two
jurisdictions that were particularly
hard-hit by the earthquake
Businesses with no damages had
to close because of propagations,
making the total cost larger.
Demand surge and reconstruction costs
from the 2004 and 2005 hurricane seasons
Definition: “a temporary increase in repair costs above the standard level of costs due to the
lack of qualified workers or materials”
Data from XActimate and Risk Management Solutions
A macroeconomic feedback ?
Supported by (rough) data on demand surge
The demand surge remained limited after Andrew (20%).
This might be explained by the pre-existing economic situation:
Annual growth rate in the construction sector in Florida
20
Andrew's landfall
10
5
Four landfalls in 2004
0
-5
-10
Time (yr)
05
20
04
20
03
20
02
20
01
20
00
20
99
19
98
19
97
19
96
19
95
19
94
19
93
19
92
19
91
19
90
-15
19
Annual growth rate (%)
15
How to model extreme events
consequences?
Natural disasters are a series of shocks that destroy capital, leading to
a disequilibrium
Preexisting disequilibrium may be important
Using a classical production function is problematic as there are no
decreasing returns on capital hit by a disaster
Accounting for financial and technical constraints on the reconstruction
pace is necessary to reproduce the observed response to past
disasters over the short term
Extreme event economic
consequences modelling
From Hallegatte et al. (2007, Ecological Economics)
The total cost of an event depends on the reconstruction capacity of the economy.
Interaction between intrinsic
dynamics and shocks (1)
From Hallegatte and Ghil (2007)
Interaction between intrinsic
dynamics and shocks (2)
Limited losses if the disaster affects
an economy in recession
From Hallegatte and Ghil (2007)
Interaction between intrinsic
dynamics and shocks (3)
Large losses if the disaster
affects an economy in
expansion
A vulnerability paradox: growing economies are more vulnerable !
From Hallegatte and Ghil (2007)
Consequences of extreme events
Here also, the average loss depends on the event characteristics
and on reconstruction capacity
Natural disasters and bifurcation in losses
Index of disaster
Index of reconstruction
frequency and
capacity
intensity
The impact of a series of events is not the sum of the event impacts.
Positive feedbacks and poverty traps
Limited
reconstruction
capacity
« Positive » feedback
Long
reconstruction
period after
each disaster
Limited economic
development
Possibility of
poverty trap
Impossibility to
increase the
infrastructure and
capital stocks
Large cost of
natural
disasters
Disasters can be a significant obstacle to economic growth and development
Reconstruction and endogenous technical
change
Accounting for endogenous technical change changes the consequences of
disasters:
If reconstruction uses the best available technologies (EN), endogenous
technical change reduces the negative consequences of disasters.
But if reconstruction only
replace the capital, using
identical technologies (IM),
then endogenous technical
change amplifies the
negative consequences of
disasters
Technical change and a series of
disasters
If reconstruction uses the best available technologies (EN), the mean GDP losses
due to a series of disasters are cancelled out.
Otherwise (identical reconstruction, IM), mean GDP losses are increased.
Indirect costs in an input output approach
Production changes in 15 sectors in Louisiana, in the Katrina’s aftermath,
sing the ARIO (Adaptive Regional Input-Output) Model.
Boom in the construction
sector
An input-output model of the
local economy, with 15
sectors that interact with
each other.
We investigate the interplay
of:
Decrease in production
capacity
(1) the decrease in
production capacity due
to damages;
(1) the increase in demand
due to reconstruction
needs
Direct losses: $107b
Total losses: $135b
Nonlinear relationship between direct
and indirect costs
The boom in the
reconstruction sector
is limited by
economic and
technical constraints,
and by damages,
leading to a
nonlinearity.
In Louisiana, this
nonlinearity
appears when direct
losses exceed $50
billion.
Sectoral results
Underestimation in sectors
with small businesses
Employment losses per sector, data and model
Conclusion
Non equilibrium economic model allows to
represent some stylised facts on business
cycles, but there is a need for calibration
Constraints on reconstruction leads to realistic
dynamics
Smaller scales allow to understand the constraints