Transcript Dia 1 - GTAP

Distributional effects of
Finland’s
climate policy package
Juha Honkatukia,
Jouko Kinnunen ja
Kimmo Marttila
10 June 2010
GTAP 2010
GOVERNMENT INSTITUTE FOR ECONOMIC RESEARCH (VATT)
Outline of the presentation
• Motivation
• The VATTAGE model
• Economic Impacts of climate change
in Finland
• Income distribution module
• Results
• Conclusions
• Further model development (if time
left)
2
Motivation
• The European Council accepted the
Energy and Climate package in
December 2008 -> CO2 emission
targets
• When prices of CO2-intensive goods
increase, what happens to
consumption opportunities of different
household groups?
– Are climate policies regressive?
– Is there some group that will be better off
than others?
• Top-Down Modeling of households
3
What is the VATTAGE?
• Applied/Computable General Equilibrium
Model for Finnish Economy
• Bases on well-known ORANI and MONASH
models http://www.monash.edu.au/policy/
• The model has been developed with the
needs of several policy applications in mind
• The model is intended as a tool for long term
policy analysis
• Model is ~fully documented and can be found
from VATT’s homepage:
http://www.vatt.fi/julkaisut/uusimmatJulkaisut/julkaisu/
Publication_6093_id/832
4
Setting up the simulations
• Baseline
– National Accounts as starting point
– Macroeconomic forecasts
• AWG (the Ageing Working Group of European
Council): long term projections for macro
variables
• Stability and growth pact
– Industry specific forecasts
• TEM; exports, transports, housing,
construction, energy production, etc.
• STAKES&VATT, AWG; Public services
• Private consumption from model
5
Setting up the simulations
•
Policies
1) EU committed to Kyoto targets and emission
trading
– EU has set target for 2020 emissions
•
•
-20% if go-it-alone
-30% if global
2) During the Kyoto period. Prices of emission
permits rise to 25€/tCO2 by 2012, and to 3045€/tCO2 by 2020
3) Policies for renewables
•
•
•
Feed-in tariffs for wind power and biogas
Tax cuts or subsidies for wood
Blending requirements for biofuels (10% by
2020)
4) Energy-saving measures in all sectors
Analyses of different policies combined with
energy sector model can be found from VATT’s
homepage:
http://www.vatt.fi/julkaisut/uusimmatJulkaisut/ju
lkaisu/Publication_6093_id/796
6
Cumulative changes in GDP
from baseline
Change in GDP
Cumulative change from
baseline, per cent
0
-0,2
-0,4
-0,6
-0,8
-1
-1,2
-1,4
EU ETS
Energy package (30€)
Energy package (45€)
ETS and RES
2025
2024
2023
2022
2021
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
-1,6
Kyoto target
7
Income distribution module
•
•
•
•
Main idea: top-down disaggregation of income and
consumption to eight different household types
(mimicking top-down regional effects calculus in
Monash-type state models)
Consumption: consumption function parameters
estimated
Income structure by household type linked to generic
VATTAGE income categories
Population: each age cohort divided into household
types
– Partly endogenous based on changes in labor markets
– Partly exogenous based on age-structure
(population growth and ageing based on Statistics
Finland’s population projection 2007)
Note: less data needed than in a full-fledged severalhousehold model; core model intact
8
Income distribution module (1/
Household types
(Socio-economic Groups,
classification code in brackets)
•
•
•
•
•
•
•
•
Farmer (10)
Entrepreneur (20)
Upper white-collar employee (30)
Lower white-collar employee (40)
Manual worker (50)
Student (60)
Retired (70)
Unemployed and others (80 + 90)
9
Income categories
•
•
•
•
•
Capital and land income
Labor income
Old-age benefits
Unemployment benefits
Other transfers
10
Data used in income
distribution module
• Income distribution statistics: Shares of
different income types and tax rates from
household income (~28,000 obs.)
• Household Budget Survey 2006: expenditure
shares by household type – estimation of
consumption functions (4,007 obs.)
• Fitted to aggregate household consumption
data of VATTAGE (from national accounts)
• Re-estimation of consumption function of
the representative consumer
11
Cumulative changes in main
Macroeconomic variables
(full energy package – allowance price 30 €)
0,5
0
-0,5
-1
-1,5
-2
-2,5
2008
2010
2012
2014
2016
2018
2020
2022
2024
Real GDP
Aggregate employment
Aggregate real investment expenditure
Real household consumption
12
Contributions of GDP
expenditure items
to cumulative change
(full energy package – allowance price 30 €)
1
0,5
0
-0,5
-1
-1,5
-2
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
Investment
-0,268 -0,243 -0,164 -0,13 -0,106 -0,087 -0,062 -0,035 -0,009 0,011 0,027 0,042 0,043
Imports
0,305 0,328 0,384 0,445
Government
0
0,5
0,546 0,584 0,617 0,632 0,65 0,669 0,69 0,715
0,001 0,002 0,002 0,003 0,003 0,003 0,002 0,001 -0,001 -0,003 -0,005 -0,008
Consumption -0,432 -0,506 -0,576 -0,661 -0,735 -0,799 -0,855 -0,904 -0,935 -0,962 -0,985 -1,006 -1,036
Exports
Exports
0,081 -0,04 -0,275 -0,41 -0,495 -0,541 -0,567 -0,583 -0,551 -0,526 -0,512 -0,509 -0,525
Consumption
Government
Imports
Investment
13
Changes in industry
structure
(full energy package – allowance price 30 €)
• Energy package changes industry
output significantly
• Decline in all industries except
agriculture and forestry
Change in industrial output 2020
Public services
Private services
Transports
Energy
Other industries
Machinery and equipment
Metal industires
Forest industries
Agriculture and forestry
-25
-20
-15
-10
-5
0
5
10
Cumulative change form baseline, per cent
14
Changes in industry structure
(full energy package – allowance price 30 €)
• Employment changes reflect changes
in output
Change in employment, wage bill weights, 2020
Public services and administration
Privat service industry
Transport
Energy
Other Industries
Machinery and equipment
Basic metal industries
Forest industry
Agriculture and forestry
-10
-8
-6
-4
-2
0
Change frpm baseline, per cent
2
4
6
8
15
Aggregated household
consumption
in the VATTAGE model
Household
Utility
Good 1
CES
Domestic
Good 1
Imported
Good 1
from the EU
Klein-Rubin
Estimated from
Household budget
survey
. . . up to . . .
Good C
Used estimates
made in Global
Trade Analysis
Project (GTAP)
Imported
Good 1
from
the Non-EU
Domestic
Good C
CES
Imported
Good C
from the EU
Imported
Good C
from
the Non-EU
16
ManOthOilP
ManWoodCork
ElecGas
WaterTrans
WaterPurif
ManNewsprint
Forestry
ExtPeat
PulpAndOth
ManFineprint
LandTrans
AirTransport
ManChemicals
Mining
WaterTrans
ManIronSteel
Trade
ManNmetMineP
Agriculture
ConRoadWater
Realestate
CulturSports
Share of energy of
production costs by product
(without energy products, <70%)
BuySellrealE
ManBasMetals
SupTranAct
ManPaper
Financial
Mann.e.c.
Education
HealthSocial
PublicAdmini
ManRubPlasti
ManTextiles
ManFood
ManBrev
ManTobac
ManMetalProd
ManTransporE
GenConBuild
HotelRest
Publishing
ManMachinery
ManuElecOpti
PostTelecom
17
0
10
20
30
40
50
60
70
Consumption share of energy use
in year 2005, per cent
(both direct and indirect use included)
Farmers
Blue-collar
Lower white-collar
Retired
Upper white-collar
Unemployed and
other
Share of
energy cost
of total
consumption
Entrepreneurs
Students
0
1
2
3
4
5
6
7
18
Change in income and real
consumption in year 2020 by socioeconomic group
(per cent from base scenario, ordered by income level)
Change in real consumption 2020
Change in net earnings 2020
0
-0,5
-1
-1,5
-2
-2,5
UPWHITECOLLAR
LOWHITECOLLAR
ENTERPRENEUR
RETIRED
BLUECOLLAR
FARMERS
UNEMP_OTHER
STUDENTS
-3
19
Contributions of product groups to
changes in consumption volumes in
2020
Other products
Vehicles
Housing, including leisure housing
Transport
Fuels, heat, water and electricity
Forestry products
1,5
1
0,5
0
-0,5
-1
-1,5
-2
Unemployed and other
Retired
Students
Blue-collar
Lower white-collar
Upper white-collar
Enterpreneurs
Farmers
-2,5
20
What if we group households
into income deciles?
• Households divided into deciles by
income / modified OECD consumption
unit (but with equal population shares)
• Another module with same data
sources and with similar equations
• The consumpion data would not allow
creating soc.econ*decile = 80 groups
into the core model
21
Consumption share of energy use in
year 2005, per cent by decile
(both direct and indirect use included)
D9
D8
D7
D6
D5
D4
D3
D2
D1
D0
0
1
2
3
4
5
6
7
22
Change in income and real
consumption in year 2020
by income decile
(per cent from base scenario)
0
-0.5
-1
-1.5
-2
-2.5
Change in real consumption 2020
Change in net earnings 2020
-3
D0
D1
D2
D3
D4
D5
D6
D7
D8
D9
23
Cumulative deviation from BASE in real
consumption by income decile
0.5
0
D1
D2
D3
-0.5
D0
D8
D5
-1
D4
D6
D7
-1.5
D9
-2
2008 2009 2010 2011
2012 2013 2014 2015 2016
2017 2018 2019 2020
2021 2022 2023 2024 2025
24
Conclusions
•
•
•
•
•
Climate policy does not seem to be regressive in the
light of our results: the large share income transfers
among low-income earners decreases the negative
income effect of climate policy
Farmers and low-income earners winners in relation
to other households when effects are measured
through changes in consumption volume – income
measures tell a different story
Indirect use of energy evens out the effects of climate
policy; analysis concentrating in consumption of
energy products and (directly) energy-intensive
products leads to wrong conclusions about the
distributional effects
The direction of conclusions hinges on the effects
stemming from consumption patterns – consumption
elasticity parameters are important
Results with other consumption functions than LES?
- Actually, changing the consumption functions into
Cobb-Douglas does not change the qualitative story at
all, and even numbers change only a little -> what
seems to matter is differences in the consumption
shares
25