Presentation of a joint work with Gauthier Vermandel about our macroeconomic model that aims at investigating the short run effects of weather shocks on business cycles and the long run potential effects of climate change on macroeconomic volatility. These slides were presented in Orléans (France), at the Laboratoire d'Economie d'Orléans (LEO) during the international conference "Environmental Economics: A Focus on Natural Resources".
Weather Shocks, Climate Change and Business Cycles
1. Weather Shocks, Climate Change
and Business Cycles
Ewen Gallica
& Gauthier Vermandelb
a
CREM UMR CNRS 6211, Université de Rennes 1 & Actinfo Chair
b
Leda, Université-Paris Dauphine, PSL Universités & France Stratégie
Source: Squarzoni, P. (2012). Saison brune. Delcourt.
Environmental Economics: A Focus on Natural Resources
Orléans, 5-6 April 2018
2. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
Motivation
1980 2000
20
40
60
(a) Number of weather
disasters per year
1980 2000
0
10
20
current USD (billions)
(b) Value of damages
per year
1980 2000
0
200
400
600
current USD (millions)
(c) Average damage
per disaster & year
Source: EM-DAT; IMF, World Economic Outlook database.
Figure 1: Global frequency and impact of weather shocks (droughts and heat waves) between 1970
and 2016.
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 2/27
3. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
Weather Shocks Policy Challenges
• With climate change, weather shocks are a growing source of business
cycle fluctuations
• Variable climate conditions force policy-makers to design new policies
aimed at stabilizing the economy following a weather shock
• Weather shocks are harmful for agriculture-based economies
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 3/27
4. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
Weather Shocks Policy Challenges
• With climate change, weather shocks are a growing source of business
cycle fluctuations
• Variable climate conditions force policy-makers to design new policies
aimed at stabilizing the economy following a weather shock
• Weather shocks are harmful for agriculture-based economies
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 3/27
5. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
Weather Shocks Policy Challenges
• With climate change, weather shocks are a growing source of business
cycle fluctuations
• Variable climate conditions force policy-makers to design new policies
aimed at stabilizing the economy following a weather shock
• Weather shocks are harmful for agriculture-based economies
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 3/27
6. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
Literature Related to the Weather
So far, the literature focuses on:
• Long run effects of climate, using:
Integrated Assessment Models (IAMs) pioneered by Nordhaus (1991)
that measure the social costs of carbon emissions on a very long per-
spective (no fluctuations).
• Short run effects of the weather, using:
VAR models that highlight the important role of weather shocks
(Buckle et al., 2007; Kamber et al., 2013)
Panel models that show that higher temperatures have a detrimen-
tal effect on economic growth (Dell et al., 2012; Burke et al., 2015;
Acevedo et al., 2017)
Yet, there is no macro model which analyzes both short run and
long run effects of the weather into an unified setup
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 4/27
7. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
Literature Related to the Weather
So far, the literature focuses on:
• Long run effects of climate, using:
Integrated Assessment Models (IAMs) pioneered by Nordhaus (1991)
that measure the social costs of carbon emissions on a very long per-
spective (no fluctuations).
• Short run effects of the weather, using:
VAR models that highlight the important role of weather shocks
(Buckle et al., 2007; Kamber et al., 2013)
Panel models that show that higher temperatures have a detrimen-
tal effect on economic growth (Dell et al., 2012; Burke et al., 2015;
Acevedo et al., 2017)
Yet, there is no macro model which analyzes both short run and
long run effects of the weather into an unified setup
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 4/27
8. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
Literature Related to the Weather
So far, the literature focuses on:
• Long run effects of climate, using:
Integrated Assessment Models (IAMs) pioneered by Nordhaus (1991)
that measure the social costs of carbon emissions on a very long per-
spective (no fluctuations).
• Short run effects of the weather, using:
VAR models that highlight the important role of weather shocks
(Buckle et al., 2007; Kamber et al., 2013)
Panel models that show that higher temperatures have a detrimen-
tal effect on economic growth (Dell et al., 2012; Burke et al., 2015;
Acevedo et al., 2017)
Yet, there is no macro model which analyzes both short run and
long run effects of the weather into an unified setup
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 4/27
9. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
Literature Related to the Weather
So far, the literature focuses on:
• Long run effects of climate, using:
Integrated Assessment Models (IAMs) pioneered by Nordhaus (1991)
that measure the social costs of carbon emissions on a very long per-
spective (no fluctuations).
• Short run effects of the weather, using:
VAR models that highlight the important role of weather shocks
(Buckle et al., 2007; Kamber et al., 2013)
Panel models that show that higher temperatures have a detrimen-
tal effect on economic growth (Dell et al., 2012; Burke et al., 2015;
Acevedo et al., 2017)
Yet, there is no macro model which analyzes both short run and
long run effects of the weather into an unified setup
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 4/27
10. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
This Paper
• Assesses the transmission mechanism of a weather shock both at a
sectoral and international levels
• Measures short run implications of weather shocks for macroeco-
nomic fluctuations
• Measures long run implications of weather shocks for macroeco-
nomic volatility and welfare
• Uses a model estimated with full-information methods on New Zealand
data
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 5/27
11. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
This Paper
• Assesses the transmission mechanism of a weather shock both at a
sectoral and international levels
• Measures short run implications of weather shocks for macroeco-
nomic fluctuations
• Measures long run implications of weather shocks for macroeco-
nomic volatility and welfare
• Uses a model estimated with full-information methods on New Zealand
data
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 5/27
12. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
This Paper
• Assesses the transmission mechanism of a weather shock both at a
sectoral and international levels
• Measures short run implications of weather shocks for macroeco-
nomic fluctuations
• Measures long run implications of weather shocks for macroeco-
nomic volatility and welfare
• Uses a model estimated with full-information methods on New Zealand
data
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 5/27
13. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
This Paper
• Assesses the transmission mechanism of a weather shock both at a
sectoral and international levels
• Measures short run implications of weather shocks for macroeco-
nomic fluctuations
• Measures long run implications of weather shocks for macroeco-
nomic volatility and welfare
• Uses a model estimated with full-information methods on New Zealand
data
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 5/27
14. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
This paper (cont.)
To do so:
1. We build a drought indicator
2. We characterize a weather shock through the lens of a VAR model
(not presented today)
3. We design a theoretical model that incorporates the response of an
economy to a weather shock and estimate it
4. We analyze the propagation of a weather shock and its implications
in terms of business cycle statistics (short-run)
5. We measure the implications of climate change on aggregate fluc-
tuations by varying the variance of weather shocks (long-run)
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 6/27
15. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
This paper (cont.)
To do so:
1. We build a drought indicator
2. We characterize a weather shock through the lens of a VAR model
(not presented today)
3. We design a theoretical model that incorporates the response of an
economy to a weather shock and estimate it
4. We analyze the propagation of a weather shock and its implications
in terms of business cycle statistics (short-run)
5. We measure the implications of climate change on aggregate fluc-
tuations by varying the variance of weather shocks (long-run)
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 6/27
16. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
This paper (cont.)
To do so:
1. We build a drought indicator
2. We characterize a weather shock through the lens of a VAR model
(not presented today)
3. We design a theoretical model that incorporates the response of an
economy to a weather shock and estimate it
4. We analyze the propagation of a weather shock and its implications
in terms of business cycle statistics (short-run)
5. We measure the implications of climate change on aggregate fluc-
tuations by varying the variance of weather shocks (long-run)
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 6/27
17. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
This paper (cont.)
To do so:
1. We build a drought indicator
2. We characterize a weather shock through the lens of a VAR model
(not presented today)
3. We design a theoretical model that incorporates the response of an
economy to a weather shock and estimate it
4. We analyze the propagation of a weather shock and its implications
in terms of business cycle statistics (short-run)
5. We measure the implications of climate change on aggregate fluc-
tuations by varying the variance of weather shocks (long-run)
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 6/27
18. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
This paper (cont.)
To do so:
1. We build a drought indicator
2. We characterize a weather shock through the lens of a VAR model
(not presented today)
3. We design a theoretical model that incorporates the response of an
economy to a weather shock and estimate it
4. We analyze the propagation of a weather shock and its implications
in terms of business cycle statistics (short-run)
5. We measure the implications of climate change on aggregate fluc-
tuations by varying the variance of weather shocks (long-run)
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 6/27
19. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
This paper (cont.)
To do so:
1. We build a drought indicator
2. We characterize a weather shock through the lens of a VAR model
(not presented today)
3. We design a theoretical model that incorporates the response of an
economy to a weather shock and estimate it
4. We analyze the propagation of a weather shock and its implications
in terms of business cycle statistics (short-run)
5. We measure the implications of climate change on aggregate fluc-
tuations by varying the variance of weather shocks (long-run)
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 6/27
20. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
Why New Zealand?
• Country subject to weather shocks:
2013 – North Island + West of the South Island drought ($1.3 billion,
≈ .5% of GDP)
2008 – national drought ($2.8 billion, ≈ 1.5% of GDP)
Source: Dave Young, on Flickr.
• Availability of both weather and agriculture data
• Small enough country ⇒ fairly homogeneous weather
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 7/27
21. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
Take-away results
• Our model suggests that weather shocks play an important role
in explaining macroeconomic fluctuations
• Propagation of a weather shock:
A weather shock (drought event) acts as a standard negative sup-
ply shock that hits the agricultural sector and spreads to the entire
economy
• Welfare costs:
Today: welfare cost of weather shocks represents 0.40% of households
consumption
Tomorrow: this cost would reach up 0.59% of household consumption
because of climate change
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 8/27
22. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
Take-away results
• Our model suggests that weather shocks play an important role
in explaining macroeconomic fluctuations
• Propagation of a weather shock:
A weather shock (drought event) acts as a standard negative sup-
ply shock that hits the agricultural sector and spreads to the entire
economy
• Welfare costs:
Today: welfare cost of weather shocks represents 0.40% of households
consumption
Tomorrow: this cost would reach up 0.59% of household consumption
because of climate change
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 8/27
23. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
Take-away results
• Our model suggests that weather shocks play an important role
in explaining macroeconomic fluctuations
• Propagation of a weather shock:
A weather shock (drought event) acts as a standard negative sup-
ply shock that hits the agricultural sector and spreads to the entire
economy
• Welfare costs:
Today: welfare cost of weather shocks represents 0.40% of households
consumption
Tomorrow: this cost would reach up 0.59% of household consumption
because of climate change
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 8/27
24. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
Roadmap
1 Introduction
2 The Model
3 Estimation
4 Business Cycles Analysis
5 Long Run
6 Conclusion
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 9/27
25. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
The Model in Brief
• DSGE model in an RBC framework
Households
Foreign
Households
Non-agricultural
Sector
Agricultural
sector
Weather
(droughts)
cons. ct
cons. c∗
t
hours ht
land
costs xt
invest-
ment iA
t
bonds
b∗
t
Figure 2: The theoretical model.
• Small open economy (home vs world)
• Two sectors:
weather-dependent agricultural sector (original feature)
standard non-agricultural sector
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 10/27
26. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
The Model in Brief
• DSGE model in an RBC framework
Households
Foreign
Households
Non-agricultural
Sector
Agricultural
sector
Weather
(droughts)
cons. ct
cons. c∗
t
hours ht
land
costs xt
invest-
ment iA
t
bonds
b∗
t
Figure 2: The theoretical model.
• Small open economy (home vs world)
• Two sectors:
weather-dependent agricultural sector (original feature)
standard non-agricultural sector
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 10/27
27. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
The Model in Brief
• DSGE model in an RBC framework
Households
Foreign
Households
Non-agricultural
Sector
Agricultural
sector
Weather
(droughts)
cons. ct
cons. c∗
t
hours ht
land
costs xt
invest-
ment iA
t
bonds
b∗
t
Figure 2: The theoretical model.
• Small open economy (home vs world)
• Two sectors:
weather-dependent agricultural sector (original feature)
standard non-agricultural sector
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 10/27
28. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
Key mechanisms:
1. Farmers face exogenous
weather (no rain dance al-
lowed)
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 11/27
29. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
Key mechanisms:
1. Farmers face exogenous
weather (no rain dance al-
lowed)
2. They can offset bad
weather conditions by pur-
chasing goods in the non-
agricultural sector
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 11/27
30. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
Key mechanisms:
1. Farmers face exogenous
weather (no rain dance al-
lowed)
2. They can offset bad
weather conditions by pur-
chasing goods in the non-
agricultural sector
3. Leading to spillover ef-
fects between the two sec-
tors
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 11/27
31. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
The Weather
• We introduce a weather variable in the model: εW
t > 1
• It is a drought index based on soil moisture data
• It follows an univariate stochastic exogenous process:
log(εW
t ) = ρW log(εW
t−1) + σW ηW
t , ηW
t ∼ N (0, 1) (1)
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 12/27
32. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
The Weather
• We introduce a weather variable in the model: εW
t > 1
• It is a drought index based on soil moisture data
• It follows an univariate stochastic exogenous process:
log(εW
t ) = ρW log(εW
t−1) + σW ηW
t , ηW
t ∼ N (0, 1) (1)
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 12/27
33. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
The Weather
• We introduce a weather variable in the model: εW
t > 1
• It is a drought index based on soil moisture data
• It follows an univariate stochastic exogenous process:
log(εW
t ) = ρW log(εW
t−1) + σW ηW
t , ηW
t ∼ N (0, 1) (1)
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 12/27
34. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
Agricultural Sector and the Weather
• Each farmer i ∈ [n, 1] has a land endowment it, whose time-varying
productivity (or efficiency) writes:
it = (1 − δ ) Ω εW
t it−1 + xit, (2)
where:
δ ∈ (0, 1) is the rate of decay of land efficiency
xit are intermediate goods to maintain land efficiency (crops, water,
fertilizers...)
Ω εW
t is a weather damage function:
Ω εW
t = εW
t
−θ
, (3)
where θ is the elasticity of land productivity with respect to the weather
variations
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 13/27
35. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
Agricultural Sector and the Weather (cont.)
• For each farm i ∈ [n, 1], the production function is:
yA
it = εZ
t
ω
it−1 kA
it−1
α
κAhA
it
1−α 1−ω
, (4)
where
εZ
t is an economy-wide technology shock
it−1 is the land endowment
kA
it−1 is physical capital
hA
it is labor demand
ω ∈ [0, 1] is the elasticity of output to land
α ∈ [0, 1] is the share of physical capital
κA > 0 is a technology parameter
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 14/27
36. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
Data
The model is estimated using Bayesian techniques which combine likeli-
hood estimation with prior information via dynare.
• We estimate 6 sequences of shocks
• We estimate 21 structural parameters
• Parameters related to weather conditions are estimated agnostically
with diffuse prior.
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 15/27
37. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
Data
The model is estimated using Bayesian techniques which combine likeli-
hood estimation with prior information via dynare.
• We estimate 6 sequences of shocks
• We estimate 21 structural parameters
• Parameters related to weather conditions are estimated agnostically
with diffuse prior.
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 15/27
38. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
Data
The model is estimated using Bayesian techniques which combine likeli-
hood estimation with prior information via dynare.
• We estimate 6 sequences of shocks
• We estimate 21 structural parameters
• Parameters related to weather conditions are estimated agnostically
with diffuse prior.
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 15/27
39. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
Data
The model is estimated using Bayesian techniques which combine likeli-
hood estimation with prior information via dynare.
• We estimate 6 sequences of shocks
• We estimate 21 structural parameters
• Parameters related to weather conditions are estimated agnostically
with diffuse prior.
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 15/27
40. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
Data (cont.)
2000 2010
−2
0
2
Production
2000 2010
−10
0
10
Agriculture
2000 2010
−10
0
10
Investment
2000 2010
0
5
Hours
2000 2010
−2
0
2
Foreign Output
2000 2010
−2
−1
0
1
Weather
Figure 3: Observable variables used to estimate the DSGE model
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 16/27
42. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
Propagation of a Weather Shock
5 10 15 20
−0.3
−0.2
−0.1
0
total output Yt
5 10 15 20
−0.15
−0.1
−0.05
0
consumption Ct
5 10 15 20
−0.4
−0.2
0
investment It
5 10 15 20
−6
−4
−2
0
2
·10−2
hours Ht
5 10 15 20
−2
0
2
4
6
·10−2
non-agriculture Y N
t
5 10 15 20
−2
−1
0
agriculture Y A
t
5 10 15 20
−0.15
−0.1
−0.05
0
relative price pN
t
5 10 15 20
0
0.2
0.4
0.6
0.8
relative price pA
t
5 10 15 20
0
5
10
land cost xt
5 10 15 20
−10
−5
0
land productivity t
5 10 15 20
0
0.05
0.1
0.15
0.2
exchange rate rert
5 10 15 20
−0.1
−0.05
0
trade balance tbt
DSGE mean DSGE 90% VAR mean
Figure 5: System response to an estimated weather shock ηW
t measured in percentage deviations
from the steady state.
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 18/27
43. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
Contribution of Weather Shocks on Aggregate Fluctuations
Q1 Q2 Q4 Q10 Q40 Q∞
0%
20%
40%
60%
80%
100%
Production Yt
Supply shocks (ηZ
t ) Demand shocks (ηG
t + ηA
t + ηI
t )
Foreign shocks (η∗
t ) Weather shocks (ηW
t )
Q1 Q2 Q4 Q10 Q40 Q∞
0%
20%
40%
60%
80%
100%
Agricultural production Y A
t
Figure 6: Forecast error variance decomposition at the posterior mean for different time horizons
(one, two, four, ten, forty and unconditional).
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 19/27
44. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
Climate Change and Business Cycles
“ A change in the state of the climate that can be identified
(e.g., by using statistical tests) by changes in the mean
and/or the variability of its properties, and that persists for
an extended period, typically decades or longer IPCC (2014)
”• Climate is supposed to be stationary in our framework: our set-up is
irrelevant for analyzing changes in mean climate values
• However, it allows for changes in the variance of climate
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 20/27
45. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
Climate Change and Business Cycles
“ A change in the state of the climate that can be identified
(e.g., by using statistical tests) by changes in the mean
and/or the variability of its properties, and that persists for
an extended period, typically decades or longer IPCC (2014)
”• Climate is supposed to be stationary in our framework: our set-up is
irrelevant for analyzing changes in mean climate values
• However, it allows for changes in the variance of climate
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 20/27
46. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
Climate Change and Business Cycles
“ A change in the state of the climate that can be identified
(e.g., by using statistical tests) by changes in the mean
and/or the variability of its properties, and that persists for
an extended period, typically decades or longer IPCC (2014)
”• Climate is supposed to be stationary in our framework: our set-up is
irrelevant for analyzing changes in mean climate values
• However, it allows for changes in the variance of climate
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 20/27
47. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
Climate Change and Business Cycles (cont.)
Less
extreme cold
weather
Less
cold
weather
More
hot
weather
More
extreme hot
weather
Figure 7: Increase in mean of weather shocks
More
extreme cold
weather
More
cold
weather
More
hot
weather
More
extreme hot
weather
Figure 8: Increase in variance of weather shocks
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 21/27
48. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
Climate Change and Business Cycles (cont.)
• How much is the variance of the weather expected to vary?
• We consider four emission scenarios:
0
10
20
30
1900 1950 2000 2050 2100
(a)
50
55
60
65
2025 2050 2075 2100
(b)
95
100
105
110
115
120
2025 2050 2075 2100
(c)
RCP 2.5 RCP 4.5 RCP 6.0 RCP 8.5
Notes: (a) represents historical CO2 emissions as well as their projections up to 2100 under each
scenario. The estimation of the standard errors of projected precipitations σW
t for each representative
concentration pathway is represented in panel (b). Their linear trend from 2013 to 2100 is depicted
in panel (c).
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 22/27
49. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
Climate Change and Business Cycles (cont.)
• These scenarios are translated in our model through an increase in
the standard deviation of drought shocks:
RCP 2.6: −4.10%
RCP 4.5: +6.82%
RCP 6.0: +9.30%
RCP 8.5: +23.25%
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 23/27
50. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
1994-2016 2100 (projections)
Benchmark RCP 2.6 RCP 4.5 RCP 6.0 RCP 8.5
sd(ηW
t ) Weather shock 100 95.90 106.82 109.30 123.25
sd(Yt) GDP 100 99.82 100.15 100.23 100.72
sd(Y A
t ) Agriculture 100 96.89 102.54 103.86 111.53
sd(Ct) Consumption 100 99.94 100.05 100.07 100.22
sd(It) Investment 100 99.98 100.01 100.02 100.07
sd(Ht) Hours 100 99.99 100.00 100.01 100.03
sd(Rt) Real interest rate 100 100.00 100.00 100.00 100.00
sd(rert) Exchange rate 100 99.86 100.12 100.18 100.57
sd(pA
t ) Agricultural price 100 99.80 100.17 100.26 100.80
sd(cat) Current account 100 99.90 100.09 100.13 100.40
E(Wt) Welfare -158.02 -158.00 -158.04 -158.06 -158.13
λ (%) Welfare cost 0.4023 0.3562 0.4417 0.4623 0.5873
Table 1: Changes in Standard-Errors of Simulated Observables Under Climate Change Scenarios.
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 24/27
51. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
Recap
• Our model suggests that weather shocks play an important role
in explaining macroeconomic fluctuations
• Propagation of a weather shock:
A weather shock (drought event) acts as a standard negative sup-
ply shock that hits the agricultural sector and spreads to the entire
economy
• Welfare costs:
Today: welfare cost of weather shocks represents 0.40% of households
consumption
Tomorrow: this cost would reach up 0.59% of household consumption
because of climate change
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 25/27
52. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
Recap
• Our model suggests that weather shocks play an important role
in explaining macroeconomic fluctuations
• Propagation of a weather shock:
A weather shock (drought event) acts as a standard negative sup-
ply shock that hits the agricultural sector and spreads to the entire
economy
• Welfare costs:
Today: welfare cost of weather shocks represents 0.40% of households
consumption
Tomorrow: this cost would reach up 0.59% of household consumption
because of climate change
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 25/27
53. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
Recap
• Our model suggests that weather shocks play an important role
in explaining macroeconomic fluctuations
• Propagation of a weather shock:
A weather shock (drought event) acts as a standard negative sup-
ply shock that hits the agricultural sector and spreads to the entire
economy
• Welfare costs:
Today: welfare cost of weather shocks represents 0.40% of households
consumption
Tomorrow: this cost would reach up 0.59% of household consumption
because of climate change
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 25/27
54. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
Looking Forward
• Fat tailed normal distribution of weather shocks to capture higher
order effects.
• Analyzing food insecurity adapting this setup for developing
economies.
• Optimal design of policy aiming at stabilizing weather shocks
(pigouvian tax, climate insurance, etc.).
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 26/27
55. Introduction The Model Estimation Business Cycles Analysis Long Run Conclusion References
Bibliography I
Acevedo, S., Mrkaic, M., Novta, N., Poplawski-Ribeiro, M., Pugacheva, E., and Topalova, P. (2017).
The Effects of Weather Shocks on Economic Activity: How Can Low-Income Countries Cope?
World Economic Outlook, Chapter 3. International Monetary Fund.
Buckle, R. A., Kim, K., Kirkham, H., McLellan, N., and Sharma, J. (2007). A structural VAR
business cycle model for a volatile small open economy. Economic Modelling, 24(6):990–1017.
doi:10.1016/j.econmod.2007.04.003.
Burke, M., Hsiang, S. M., and Miguel, E. (2015). Global non-linear effect of temperature on
economic production. Nature, 527(7577):235–239. doi:0.1038/nature15725.
Dell, M., Jones, B. F., and Olken, B. A. (2012). Temperature shocks and economic growth:
Evidence from the last half century. American Economic Journal: Macroeconomics, 4(3):66–95.
doi:10.1257/mac.4.3.66.
Kamber, G., McDonald, C., Price, G., et al. (2013). Drying out: Investigating the economic effects of
drought in New Zealand. Reserve Bank of New Zealand Analytical Note Series No. AN2013/02,
Reserve Bank of New Zealand, Wellington, New Zealand.
Narasimhan, B. and Srinivasan, R. (2005). Development and evaluation of soil moisture deficit
index (SMDI) and evapotranspiration deficit index (ETDI) for agricultural drought monitoring.
Agricultural and Forest Meteorology, 133(1):69–88. doi:10.1016/j.agrformet.2005.07.012.
Nordhaus, W. D. (1991). To slow or not to slow: the economics of the greenhouse effect. The
economic journal, 101(407):920–937. doi:10.2307/2233864.
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 27/27
56. Data Estimation Business Cycles Analysis
Measuring the Weather
• Challenge: computing at a macro level a weather index strongly
correlated with real agricultural production.
• Solution: create soil moisture deficit index:
1. Collect soil water deficit data fril weather stations (on a monthly
basis).
2. Compute percentage deviation from monthly median as in
Narasimhan and Srinivasan (2005): Dt,m =
SW Dt,m−Med(SW Dm)
Med(SW Dm)
and include persistence of deficits,
SMDIt,m = 0.5 × SMDIt,m−1 +
Dt,m
50
that captures the
evatransporation.
3. Aggregation: compute an average per region, get national index by a
weighting each region by its relative size in national agricultural
production.
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 28/27
57. Data Estimation Business Cycles Analysis
Measuring the Weather (cont.)
-45
-40
-35
170 175
Auckland
Bay of Plenty
Canterbury
Gisborne
Hawke’s Bay
Manawatu-Wanganui
Marlborough
Northland
Otago
Southland
Taranaki
Tasman/Nelson
Waikato
Wellington
West Coast
Q1 Q2
Q3 Q4
25
50
75
100
mm
Fig a: Weather stations in New Zealand Fig b: Seasonality of Weather
Historical average 2012Q1 2013Q1
(0,10]
(10,30]
(30,50]
(50,70]
(70,90]
(90,110]
(110,130]
(130,150]
Fig c: Historical vs Normal times (2012:1) vs Drought (2013:1)
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 29/27
58. Data Estimation Business Cycles Analysis
Measuring the Weather (cont.)
weather wt
Output gap
Agricultural Output gap
-2
-1
0
1
2
-0.03
-0.02
-0.01
0.00
0.01
0.02
-0.050
-0.025
0.000
0.025
0.050
1990 1995 2000 2005 2010 2015
1990 1995 2000 2005 2010 2015
1990 1995 2000 2005 2010 2015
Figure 9: Weather and output gaps (Shaded Area denotes drought episodes)
Go back to the model
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 30/27
59. Data Estimation Business Cycles Analysis
Calibration
Variable Interpretation Value
β Discount factor 0.9883
δK Capital depreciation rate 0.025
α Share of capital in output 0.33
g Share of spending in GDP 0.22
ϕ Share of good in consumption basket 0.15
¯HN = ¯HA Hours worked 1/3
σC Risk aversion 1.5
¯ Land per capita 0.40
ω Share of land in agricultural output 0.15
δ Rate of decay of land efficiency 0.10
αN Openness of non-agricultural market 0.25
αA Openness of agricultural market 0.45
χB International portfolio cost 0.007
σ∗
C Foreign risk aversion 1.5
b∗ Foreign consumption habits 0.7
Table 2: Calibrated parameters.
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 31/27
60. Data Estimation Business Cycles Analysis
Calibration (cont.)
Variable Interpretation Model Data
¯C/ ¯Y consumption-to-GDP 0.56 0.57
¯I/ ¯Y investment to GDP 0.22 0.22
400 × (¯r − 1) real interest rate 4.74 4.75
(1 − ϕ) αN + ϕαA goods market openness 0.28 0.29
n ¯Y A/ ¯Y farming production-to-GDP 0.08 0.07
Table 3: Steady state ratios (empirical ratios are computed using data between 1990 to 2017).
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 32/27
61. Data Estimation Business Cycles Analysis
Estimation
Prior distributions Posterior distribution
Shape Mean Std. Mean [5%:95%]
σH Labor disutility B 2 0.75 1.87 [1.32:2.40]
b Consumption habits B 0.7 0.10 0.82 [0.74:0.90]
ι Labor sectoral cost G 2 1 2.32 [1.36:3.31]
κ Investment cost N 4 1.50 1.83 [0.77:2.91]
µ Subst. by type of goods G 1.5 0.8 4.93 [3.53:6.26]
µN Subst. home/foreign G 1.5 0.8 1.91 [0.86:2.94]
µA Subst. home/foreign G 1.5 0.8 0.41 [0.26:0.56]
φ Land expenditures cost G 1 0.60 0.76 [0.02:1.51]
θ Land-weather elasticity U 0 10 8.62 [2.3:15.78]
Marginal log-likelihood -1012.83
Table 4: Prior and posterior distributions of structural parameters.
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 33/27
62. Data Estimation Business Cycles Analysis
Do Weather Shocks Matter?
No Weather-Driven Weather-Driven
Business Cycles Business Cycles
M (θ = 0) M (θ = 0)
Prior probability 1/2 1/2
Laplace approximation -1016.853 -1012.835
Posterior odds ratio 1.000000 55.626
Posterior model probability 0.018 0.982
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 34/27
63. Data Estimation Business Cycles Analysis
Historical Decomposition
1994 1996 1999 2002 2005 2007 2010 2013 2016
−4 %
−2 %
0 %
2 %
4 %
GDP log(Yt/ ¯Y )
1994 1996 1999 2002 2005 2007 2010 2013 2016
−10 %
0 %
10 %
Agricultural production log(Y A
t / ¯Y A
)
Supply shocks (ηZ
t ) Demand shocks (ηG
t + ηI
t + ηA
t )
Foreign shocks (η∗
t ) Weather shocks (ηW
t )
Residual Variable Path
Figure 10: Historical decomposition of aggregate output and agricultural production.
E. Gallic & G. Vermandel Weather Shocks, Climate Change and Business Cycles - 35/27