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

7

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

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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

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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

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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

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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

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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

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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