distributional effects of finland’s climate policy package juha honkatukia, jouko kinnunen ja...
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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)
2
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)
3
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
4
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
5
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
6
Setting up the simulations
• Policies1) 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 30-45€/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/julkaisu/Publication_6093_id/796
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Cumulative changes in GDP from baseline
Change in GDP
-1,6
-1,4
-1,2
-1
-0,8
-0,6
-0,4
-0,2
0
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
Cumulative change from baseline, per cent
EU ETS Energy package (30€) Energy package (45€) ETS and RES Kyoto target
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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 several-household model; core model intact
9
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)
Income distribution module (1/
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Income categories
• Capital and land income • Labor income• Old-age benefits• Unemployment benefits• Other transfers
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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
12
Cumulative changes in main Macroeconomic
variables(full energy package – allowance
price 30 €)
-2,5
-2
-1,5
-1
-0,5
0
0,5
2008 2010 2012 2014 2016 2018 2020 2022 2024
Real GDP
Aggregate employment
Aggregate real investment expenditure
Real household consumption
13
Contributions of GDP expenditure items
to cumulative change(full energy package – allowance
price 30 €)
-2
-1,5
-1
-0,5
0
0,5
1
Exports Consumption Government Imports Investment
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 0,5 0,546 0,584 0,617 0,632 0,65 0,669 0,69 0,715
Government 0 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 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
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
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Change in industrial output 2020
-25 -20 -15 -10 -5 0 5 10
Agriculture and forestry
Forest industries
Metal industires
Machinery and equipment
Other industries
Energy
Transports
Private services
Public services
Cumulative change form baseline, per cent
Changes in industry structure
(full energy package – allowance price 30 €)
• Energy package changes industry output significantly
• Decline in all industries except agriculture and forestry
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Changes in industry structure
(full energy package – allowance price 30 €)
• Employment changes reflect changes in output
Change in employment, wage bill weights, 2020
-10 -8 -6 -4 -2 0 2 4 6 8
Agriculture and forestry
Forest industry
Basic metal industries
Machinery and equipment
Other Industries
Energy
Transport
Privat service industry
Public services and administration
Change frpm baseline, per cent
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CES CES
Good 1 Good C. . . up to . . .
Klein-Rubin
Household Utility
ImportedGood 1
from the EUDomesticGood 1
ImportedGood 1
fromthe Non-EU
DomesticGood C
ImportedGood C
from the EU
ImportedGood C
fromthe Non-EU
Aggregated household consumption
in the VATTAGE model
Estimated from Household budget survey
Used estimates made in Global Trade Analysis Project (GTAP)
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0 10 20 30 40 50 60 70
PostTelecom
ManuElecOpti
ManMachinery
Publishing
HotelRest
GenConBuild
ManTransporE
ManMetalProd
ManTobac
ManBrev
ManFood
ManTextiles
ManRubPlasti
PublicAdmini
HealthSocial
Education
Mann.e.c.
Financial
ManPaper
SupTranAct
ManBasMetals
BuySellrealE
CulturSports
Realestate
ConRoadWater
Agriculture
ManNmetMineP
Trade
ManIronSteel
WaterTrans
Mining
ManChemicals
AirTransport
LandTrans
ManFineprint
PulpAndOth
ExtPeat
Forestry
ManNewsprint
WaterPurif
WaterTrans
ElecGas
ManWoodCork
ManOthOilP
Share of energy of production costs by product
(without energy products, <70%)
18
Consumption share of energy use in year 2005,
per cent (both direct and indirect use
included)
0 1 2 3 4 5 6 7
Students
Entrepreneurs
Unemployed andother
Upper white-collar
Retired
Lower white-collar
Blue-collar
Farmers
Share ofenergy costof totalconsumption
19
Change in income and real consumption in year 2020 by socio-economic group
(per cent from base scenario, ordered by income level)
-3
-2,5
-2
-1,5
-1
-0,5
0
ST
UD
EN
TS
UN
EM
P_O
TH
ER
FA
RM
ER
S
BLU
EC
OLL
AR
RE
TIR
ED
EN
TE
RP
RE
NE
UR
LOW
HIT
EC
OLL
AR
UP
WH
ITE
CO
LLA
R
Change in real consumption 2020 Change in net earnings 2020
20
Contributions of product groups to changes in
consumption volumes in 2020
-2,5
-2
-1,5
-1
-0,5
0
0,5
1
1,5
Fa
rme
rs
En
terp
ren
eu
rs
Up
pe
r w
hite
-co
llar
Lo
we
r w
hite
-co
llar
Blu
e-c
olla
r
Stu
de
nts
Re
tire
d
Un
em
plo
yed
an
d o
the
r
Other products
Vehicles
Housing, including leisure housing
Transport
Fuels, heat, water and electricity
Forestry products
21
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
22
Consumption share of energy use in year 2005, per
cent by decile (both direct and indirect use included)
0 1 2 3 4 5 6 7
D0
D1
D2
D3
D4
D5
D6
D7
D8
D9
23
Change in income and real consumption in year 2020
by income decile (per cent from base scenario)
-3
-2.5
-2
-1.5
-1
-0.5
0
D0 D1 D2 D3 D4 D5 D6 D7 D8 D9
Change in real consumption 2020
Change in net earnings 2020
24
Cumulative deviation from BASE in real consumption by
income decile
-2
-1.5
-1
-0.5
0
0.5
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025
D1
D2
D3
D0
D8
D5
D4
D6
D7
D9
25
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