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The Pennsylvania State University
The Graduate School
College of Agricultural Sciences
The Welfare Consequences of Carbon Tax Reform in Open
Economies: The Application of Computable General
Equilibrium Model for Pennsylvania
A Thesis in
Agricultural, Environmental, and Regional Economics
by
Jeong Hwan Bae
© 2005 Jeong Hwan Bae
Submitted in Partial Fulfillment
of the Requirements
for the Degree of
Doctor of Philosophy
December 2005
The thesis of Jeong Hwan Bae was reviewed and approved* by the following:
James S. Shortle
Distinguished Professor of Agricultural and Environmental Economics
Thesis Advisor
Chair of Committee
Adam Rose
Professor of Geography
David Abler
Professor of Agricultural, Environmental and Regional Economics and
Demography
Martin Shields
Associate Professor of Agricultural and Regional Economics
Stephen M. Smith
Professor of Agricultural and Regional Economics
Head of the Department of Agricultural Economics and Rural Sociology
*Signatures are on file in the Graduate School.
iii
ABSTRACT
Taxes on environmental externalities have long been recognized in the
economics literature as a cost‐effective mechanism for reducing the costs of
environmental degradation. However, in recent years there has been substantial
interest in other possible benefits from environmental taxes, most notably, the
economic gains that could result from substituting revenues from environmental
taxes for conventional, distortionary taxes on labor income or other goods and
services. Emerging from this literature is the “double dividend hypothesis” that
the economic gains from environmental taxes will be greater if the revenues are
used to reduce distortionary taxes rather than be returned to consumers through
lump sum transfers.
Most theoretical and empirical research on the double dividend has been
conducted under the assumption of a closed economy. This assumption removes
the possibly important effects of interregional factor mobility and interregional
trade. Moreover it is an unrealistic assumption for most economies, and
especially for those of small countries or sub‐national political jurisdictions, such
as states.
The purpose of this study is to investigate the double dividend hypothesis in
the context of an open economy. The study specifically considers the hypothesis
in the context of a carbon tax in the state of Pennsylvania. To test the double
dividend, the carbon tax revenues are used to reduce labor income taxes in the
state. A static CGE model is constructed for Pennsylvania. The model captures
key features of a regional open economy, most notably endogenous factor
mobility, and interregional trade. An innovative feature of the model is labor
migration in response to environmental quality and the after‐tax wage rate.
Results show that the double dividend holds for the Pennsylvania model for
a carbon tax imposed either unilaterally by the state government, but also for a
national carbon tax. The magnitude of the double dividend is larger for the state
tax than for the federal tax. Labor migration affects negatively the double
dividend, but the impact is small.
iv
TABLE OF CONTENTS
LIST OF TABLES .............................................................................................VIII
LIST OF FIGURES...............................................................................................X
ACKNOWLEDGEMENTS ...................................................................................XI
CHAPTER 1 INTRODUCTION .......................................................................... 1
1.1 Motivation.................................................................................................................... 1
1.2 Objectives..................................................................................................................... 3
1.3 Methodology ................................................................................................................ 4
1.4 Overview of Study Area ............................................................................................. 7
1.5 Outline of the study................................................................................................... 14
CHAPTER 2 LITERATURE REVIEW AND THEORY........................................ 16
2.1 The Welfare Effects of Environmental Taxes ........................................................ 16
2.1.1 First-Best Environmental Taxes .......................................................................... 17
2.1.2 The Double-Dividend Hypothesis ....................................................................... 18
2.1.3 Tax Interaction Effects......................................................................................... 22
2.1.4 Non-Separability of Environmental Effects......................................................... 24
2.2 CGE Models for Environmental Tax Analysis ...................................................... 26
v
2.2.1 Test of the Double-Dividend Hypothesis ............................................................ 26
2.2.2 Economic Impacts of Environmental Taxes ........................................................ 32
2.3 Partial Equilibrium Analysis on the Trade Effect ................................................. 39
2.4 Labor Migration........................................................................................................ 41
2.4.1 Labor Migration ................................................................................................... 42
2.4.2 Empirical Studies on Amenities and Migration................................................... 46
2.5 Partial Equilibrium Analysis on the Environmental Tax and Labor Mobility .. 56
2.6 Other Theoretical Considerations ........................................................................... 60
2.6.1 Potential Effects of Backward Bending Labor Supply Curve ............................. 60
2.6.2 Long Run Effects of the Environmental Tax Recycling Policy .......................... 62
CHAPTER 3 THE PENNSYLVANIA CGE MODEL ........................................... 64
3.1 Main Features of CGE Model.................................................................................. 64
3.2 Interactions among Institutions in PA CGE Model............................................... 69
3.3 Derivation of Equations............................................................................................ 72
3.3.1 Production and factor demands............................................................................ 72
3.3.2 Consumption Sector............................................................................................. 80
3.3.3 Trading Sector...................................................................................................... 85
3.3.3.1 Regional Supply and Export ............................................................................. 88
3.3.3.2 Regional Consumption and Imports ................................................................. 91
3.3.4 Government Account ........................................................................................... 94
vi
3.3.4.1 Tax Payments.................................................................................................... 95
3.3.4.2 Government Budget Balances........................................................................... 98
3.3.5 Environmental Indicator .................................................................................... 101
3.3.6 Factor Mobility .................................................................................................. 103
3.4 Equilibrium and Macro Closure Rule .................................................................. 104
CHAPTER 4 SCENARIOS AND RESULTS..................................................... 108
4.1 Experimental Design............................................................................................... 108
4.2 Implementation of Carbon Taxes.......................................................................... 110
4.3 Welfare Measurement ............................................................................................ 112
4.4. RESULTS.................................................................................................. 116
4.4.1 $5/t State Carbon Tax without Revenue Recycling .......................................... 116
4.4.2 $5/t State Carbon Tax with Revenue Recycling................................................ 120
4.4.3 $5/t Federal Carbon Tax with Revenue Recycling ........................................... 123
4.4.4 Alternative Carbon Tax Rates............................................................................ 125
4.4.5 Sensitivity Analysis: Migration Elasticities ....................................................... 130
CHAPTER 5 CONCLUSIONS.......................................................................... 132
5.1 SUMMARY AND MAIN FINDINGS ............................................................ 132
vii
5.2 FURTHER STUDY ..................................................................................... 135
REFERENCES ................................................................................................. 137
APPENDIX A: NUMBER OF EQUATIONS AND VARIABLES ....................... 154
APPENDIX B: DATA AND STATISTICS......................................................... 158
B.1 Carbon emission and Energy consumption ......................................................... 158
B.2 Hybrid SAM of Pennsylvania ............................................................................... 167
B.3 Data on Elasticities ................................................................................................. 174
B.4 Benchmark Value of Economic Variables ........................................................... 176
viii
LIST OF TABLES
Table 1.1 Base Year Output, Trade, and Consumption from Hybrid IMPLAN (Million
Dollars) ............................................................................................................................... 9
Table 1.2 Classification of Durable and Non‐durable Industries............................ 10
Table 2.1 Classification of CGE Models on Environmental Taxation ............................. 39
Table 3.1 Description of Industrial Sectors ................................................................. 66
Table 3.2 Labor Income Tax, Proprietary Tax, and Corporate Taxes and Rates... 96
Table 3.3 Indirect Business Tax Payments and Rates ............................................... 97
Table 4.1 Scenarios ....................................................................................................... 110
Table 4.2 Carbon Tax and Equivalent Ad‐Valorem Fuel Tax Rates...................... 112
Table 4.3 Tax Revenue of Carbon Taxes by Fuel Type ($ Million) ....................... 112
Table 4.4 Relative Changes in Major Economic Variables for The $5/T Carbon Tax
without Revenue Recycling (%)................................................................................. 119
Table 4.5 Relative Changes in Economic Variables for The $5/T Carbon Tax with the Tax
Revenue Recycling ......................................................................................................... 122
Table 4.6 Relative Changes in Major Economic Variables for the $5/T of Federal
Carbon Tax with the Tax Revenue Recycling .......................................................... 124
Table 4.7 Relative Changes in Major Economic Variables for the $10/t and $15/t of
Carbon Taxes without Revenue Recycling............................................................... 127
Table 4.8 Reduction in the Carbon Emission for $5 of Carbon Tax ...................... 129
Table 4.9 Reduction in the Carbon Emission for $10 of Carbon Tax .................... 129
ix
Table 4.10 Reduction in the Carbon Emission for $15 of Carbon Tax .................. 129
Table 4.11 Relative Changes in Major Economic Variables for the Sensitivity of
Migration Elasticities on $5/t of Carbon Taxes without the Tax Revenue Recycling
......................................................................................................................................... 131
Table A.1 Count of Independent Equations and Endogenous Variables ............. 154
Table B.1 Changes in the Share of GHGs Emission by Emission Source ............. 159
Table B.2 Changes in CO2 Emission by Sectors....................................................... 160
Table B.3 Energy Consumption by Sectors............................................................... 161
Table B.4 GHG Inventory in Pennsylvania............................................................... 162
Table B.5 Carbon emission of Pennsylvania by Emission Sources ....................... 163
Table B.6 Pennsylvania Coal Statistics ...................................................................... 164
Table B.7 Oil Prices in 2000, Pennsylvania ............................................................... 165
Table B.8 Natural Gas Price ........................................................................................... 166
Table B.9 Hybrid SAM for Pennsylvania, 2000 ....................................................... 168
Table B.10 Description of Variables........................................................................... 173
Table B.11 Elasticities of Substitution in Production Functions ............................ 174
Table B.12 Elasticity Data for Armington and CET functions ............................... 175
Table B.13 Benchmark Value of Economic Variables (Million U.S. Dollar.......... 176
x
LIST OF FIGURES
Figure 1.1 Expenditure Trend on Energy by sources in PA ............................................. 13
Figure 1.2 Energy Sources of Electricity.......................................................................... 14
Figure 2.1 Partial Equilibrium Analysis on Efficiency Effects of Environmental Taxes. 21
Figure 2.2 Two Region Equilibrium Model ..................................................................... 44
Figure 2.3 Labor Migration Effects on the Deadweight Loss .......................................... 58
Figure 2.4 Feedback of Environmental Taxes in the Long Run ....................................... 63
Figure 3.1 Flow Diagram of Pennsylvania CGE Modeling....................................... 71
Figure 3.2 NCES Production System................................................................................ 73
Figure 3.3 Consumer’s Utility System ........................................................................ 81
Figure 3.4 Structure of Trading Sector ........................................................................ 87
Figure 3.5 Structure of Trading Sector ........................................................................ 87
xi
ACKNOWLEDGEMENTS
My first kudos to Professor Shortle for his warm‐hearted advice and
guidance in writing my dissertation! Whenever I was lost, he showed the right
way. Whenever I became overly self‐complacent, he warned me to be humble.
Whenever I was disappointed with consequences, he cheered me up. Professor
Rose kindly taught me CGE modeling, which is the crux of my dissertation. He
has come up with innovative ideas to fix problems in the analysis. Professors
Abler and Shields constantly backed me to focus on the dissertation and their
comments were truly instrumental. I thank Professor Smith for his careful review
of my dissertation draft.
A dream comes true that I would work for a sustainable earth where human
beings and all other creatures co‐exist peacefully. The dream will sustain my
intellectual journey until I exhaust all my knowledge and experiences to
materialize the sustainable world.
Last, but not the least kudos to my endeared family for their
encouragements and the support. My most beloved wife, Hyun Ju has stood by
me whenever I was in trouble. My great appreciation goes to my parents for their
constant support with unselfish love throughout the long journey, Ph.D. program.
All the success and honor I am now entitled to are ascribed to their unlimited
support and love.
1
CHAPTER 1
INTRODUCTION
1.1. Motivation
Taxes on environmentally harmful products or activities have long been of
interest to economists as a means for efficiently reducing environmental
externalities. During the last decade, environmental taxes have been applied in
practice to various environmental problems, both internationally and within the
U.S. (U.S. EPA, 2001 and 2004). In many cases, environmental taxes have been
charged on production or consumption activities that affect water, air, and land
pollution.
Revenues from the environmental tax are used to compensate the
administrative costs, develop environment‐friendly technology or transfer to
consumers or firms (U.S. EPA, 2001 and 2004). Economists have found, However,
that “recycling revenue” from environmental taxes to cut marginal tax rates for
labor income or other distortionary taxes may provide a ‘double dividend’ (Lee
and Misiolek, 1986; Pearce, 1991; Repetto et al., 1992; Oates, 1993; Poterba, 1993).
The first dividend, sometimes called the ‘Pigouvian effect’, is the conventional
benefit from reducing negative environmental externalities. The second dividend,
2
called the ‘tax recycling effect’, is the reduction in the deadweight costs of the
conventional taxes.
While early economic literature on the use of environmental tax revenues
focused on the “double dividend”, more recent literature (e.g., Goulder, 1995;
Parry, 1995; Goulder et al., 1997; Parry, 1997) has identified a third effect, referred
to as the ‘tax interaction effect’, which reduces the gains. The tax interaction
effect results from changes in labor supply, induced by the environmental tax,
which may amplify labor market distortions (Goulder, 1995). Recent theoretical
and empirical research on the double dividend hypothesis shows that the welfare
loss of the interaction effects is larger than the welfare gain of tax revenue effects
(Parry, 1995; Bovenberg and Goulder, 1996). But these results are now being
debated, with additional studies showing that there are other types of tax
interaction effects that may augment the conventional double dividend
(Schwartz and Repetto, 2000; Parry and Bento, 2000).
The extant literature on the double dividend debate has been confined to
closed economies (i.e., economies without trade or interregional factor flows)
(Parry, 1997; Goulder et al., 1997; Parry et al., 1999; Goulder et al., 1999; Parry
and Bento, 2000). Even large contemporary economies are open to and
increasingly influenced by globalization of trade and factor migration (Morgan et
al., 1989).
3
This research proposes that inter‐regional factor migration and inter‐
regional trade effects can significantly influence the magnitude and sign of tax
revenue and tax interaction effects. Carbon taxes are considered as an important
example of environmental taxes. Pennsylvania offers a useful case study as a
major producer of carbon emissions within the U.S. Specifically, this study
explores how inter‐regional labor mobility and inter‐regional trade affect the
welfare consequences of substituting carbon taxes for conventional taxes in the
context of a state (Pennsylvania) level economy.
1.2 Objectives
The purpose of this research is to explore economic benefits and
environmental impacts of substituting carbon taxes for labor income taxes in the
context of an open state economy.
The study pursues three specific objectives.
The first objective is to investigate the effects of interregional migration and
trade on the economic impact of a carbon tax.
The second objective is to examine the double dividend hypothesis when
the revenue from a carbon tax replaces revenue from labor income taxes given
interregional labor migration.
4
The third objective is to examine the effect of carbon taxes when imposed
by federal versus state authorities. A carbon tax charged by a state will change
the relative environmental amenity between the state and other states. The
change in the relative environmental quality will impact the sign and the
magnitude of the labor migration. However, a carbon tax imposed by the
national government will have a smaller impact on relative environmental
quality.
The primary innovative features in this study are the modeling of
endogenous trading and labor migration. This study will show how the double
dividend will be affected by the trading, labor migration and the different
environmental tax authorities.
1.3 Methodology
A static computable general equilibrium (CGE) model of the Pennsylvania
economy is constructed for this study. Taxes on the emission of carbon is
implemented in the form of fuel taxes. Fuel taxes are imposed on the
intermediate demand for fossil fuels. Labor income taxes are reduced to maintain
the tax revenue neutrality. The model consists of seventeen industries, one
representative household, three factor inputs, and a two‐tier trade system.
Industries include coal, oil, natural gas, alternative fuels, electricity,
5
transportation, agriculture, mining, construction, non‐durable manufacturing,
durable manufacturing, utility, electric and gas utility, trade, FIRE(Financial,
Insurance, and Real Estate), services, and others. Factor inputs are labor,
proprietors, and capital. Trade consists of foreign and inter‐regional trade.
CGE models are commonly used to analyze the welfare effects of
environmental taxes at both national and sub‐national levels when economy‐
wide impacts are expected (Gunning and Keyzer, 1995). CGE models provide a
theoretically consistent tool for modeling equilibrium responses in multiple‐
interdependent sectors (Shoven and Whalley, 1992).
The General Algebraic Modeling System (GAMS) software is used to
program the CGE model and obtain feasible solutions (Brooke et al., 1998). The
CONOPT solver is employed to solve the optimization problem.
The benchmark model representing the baseline economy is constructed
using a Social Accounting Matrix (SAM) generated from IMPLAN (Impact
Analysis for PLANning). A SAM is a snapshot of an economy reflecting
monetary flow of interactions among institutions. Physical energy consumption
data are integrated into the SAM to facilitate modeling carbon taxes. To examine
the impacts of carbon taxes substituting for the labor income tax, counterfactual
models are examined for various scenarios. Scenarios include cases of state and
federal taxes combined with migration versus no migration.
6
There are key features in the Pennsylvania CGE model relevant to this
analysis: substitutability of both conventional fossil fuels and alternative energy
sectors, substitutability between leisure and market goods, endogenous export
prices and endogenous labor migration.
Nested constant elasticity of substitution (CES) production functions are
used to model substitution among labor, proprietors, capital, energy, and
intermediate inputs. Energy composite includes coal, oil, gas, electricity, and an
aggregated alternative fuel including hydropower, nuclear power, solar,
geothermal, bio‐energy, fuel cells, and waste. The substitutability among fossil
fuels and alternative fuels reflects industry’s response to avoid the increased
production costs due to the carbon tax.
A nested CES utility function is used to model final commodity demands.
This specification captures substitutability between demand for leisure and
demand for market goods. Consumers will change labor supply and
consumption as the carbon tax replaces the labor income tax in part.
Two tier Armington and constant elasticity of transformation (CET)
functions are used to model trade. Armington functions capture imperfect
substitutability between imports and regional products. CET functions represent
the differentiation of markets for exports and regional products. The imperfect
substitution between traded and non‐traded goods reflects differentiated
7
production. Thus, one good can be imported and produced in a region or a
country at the same time. Also, one good can be regionally consumed and
exported to the other region or country simultaneously.
Labor supply is affected by interregional labor migration. Labor mobility is
a function of relative real wages and relative environmental quality between
Pennsylvania and other states. Environmental damage affecting the overall
environmental indicator is a diminishing function of consumption of fossil fuels.
1.4 Overview of Study Area
Pennsylvania has been one of major carbon‐emitting states in the U.S. In
1998, Pennsylvania ranked fourth among the states in U.S. GHG (Green House
Gases) emissions. Also, the state is an observer in the Regional Greenhouse Gas
Initiative (RGGI), a consortium of state governments exploring the reduction of
GHGs in the northeast states (www.RGGI.org). A multi‐state cap‐and‐trade
program with a market‐based emissions trading system is the central initiative in
this consortium, and the design of regional strategies is under discussion. Carbon
taxation can be a possible regional strategy for GHGs emission abatement. If
carbon tax reform can generate efficiency gains from recycling revenues of
carbon taxes in addition to the primary gains from environmental improvement,
Pennsylvania will be better off with carbon taxes than with a tradable permit
8
system given that the permits are allocated based on historical emission of
carbon dioxide (grandfathering)1.
On the overall economic and environmental profile of Pennsylvania for year
2000, the gross state product (GSP) of Pennsylvania was $ 394,649 million, and
the total number of employment is 6,887,870 in the year 2000. In the same year,
the total population of Pennsylvania was 12,285,492, total number of households
was 4,777,003, total personal income was $267 billions, labor income was $197
billion, and gross state per capita income was $29,697 in year 2000 (U.S. Census
bureau, 2000).
Industries are aggregated into 17 sectors and the aggregation is focused on
energy production industries such as coal, oil (petroleum), and natural gas,
alternative fuels, and electricity. Alternative fuels include renewable energy such
as geothermal, biomass, hydrogen, hydropower, solar, and wind as well as
nuclear power. Table 1.1 shows output, trade, and final consumption for each
industry in 2000.
1 However, if the emission permit is auctioned, the tradable permit will have the same effect with the carbon tax.
9
Table 1.1 Base Year Output, Trade, and Consumption from Hybrid IMPLAN (Million
Dollars)
Industrial Sector Output Foreign Import
Domestic Import
Foreign Export
Domestic Export
Final Consumption
Agriculture 6,821.72 60.03 1,298.03 285.42 773.14 917.91
Mining 936.64 11.68 141.99 30.08 766.55 12.49
Coal 3,719.27 49.36 660.77 411.24 2,725.96 0.49
Gas 1,347.76 0.00 656.97 0.00 0.00 477.66
Oil 973.84 116.84 0.00 0.00 0.00 96.96
Alternative fuels 169.24 4.60 36.49 0.00 0.00 24.65
Construction 49,922.52 1,424.98 8,775.97 0.00 1,479.77 0.00
Nondurable manufacture
116,845.42 2,433.47 18,981.12 11,585.10 20,120.68 34,810.53
Durable manufacture
100,726.37 4,551.41 23,027.73 21,047.88 41,521.60 6,121.09
Transportation 24,996.86 186.91 2,820.71 3,582.43 3,647.39 5,091.44
Utility 18,651.63 174.60 2,600.73 225.81 6,943.21 4,512.63
Electricity 10,874.47 19.33 492.32 22.33 3,019.72 832.75
Electric and gasutility
4,761.56 352.31 1,549.82 37.29 310.48 1,771.99
Trade 86,304.94 240.57 5,324.20 3,099.80 352.50 53,630.02
F.I.R.E 109,135.98 62.60 9,059.92 2,715.58 30,692.73 46,949.79
Services 144,230.44 716.26 11,948.09 1,080.78 19,470.46 62,849.77
Others 43,315.25 43.61 651.65 1,177.59 657.43 6,679.23
This study divides the manufacturing sector into non‐durable and durable
sectors, since the two sectors require different intermediate and energy inputs.
The U.S. Bureau of the Census, Bureau of Economic Analysis, and Bureau of
Labour Statistics publish a durable/non‐durable manufacturing industry
classification (table 1.2).
10
Table 1.2 Classification of Durable and Non-Durable Industries
Durable Manufacturing Industries Non-Durable Manufacturing
Industries Wood Product Manufacturing
Non Metallic Mineral Product Manufacturing
Primary Metal Manufacturing
Fabricated Metal Product Manufacturing
Machinery Manufacturing
Computer and Electronic Product
Manufacturing
Electric Equipment, Appliance and
Component Manufacturing
Transportation Equipment Manufacturing
Furniture and Related Product
Manufacturing
Miscellaneous Manufacturing
Food Manufacturing
Beverage and Tobacco Product
Manufacturing
Textile and Product Mills
Clothing Manufacturing
Leather and Allied Product Manufacturing
Paper Manufacturing
Printing and Related Support Activities
Petroleum and Coal Products
Manufacturing
Chemical Manufacturing
Plastics and Rubber Products
Manufacturing
(Source: Bureau of Economic Analysis,2000)
State government taxes are divided into corporation taxes, consumption
taxes, personal income taxes, realty transfer taxes, inheritance taxes, and minor
and repealed taxes. The corporation tax accounted for 19% of total state
government revenues in 2000. The proportion of consumption taxes to the total
government tax revenue is 37%, and for personal income tax and others the
proportion is 42% (Pennsylvania Department of Revenue, 2002).
Local government taxes consist of real estate tax (property tax), earned
income tax, per capita tax, occupation tax, occupational privilege tax, real estate
transfer, amusement/admissions tax, and business gross receipts tax.
11
Pennsylvania local governments levy a property tax (real estate tax) divided into
school district, county, and municipal levels. In 2000, the property tax accounted
for $9.8 billion which was 30% of total local government revenues or 70% of all
local government tax revenues (Governor’s Center for Local Government
Services, 2002).
Energy production sectors include coal, petroleum, natural gas, and
electricity. Pennsylvania’s total production of coal in the year 1999 was 76,399
thousand short ton (TST), 48.3% of total production was used within
Pennsylvania, 41.6% was consumed by other states, and 9.12% was exported to
foreign countries2. In the same year, the total employed in the state by the coal
industry is 9,318 (0.14% of all employed) and total expenditure on the
consumption of coal were $1853.9 million3. In 2000, 2,385,051 million cubic feet
(MCF) of natural gases were imported from other states such as Delaware,
Maryland, New Jersey, New York, Ohio, and West Virginia, and 1,868,831 MCF
2 Sources: Energy Information Administration (EIA), Form EIA-3, "Quarterly Coal Consumption Report Manufacturing Plants"; Form EIA-5, "Coke Plant Report Quarterly"; Form EIA-6A, "Coal Distribution Report"; Form EIA-7A, "Coal Production Report"; Form EIA-759, "Monthly Power Plant Report," and U.S. Department of Labor, Mine Safety and Health Administration, Form 70002, "Quarterly Mine Employment and Coal Production Report." 3 Table1. Energy Price and Expenditure Estimates by Source, 1970-2001, Pennsylvania at http://www.eia.doe.gov/emeu/states/sep_prices/total/pr_tot_pa.html
12
were exported to the same states4. Total expenditures on the consumption of
natural gas were $4,529.1 million in that year5.
In 2001, Pennsylvania produces 7,000 barrels of petroleum per day, ranked
22nd among U.S. states, and consumed 30.3 million gallons per day, ranked 6th
among the states. Total petroleum expenditures in 2000 were $14,152.5 million6.
Figure 1.1 shows the expenditure trend on energy by sources between 1970 and
2001 in Pennsylvania. The portion of coal expenditure declines compared to total
energy expenditure, while expenditures on gas, oil, and electricity grow.
Expenditure on nuclear and bio energy including waste and wood is less than 2%
in 2001.
4 Pennsylvania International and Interstate Movements in 2000 at http://tonto.eia.doe.gov/dnav/ng/ng_move_ist_a2dcu_SPA_a.htm 5 Pennsylvania International and Interstate Movements in 2000 at http://tonto.eia.doe.gov/dnav/ng/ng_move_ist_a2dcu_SPA_a.htm 6 Pennsylvania International and Interstate Movements in 2000 at http://tonto.eia.doe.gov/dnav/ng/ng_move_ist_a2dcu_SPA_a.htm
13
Figure 1.1 Expenditure Trend on Energy by Sources in PA
0
5000
10000
15000
20000
25000
30000
35000
1970197219741976197819801982198419861988199019921994199619982000
Million Dollars
Electricity
Biofuel
Nuclear
Oil
Gas
Coal
(Source: Energy Information Administration (EIA) website, Energy price and
expenditure by source, 1970-2001, Pennsylvania)
Pennsylvania’s total electricity consumption was 470.5 trillion Btu, ranked
5th in 2001, net generation of electricity was 204,322,878 megawatt hours, and
125,225 TST of carbon dioxide were emitted, ranked 5th in the United States in
20027. Total expenditure on the consumption of electricity were $10,175 million8
7 2002 summary statistics in Pennsylvania at http://www.eia.doe.gov/emeu/states/main_pa.html 8 Pennsylvania International and Interstate Movements in 2000 at http://tonto.eia.doe.gov/dnav/ng/ng_move_ist_a2dcu_SPA_a.htm
14
and the main energy source of electricity was coal (46%), followed by nuclear
(23%) (Figure 1.1).
Figure 1.2 Energy Sources of Electricity
(Source: 2002 summary statistics in Pennsylvania from EIA web site)
1.5 Outline of the study
Chapter 2 reviews the relevant literature on theoretical and empirical studies
of impacts of environmental taxes. A brief partial equilibrium analysis of the
impact of environmental taxes on trading and migration is presented. The
literature review includes a welfare analysis of environmental taxes, empirical
studies on CGE models focused on carbon taxes, and theoretical and empirical
research on labor migration and environmental amenities. The effect of
Energy source of electricity
46%
6%6%
23%
5%
14% coal oil gas nuclear hyroelectric
others
15
backward bending labor supply on welfare consequences and long run
perspectives of the revenue from carbon taxes are discussed as other theoretical
consideration. Chapter 3 describes the overall structure of the CGE model,
equations and endogenous variables. Chapter 4 includes scenario design and
results including the relative changes in the welfare index, macro variables, and
micro variables. Conclusion and discussion of further study are presented in the
last chapter. Appendices include tables on equations and endogenous variables,
the Pennsylvania SAM (Social Accounting Matrix), energy consumption data,
factor demand and income, government, trade, and data on elasticities.
16
CHAPTER 2
LITERATURE REVIEW AND THEORY
The literature review begins with an overview of the economics of
environmental taxes. Next, I summarize research on CGE models for analyzing
the economic and environmental impacts of environmental taxes. Theoretical
and empirical research on the relationship between labor migration and
environmental amenities is then discussed, focusing on the justifications for and
limitations of the use of wage and environmental quality differentials as pull
factors of labor migration. Subsequent sections discuss various other theoretical
considerations, including backward bending labor supply curves, long run
effects of environmental taxes on government revenue, and the decomposition of
economic effects in the CGE model.
2.1 The Welfare Effects of Environmental Taxes
Research on the welfare effects of environmental taxes can be
conveniently classified into four generations. The first generation focused on the
efficiency of environmental taxes as a method for eliminating environmental
17
externalities in a first‐best economy (Pigou, 1938; Baumol and Oates, 1971;
Tietenberg, 1990). The second generation addressed the efficiency gain from
recycling environmental tax revenues to reduce distortions due to conventional
taxes. This second gain in addition to the first gain from environmental
improvement is referred to as ‘double dividend’ (Lee and Misiolek, 1986; Pearce,
1991; Repetto et al., 1992; Oates, 1993; Poterba, 1993). The third generation of
research discusses the negative efficiency loss from environmental taxes
(Bovenberg and Mooij, 1994; Goulder, 1995; Parry, 1995). The major argument of
this generation is that the negative tax interaction effect dominates the positive
environmental and tax recycling effects. Fourth generation literature points out
how the welfare consequences can vary depending on the assumptions about
utility function and market conditions (Parry and Bento, 2000; Schwartz and
Repetto, 2000; Williams III, 2002 and 2003).
2.1.1 First-Best Environmental Taxes
Literature since 1938, when Pigou suggested the environmental tax,
discussed the use of environmental taxes to internalize negative environmental
externalities (Pigou, 1938). The case for taxation was originally and primarily on
the basis of reducing the efficiency losses due to environmental externalities. In
an economy where there exists environmental degradation, the marginal private
cost of producing polluting goods is lower than the marginal social cost
18
including environmental damage. The market prices of polluting goods do not
reflect environmental damage costs, since prices are set as equal to the marginal
private cost. In a first best economy where there are no distortions such as
imperfect market conditions, distortionary taxes, or subsidies, the optimal
Pigouvian (or environmental) taxes are set to be equal to the environmental
damage cost. The imposition of environmental taxes shifts up the marginal
private cost (or market price) until it is equal to the marginal social cost.
However, in a second best economy where distortionary taxes exist, the optimal
environmental taxes will be affected by other efficiency costs from distorted
markets, leading to different level of environmental tax rates. On the other hand,
most studies on the first best environmental tax assume a ‘closed economy
model’ with no factor mobility between the internal and external economies. In
this study, the main assumptions on the first best tax and closed economy will be
changed into a second best tax and open economy with factor mobility.
2.1.2 The Double-Dividend Hypothesis
Economic thought about the use of environmental tax revenues emerged in
the second generation. The literature recognized that environmental taxation
occurs within a social context in which governments levy taxes on “private
goods” to support the provision of “public goods”. Taxes on private goods create
19
distortions that result in welfare losses. In this context, Sandmo’s paper (1975)
was the first to point out that the analysis of environmental taxes with a first best
assumption needed to be modified for a second best economy where ordinary
taxes are imposed on the demand of factors and consumers. Subsequent research
discussed environmental taxes in a second best economy with two main findings:
1) that the gross cost of environmental taxes relies on the marginal rates of
existing distortionary taxes, and 2) that the tax revenue from environmental taxes
can be used to cut the marginal distortionary tax rates (Goulder, 1997).
A number of economists (Lee and Misiolek, 1986; Pearce, 1991; Repetto et al.,
1992; Oates, 1993; Poterba, 1993) have proposed that there is a “double dividend”
if the government uses revenue from environmental taxes to reduce the marginal
rate of distortionary taxes on factor inputs and commodities. The first dividend is
the gain from reducing environmentally damaging activities, now referred to as
the ‘Pigouvian effect’. The second dividend arises from the reduction of the
deadweight loss from pre‐existing taxes, referred to as the ‘tax revenue recycling
effect’; this dividend can reduce the gross cost of environmental taxes.
The double dividend hypothesis can be divided into ‘weak’ and ‘strong’
forms of the double dividend (Goulder, 1994). The weak form implies that there
is more cost saving when revenues from environmental taxes are used to reduce
marginal rates of existing distortionary taxes than when they are given to
20
taxpayers in a lump‐sum transfer. The strong form of the double dividend
hypothesis argues that the gross cost of substituting environmental taxes for
distortionary taxes should be zero or negative.
Partial equilibrium analysis done by Goulder (1997) on the ‘weak’ form
versus ‘strong’ form of the double dividend hypothesis sheds light on the
importance of applying general equilibrium analysis to the welfare effects of
environmental taxes.
Goulder (1997) defines the ‘gross cost’ of revenue‐neutral environmental
taxes as the reduction of individual welfare deducted from the welfare effect
(Pigouvian effect) of environmental improvement. Given that C(te, Tf) denotes
the gross cost of environmental taxes (te), the revenues of which are given to
lump‐sum tax (Tf) reductions, and C(te, tL) denotes the gross cost of substituting
environmental taxes for existing distortionary taxes (tL), the weak double
dividend can be described as:
C(te, Tf) > C(te, tL) (2.1)
This claim is equivalent to the argument that replacing lump‐sum taxes by
distortionary taxes derives positive welfare costs. This argument can be verified
through partial equilibrium analysis on the first best situation for welfare effects
of environmental taxes.
21
Figure 2.1 Partial Equilibrium Analysis on Efficiency Effects of Environmental Taxes
(Source: Goulder, 1997 in Folmer and Tietenberg (eds), 1997)
In Figure 2.1, which represents the market for a polluting good, MCp
denotes the private marginal cost of production, MCs denotes the social marginal
cost of production, MED denotes the associated marginal environmental damage,
and MB denotes the marginal benefit of the good to consumers. Given that
environmental taxes are imposed as equivalent to MED, the gross cost or net
reduction in benefit is area A, and the welfare gain from the reduction in
environmental damage is area A+B. Therefore, the net welfare gain is area B. If
area R, the revenue from environmental taxes, is used to reduce the gross cost of
environmental taxes, then the gross costs can be less than area A. However, the
MED R A
BMCs
MCp
Q1 Q2 Q
MB
22
analysis is based on first best framework. If there exist distortionary taxes, the
gross costs will be different from costs in the first best case.
The strong form of the double dividend hypothesis is defined as:
C(te, tL ) < 0 (2.2)
Equation 2.2 implies that gross cost is negative when the revenue from
environmental taxes is used to reduce distortionary taxes. The strong double
dividend hypothesis stimulated discussion on the negative “tax interaction
effects” of environmental taxes represented in the next section.
2.1.3 Tax Interaction Effects
The seminal literature on the double dividend failed to consider other kinds
of costs arising from pre‐existing distortionary taxes. Subsequent literature has
identified, the ‘tax interaction effect’, a negative dividend referring to the
decrease in labor supply due to distortions in polluting commodity markets.
When environmental taxes are imposed on polluting goods, relative prices of
polluting goods increase. Given that leisure is a substitute for polluting
commodities, the increase in prices of polluting goods leads to an increase in
leisure demand, in turn diminishing labor supply (Goulder et al., 1997; Kolstad,
2000; Schwartz and Repetto, 2000).
23
Tax interaction effects are found in early literature on the double dividend
hypothesis as well. Bovenberg‐de Mooij (1994) and Parry (1995) claimed that the
environmental tax generates distortions in the labor market by decreasing real
wage rates, which are affected by the increase in consumer prices. Also, they
pointed out that the environmental tax can disturb the consumption decision
between polluting goods and non‐polluting goods. These costs are jointly
referred to as the tax interaction effect and the aforementioned studies argue that
the tax interaction effect is larger than the tax recycling effect. The authors’
discussion addresses that the partial equilibrium analysis on the welfare effect of
environmental taxes (in figure 3.1) can be considerably misleading. Therefore,
general equilibrium analysis is more appropriate to capture the whole welfare
effects of environmental taxes if there are prior distortionary taxes.
Parry (1995) showed that the tax interaction effect dominates the tax
revenue recycling effect unless polluting goods are weak substitutes for leisure.
With this assumption, he argues that the optimal Pigouvian tax in a second‐best
economy should be less than the marginal environmental damage. When an
environmental tax is imposed on intermediate inputs and the tax interaction
effect is considered, the optimal tax ranges from 63% to 78% of the marginal
damage function (Parry, 1995).
24
The weak double‐dividend is generally accepted by environmental
economists (McCoy, 1997), but the strong double‐dividend hypothesis has been
contentious since there are contradictory findings in empirical as well as in
theoretical studies (Bovenberg, 1999). Supportive studies include Jorgenson and
Wilcoxen (1994) and Parry and Bento (2000), while studies with negative findings
include Shackleton et al. (1992), Shah and Larsen (1992), Goulder (1995), and
Parry (1997).
2.1.4 Non-Separability of Environmental Effects
In the fourth generation, research began to focus on the assumptions of the
utility function and other kinds of interaction effects. Schwartz and Repetto (2000)
address the “non‐separability” assumption of environmental effects on the utility
function. Given that environmental effects due to environmental taxes leads to
reduced medical cost, fewer sick days, and increased labor productivity, non‐
separability of environmental effects from the utility function affects the tax
interaction effects. The ‘non‐separability’ assumption implies that environmental
effects influence the marginal rate of substitution between leisure and goods,
which leads to the changes in labor supply. Schwartz and Repetto (2000)
conclude that if environmental effects increase the labor supply, the negative tax
interaction effect can be reduced or possibly even reversed. Williams (2002, 2003)
25
shows how non‐separable environmental effects (including reduced medical cost
and number of sick days and increased labor productivity) influence tax
interaction effects.
In summary, the welfare analysis of environmental taxes substituting for
distortionary taxes is conducted mainly to examine whether negative tax
interaction effects dominate positive welfare gains from environmental taxes in a
second best economy.
The subsequent section discusses empirical findings on the welfare
consequences of environmental taxes including the Pigouvian, tax revenue
recycling, and tax interaction effects. Most empirical work relies on a CGE
modeling approach, since CGE modeling can analyze economic interactions
among multiple institutions optimizing their behaviors. Each CGE model has
different assumptions and modeling structures, which affect the welfare
consequences significantly. For example, welfare consequences can vary
depending upon whether the CGE model assumes a first best economy or a
second best economy, and whether it uses highly aggregated industries or more
detailed industries.
Restrictions exist when the focus moves from closed economies to open
economies. In regional, open economies, the disequilibrium of wages and
environmental qualities among regions can cause inter‐regional labor migration,
26
which leads to different welfare outcomes than in a closed economy. This study
will highlight the impact of labor migration on the welfare consequences of
environmental taxes in an open economy.
2.2 CGE Models for Environmental Tax Analysis
Empirical studies of environmental taxes can be divided into two foci: one
on testing the double dividend hypothesis and the other on investigating the
economic impacts of environmental taxes. The former focuses more on
consumers’ welfare changes, while the latter focuses more on production and
trading sectors. This section explores how different CGE models have different
welfare outcomes and different economic impacts on production and trading
sectors. Also, summary of CGE models focuses on how environmental taxes are
levied, what are the main equations, and what are the main scenarios, welfare
consequences, and the overall economic impacts such as output, employment
and trading.
2.2.1 Test of the Double-Dividend Hypothesis
Goulder (1995) employs an inter‐temporal CGE model of the U.S.
economy focusing on how the nature of existing distortionary taxes influences
27
the welfare costs of carbon taxes. He constructs detailed tax systems, investment
incentives, equity values, profits, nonrenewable resource supply dynamics, and
capital adjustment dynamics. In particular, he addresses the transition from
conventional fossil fuel to synthetic fuels and its effects on the model. The energy
industries are coal, oil and gas, petroleum refining, synthetic fuels, electric
utilities, and gas utilities. Commodities are produced using CES production
functions. Labor, capital, energy composite, material composite, and current level
of investment are used as inputs. Import prices are exogenous and export
demand is a function of foreign export price and the level of foreign income. In
the simulation, Goulder uses different carbon tax rates such as $25.00, $50.00, and
$100.00 per ton of carbon emission, and different tax substitutions including
personal income adjustment, corporate tax replacement, and payroll tax
replacement. The welfare loss with recycling the tax revenue from carbon taxes
to reduce the personal income tax rate is about 36% less than lump‐sum revenue
replacement; the welfare loss is 37% less when recycling the revenue to reduce
the profit tax, 53% when recycling the revenue to reduce the payroll tax, and 42%
less when recycling the revenue to reduce all tax rates.
Bovenberg and Goulder’s paper (1997) examines whether gross costs of
environmentally motivated policy can be eliminated when the revenue from
environmental taxes is used to cut marginal income tax rates. They use two types
28
of environmental taxes: taxes on fossil fuels and taxes on gasoline. The base of
fuel taxes is intermediate input demand, and that of gasoline tax is consumption
of gasoline by consumers. Production is represented by a CES production
function using labor, capital, and polluting source as factor inputs. Ordinary
taxes include income taxes on labor and capital. The authors divide income tax
conditions into two cases; one in which only labor income taxes are imposed, and
the other in which both labor and capital income taxes are imposed.
They developed an inter‐temporal CGE model between 1990 and 2070.
Energy composite in the production function includes coal mining, crude oil and
natural gas, synthetic fuels, petroleum refining, electric utilities, and gas utilities.
Shale oils are regarded as synthetic fuels that are perfect substitutes for fossil
fuels. The main scenarios include four policies. First, a BTU tax per million BTUs
is imposed on oil, coal, and natural gas in proportion to the BTU contents of the
fossil fuels. A BTU tax rate of 0.45 was applied to imported fossil fuels as well as
domestic fossil fuels, but exported fossil fuels were exempted from the BTU tax.
Second, a gasoline tax was levied on the purchase of gasoline by consumers; the
gasoline tax rate was 0.692 per gallon. Third, marginal rates of personal income
taxes were increased, and fourth, marginal rates of corporate income taxes were
increased. The study’s main conclusion is that the substitution of environmental
taxes for ordinary taxes generates positive gross costs. The simulation shows that
29
the tax‐neutral environmental tax reform shifts the burden of taxation to a less
efficient factor, which results in the improvement of double dividend. This tax
burden shifting effect appears in the case of the gasoline consumption tax, but
not in the case of the BTU tax.
Goulder et al.’s research (1997) examines if there are tax revenue recycling
effects and if tax interaction effects dominate tax revenue recycling effects by
comparing pollution tax policy with grandfathered pollution quotas policy.
Revenues from pollution taxes are returned to the economy through cuts in the
marginal labor income tax rate, while grandfathered pollution quotas do not
generate government revenue. Therefore, pollution tax policy has environmental,
tax interaction, and tax revenue recycling effects, while pollution quotas policy
has only environmental and tax interaction effects.
Consumers’ utility function includes consumption of both electricity‐
intensive and non electricity‐intensive goods, demand for leisure, and separable
environmental quality. There are two intermediate inputs: general intermediate
input and electricity input. The use of electricity input generates sulfur dioxide
(SO2), and firms can reduce SO2 emissions by either decreasing the electricity
output level or purchasing abatement services or facilities. A CES production
function is employed and labor, electricity, and general inputs are used to
produce output.
30
The experiment shows that as pre‐existing tax rates increase, marginal
abatement costs increase, and the relative increased size is larger in emission
quota policy than in pollution tax policy. U.S. environmental regulation aims for
an annual reduction of about 10 million tons of SO2 from coal‐fired electric power
plants. The authors’ outcomes show that the costs of this regulation with pre‐
existing taxes are approximately 71% higher than those without pre‐existing
taxes; over 50% of these costs can be avoided if emission quotas are auctioned
rather than grandfathered.
Bruvoll and Ibenholt (1998) develop a dynamic CGE model for a small,
open Norway economy, referred to as DREAM (Dynamic Resource/Environment
Applied Model), with the assumption of inter‐temporal optimization. Ad‐
valorem taxes are levied on all intermediate inputs (raw material and all
processed material), which are equivalent to taxes on solid waste. They test if
recycling revenue from environmental taxes to cut labor taxes can offer a double
dividend. Labor supply and capital supply are endogenous. They assume that
reduced air pollution affects factor efficiency through less capital depreciation
and better health conditions. Welfare effects are calculated for changes in
conventional material consumption, leisure time, and environmental quality and
multi‐level CES production functions are used. Material and labor‐capital‐energy
composite combine into output and energy is a composite of oil and electricity.
31
In Bruvoll and Ibenholt’s model, consumption has multi‐level CES
functions as well. They assume there are three channels for environmental
feedback: health damage and labor productivity, material damage, and the direct
welfare effect from an improved environment. They observe two opposite effects
from simulating the CGE model considering environmental taxes with the
reduction of labor taxes as external shocks. Environmental taxes will increase the
production costs, while reduction of labor taxes will lower the production costs
and broaden tax bases. Main findings are that the environmental reform
increases production costs, decreases output, decreases labor demand – so there
is no double dividend of environmental improvement with increased
employment – and improves the overall environmental quality such as waste
generation and material deposition.
Parry and Bento (2000)10 investigated whether a double dividend can be
generated from substituting environmental taxes for labor income taxes. They
incorporate tax‐favored consumption goods such as medical care, housing, and
mortgage interest into a CGE model. CES utility and production functions are
employed to allow flexible substitutibility. Generally, the optimal Pigouvian tax
rates in a distortionary economy should be lower than those in the first best
10 Prior to this study, Shar and Larsen (1992) argued that subsidies to energy sectors in developing countries can be removed when the revenue from carbon taxes is used to cut conventional taxes.
32
economy since the negative tax interaction effects dominate the positive tax
recycling effects (Bovenberg and Goulder, 1996; Parry, 1995). But, in Parry and
Bento’s study, the authors show that efficiency gains from recycling the revenue
from environmental taxes can exceed the efficiency losses from tax interaction
effects when deductible goods such as housing, health care, and mortgage are
considered. Distortion from the subsidy on deductible goods can be reduced
when the consumption of tax‐favored goods is reduced due to the environmental
tax; this welfare gain is referred to as the ‘subsidy‐interaction effect’. For the tax
revenue recycling effect, if the revenue from the environmental tax can be used
to cut the non‐comprehensive tax, it reduces the subsidy for tax‐favored goods;
this is referred to as the ‘strong revenue recycling effect’. Main simulation results
show that marginal costs for the environmental tax ‐ with revenue recycling to
reduce non‐comprehensive labor tax‐ are the lowest among various marginal
costs, and at 13% of environmental taxes, the marginal costs are zero (Parry and
Bento, 2000).
2.2.2 Economic Impacts of Environmental Taxes
The DICE (Dynamic Integrated Climate‐Economy Model) model,
developed by Nordhaus (1993), derives the levels of investment in capital and
GHG reductions which maximizes global welfare. The author focuses on the
33
choices among current consumption, investment in capital, and GHG abatement,
and employs a Cobb‐Douglas production function of capital, labor, and
technology. Capital accumulation is determined by the optimization of
consumption over time.
Two key aspects of the DICE model are a climate damage function and a
GHG reduction cost function. The model supposes that an increase of 3°C in
global temperature leads to a decrease of 1.3% in world output, and the loss is an
increasing function of temperature in a quadratic form. For the emissions
reduction cost function, the model presumes that a 10% reduction in GHG
emissions from 1990 levels can be obtained with negligible cost, while a 50%
reduction will generate a 1% cut in global economic output. When GHG
emissions are cut by 20% again from a 1990 baseline, carbon taxes are $55.55 per
ton and net global costs are $762 billion. If the revenue from carbon taxes is used
to reduce other burdensome taxes, the optimal carbon taxes are $59.00 per ton of
carbon emission, and global annualized gains are around $200 billion (Nordhous,
1993). However, this model does not consider distortions in the world economy,
meaning that no tax interaction effects are included in the analysis.
Li and Rose’s study (1995) uses a static long‐run CGE model of
Pennsylvania assuming that revenue from carbon taxes is used to cut the
government deficit. Nested generalized Leontief cost functions and two‐stage
34
trade equations are applied to the CGE model. Carbon taxes are applied to
regional producers, and export and import prices are fixed. For sensitivity
analysis, different elasticity of substitution for the production and trade functions,
expanded government expenditure from the revenue of carbon taxes, and inter‐
regional labor mobility with a Keynesian closure rule are examined.
The model assumes full mobility across the industries of labor and capital.
For inter‐regional movement of factors, the authors take an endogenous
determination of labor mobility to adjusting to the classical closure rule. To
maintain 2000 emission levels, carbon taxes of $8.55 per ton are levied on the
outputs of coal, crude oil, and natural gas. The direct effect of carbon taxation is
increased fossil fuel prices, leading to substitution between fossil fuels as well as
substitution towards other energy sources. Real gross regional product (GRP)
decreases by 0.26% for maintaining year 2000 emission levels. For the case with
year 1990 emission levels with a carbon tax of $16.96 per ton, real GRP drops by
0.53%; the real GRP declines by 1.2% for a 20% reduction of year 2000 emission
levels. The simulation results show that the macro‐economic impacts of carbon
taxes are proportional to the level of carbon taxes.
Boyd et al.’s study (1995) derives the net benefit of energy taxation to
determine the optimal energy tax rate, the efficient level of energy conservation,
and the proper abatement level of carbon dioxide emissions. Factor inputs
35
include labor, capital, and land. The prices of traded goods are endogenous
given that the U.S. economy is large enough to affect prices of world trade.
Production and utility are described as CES functions. Energy taxes are levied on
coal, oil, and gas in proportion to the fuels’ respective carbon contents. The
energy taxes are converted into ad valorem taxes relative to prices of fossil fuels,
and they are applied to domestic energy producers as well as energy importers.
The main scenario supposes that the government increases the revenue
from environmental taxation as non‐distortionary lump‐sum taxes. Coal tax rates
are the highest; oil tax rates are 53% of the coal tax level; and natural gas tax rates
are 26% of the coal tax level. Energy taxes are applied to domestic energy
producers as well as energy importers, and the environmental benefit function is
linear with the reduction of carbon dioxide emissions. Welfare cost is estimated
from a CGE model for the United States in 1988 and the net environmental
benefit is calculated from the gross environmental benefits and welfare costs. The
simulation results show that the coal price should increase 20% more than the
baseline price, and that oil and natural gas prices should be about 10% and 5%
higher, respectively, than the benchmark prices. Depending on the
substitutability and reduction levels, 20% ‐ 50% of baseline emission levels can be
reduced without generating a net loss of national welfare.
36
A study by Böhringer and Rutherford (1997) shows that tax exemptions
on export‐and energy‐intensive sectors to maintain price competitiveness can be
more costly than sector‐specific wage subsidies when unilateral carbon taxes are
imposed on carbon dioxide emissions from the use of fossil fuels in domestic
production and consumption. Carbon taxes are not applied to imported and
exported goods. A static CGE model for West Germany as an open economy
with 58 sectors in 1990 is employed to derive the welfare costs of tax exemption.
Various revenue‐neutral policies including lump‐sum, labor tax reduction, and
capital tax reduction are combined with the three scenarios (unilateral carbon
taxes, carbon taxes with exemption, and uniform carbon taxes with wage
subsidies). The excess burden of tax exemption calculated by Hicksian‐
equivalent variations is raised by approximately 20% per 30% reduction in
carbon emissions. An effective general equilibrium wage premium index shows
that uniform carbon taxes with wage subsidies on exemption sectors gain more
employment than an exemption policy with carbon taxes. For the export side,
uniform carbon taxes with wage subsidies cause less of a decrease in exports
than uniform carbon taxes or an exemption policy.
Kamat et al. (1999) explored the welfare impact of a global carbon tax in
the U.S. Susquehanna River Basin. A key feature in their analysis is that global
carbon taxes change the prices of imported and exported fossil fuels as well as
37
prices of other imported and exported goods in the focus region. They mention
price differentials for the other regions and other countries as well due to the
different combinations of fuel usages and technologies. Carbon taxes on the
consumption of fossil fuels are converted into ad valorem taxes on composite
consumption (intra‐regional sales and imports) of fossil fuels including coal and
oil/gas sectors. The other case assumes ad valorem taxes on the production of
fossil fuels, and the taxes are specified as increases in indirect business tax rates
in the fossil fuel sectors.
To maintain year 2000 emission levels, real gross regional product (GRP)
for the consumption tax base decreases by 0.03%, while GRP for the production
tax base decreases by 0.04%. For the second scenario of maintaining 1990
emission levels, GRP for the consumption tax base decreases by 0.11%, while
GRP for the production tax base decreases by 0.14%. As a whole, regional exports
are affected positively, while regional imports, foreign imports and exports are
affected negatively by the global carbon tax. The literature review by Kamat et al.
(1999) found that carbon taxes targeting 50% reduction of carbon emission leads
to a 1% to 3% reduction in real GDP.
Andre et al. (2003) develop a regional CGE model for Andalusia, Spain to
examine the economic effects of the imposition of carbon taxes on carbon dioxide
and sulfur dioxide emissions. They simulate scenarios such that revenues from
38
carbon taxes are used to reduce either income taxes or payroll taxes with revenue
neutrality. When revenue from carbon taxes is recycled to reduce payroll taxes,
emissions and the unemployment rate decrease monotonically with the carbon
tax rates. When revenue from carbon taxes are used to cut income tax rates, no
double dividend is generated; all the economic variables including
unemployment are adversely affected.
To sum up the empirical studies on environmental taxation, CGE models
are developed to analyze the welfare consequences of environmental taxation
including debate on the double dividend hypothesis (Goulder, 1995; Bovenberg
and Goulder, 1997; Goulder et al., 1997; Parry and Bento, 1999). Also, the other
CGE models have examined the economic impact of environmental taxes such as
changes in output, consumption, employment, and trade (Nordhaus, 1993; Boyd
et al., 1995). All these models are based on a second best, closed economy. On the
other hand, some CGE models for open economies discuss the welfare
consequences of environmental taxes such as efficiency costs, tax revenue
recycling, and tax interaction effects (Bohringer and Rutherford, 1997; Bruvoll
and Ibenholt, 1998). These models investigate economic impacts of
environmental taxes such as employment, output, demand, and trade (Bruvoll
and Ibenholt, 1998; Kamat et al., 1999; Andre et al., 2003). The aforementioned
studies, however, do not include inter‐regional mobility of labor in the CGE
39
models. One study by Li and Rose (1995) shows the economic impact of carbon
taxes using a regional static CGE model including labor mobility between
regions, but as table 2.1 shows, no CGE research was found to examine both the
welfare consequences and economic impacts of environmental taxes with
migration effects at state‐level open economies.
Table 2.1 Classification of CGE Models on Environmental Taxation Welfare Consequences
and Double Dividend
Production and Macro-
Economic Changes
Closed economy
Goulder (1995)
Goulder et al. (1997)
Bovenberg and Goulder (1997)
Parry and Bento (2000)
Nordhous (1993)
Boyd et al. (1995)
Inter-regional
mobility
No study found Li and Rose (1995)
Open
economy
No inter-regional
mobility
Bohringer and Rutherford
(1997)
Bruvoll and Ibenholt (1998)
Andre et al. (2003)
Kamat et al. (1999)
Bruvoll and Ibenholt (1998)
2.3 Partial Equilibrium Analysis on the Trade Effect
Most studies mentioned in this chapter do not consider inter‐regional and
foreign trade; exceptions are Böhringer and Rutherford (1997) and Kamat et al.
40
(1999). The impact of the environmental tax on the output and trade can be
affected significantly by different assumptions on the prices of traded goods.
Partial equilibrium analysis will show how production and trade will be affected
by the environmental tax relying on different assumptions on the prices of traded
goods.
Suppose there is an energy‐intensive good, and it is exported to other
countries. When an environmental tax (Ts) is imposed on the energy‐intensive
good, it will increase the export price of the energy‐intensive good ( 'ee PP → ),
assuming that the world demand price of the good ( tP ) in the export market is
constant. The supply curve of the good in the export market shifts upward due to
the environmental tax. Then, tP = eP + Ts. This implies that even if the world
demand price of the good in the export market is fixed at Pt, the price that the
industry exporting the energy‐intensive good receives will be less than before the
imposition of the environmental tax; therefore, when the environmental tax is
imposed on the energy‐intensive good, the supply of that good in the export
market decreases.
On the other hand, if the environmental tax is extended to import demand
for the energy‐intensive good, the import demand for the energy‐intensive good
will be affected by the environmental tax. But this study only assumes the export
supply price is affected by the environmental tax.
41
The general equilibrium approach may have different outcomes than those
of the partial equilibrium approach, since foreign and inter‐regional exports and
imports are affected by substitution among domestic and regional products,
changes in export and import prices, domestic prices, and cost shares. Therefore,
partial equilibrium analysis cannot capture direct and indirect effects of the
environmental tax on the traded sector.
2.4 Labor Migration
This section will summarize theoretical analyses and empirical research
on labor migration. Among the various determinants of labor migration, the
major focus here will be on wage and environmental amenity differentials.
Throughout the review of the theoretical analyses on labor migration, how the
wage differential affects labor mobility and the interaction with other
determinants will be explained in a general equilibrium framework. The review
on empirical studies will focus more on the environmental amenity differential
as a determinant of labor mobility, and what kind of variables are identified as
environmental amenities will be summarized.
42
2.4.1 Labor Migration
Although there have been extensive studies on labor migration, major
focuses here will be on the role of wage and natural amenity variables in the
decision of labor migration. Roback (1982) examines the effects of wages and
rents in allocating workers to locations with different amenities. In her study,
amenity is defined as clear days and low population density, while disamenity is
defined as crime, pollution, and cold weather. She employs a simple general
equilibrium model where capital and labor are completely mobile across cities,
while land is a fixed factor input. Workers with identical skills and tastes
consume a composite commodity (x) and residential land (lc) given that each city
has different amenity levels, s. Factor prices of labor and land are w and r. Based
on utility maximization subject to budget constraints, the market equilibrium
condition for workers is given by an indirect utility function with 0/ >∂∂ sV in
equation (2.3).
ksrwV =);,( (2.3)
Firms produce composite good x using land and labor with a constant
returns to scale production function. The equilibrium condition for firms is that
the unit cost of production should equal the product price.
1);,( =srwC (2.4)
43
0<sC if amenity is unproductive, while 0>sC if amenity is productive.
The equilibrium level of w and r are determined by equations (2.3) and (2.4).
Major results from the general equilibrium model are described in equations (2.5)
and (2.6).
0)(1<+−
∆= rsrs VCCV
dsdw (2.5)
0)(1f
pswsw VCCV
dsdr
+−∆
= (2.6)
As a city has more amenity, the wages should be lowered (equation 2.5),
while the rent of residential land is ambiguous for more amenable cities in
equation (2.6). From the general equilibrium discussion and numerical examples,
Roback concludes that the value of amenity is reflected in the wage as well as the
rent gradient of land.
Wildasin (1994) uses a two‐region equilibrium model for migration to
analyze the effect of an income redistribution policy on migration. This literature
review is confined to the basic model, since its major purpose is to address the
role of wage or amenity variables in the decision of migration. Wildasin assumes
two regions (or countries), 1 and 2, one produced good, two factor inputs,
immobile land and mobile labor. Immobile households such as landowners
receive returns to the fixed input, and mobile workers get the returns to the
mobile input. The number of mobile workers in region i is Ni. With labor
44
migration, the number of employed workers is Li. Li‐Ni implies the amount of
immigration into region i. The condition for the equilibrium allocation of labor is
NNNLL ≡+=+ 2121 (2.7)
In the initial stage, wages of both regions are different ( 02
01 ww ⟩ ) assuming
that there are migration costs. With free mobility, workers will flow into region 1,
leading to equilibrium status with eL1 and ew (Figure 2.3).
Figure 2.3 Two Region Equilibrium Model
Dickie and Gerking’s 1998 study focuses on the main reasons of continuity
of inter‐regional wage differentials for a long time period. Based on Roback’s
paper (1982 and 1988), they extend Roback’s model in two ways: first, each
region has a different amount of land and a distinct amenity. Second, workers
01w
ew
L1 L2 N
ew
02w
N1 eL1
45
are imperfectly mobile. The basic general equilibrium model in Dickie and
Gerking’s study assume that two regions produce different composite
commodities (Xa, and Xb) using labor (H) and land (L). Unit cost equations are
PtnrwC AAAAA =),;,( (2.8)
1),;,( =BBBBB tnrwC (2.9)
Where wi denotes wages of labor, ri denotes the rent per unit of land, P
denotes producer price of XA relative to the producer price of XB, ti denotes
technical progress, and ni denotes inter‐regionally differentiated natural
amenities. Relying on constant returns to scale, land is fully allocated between
industrial land and residential land, and workers are fully employed.
iiiiir LHXc =+ϕ i=A, B (2.10)
iiiw HXc = i=A, B (2.11)
Where jcc
iij ∂
∂= (j = w,r) denotes use of a factor for producing one unit
of goods, iϕ denotes residential use of land, and Hi denotes the level of labor.
HHH BA =+ (2.12)
The demand side of Dickie and Gerking’s model has specific assumptions.
First, workers in region i may receive a lump‐sum transfer from the government.
Second, relocation costs may accrue for labor migration, which leads to different
utility levels. Third, the initial utility level of region A is supposed to be larger
46
than that of region B, so the wage rate in region A is higher than the wage rate in
region B. Fourth, there are transportation costs in trading commodities. Based on
the equilibrium conditions of trade between regions, they take comparative static
effects of production‐cost differences, amenities, relocation costs, and inter‐
regional transfer payments on the wage gap. In the Dickie and Gerking’s study,
the key factor of labor migration is the wage difference between regions.
With the role of the wage differential in determining labor migration,
natural amenity is another important factor in labor migration. The next section
discusses empirical studies on evidence on the natural amenity as an important
determinant of labor migration.
2.4.2 Empirical Studies on Amenities and Migration
In Judson et al (1999), environmental amenity is defined as
“any attribute of a geographic location for which a resident or potential migrant would be willing to pay, either through higher housing costs, lower wages, or other location‐specific costs, but for which there is no market through which the individual can directly purchase a given amount of that good.”
There have been numerous empirical studies regarding the effect of
amenities on labor migration. Cromartie and Wardwell (1999) argue that the
main pull factor of net in migration is natural amenity as defined by the USDA
(US Department of Agriculture) ERS (Economic Research Service). Rudzitis
47
(1999) addresses that wage and employment opportunity cannot totally explain
migration to the rural West. He uses outdoor recreation, landscape, scenery,
environment, and climate as amenity attributes. Compared to other pull factors
of migration to the rural West, these amenity variables have higher scores when
people are asked about the reasons of migration. Vias’s (1999) study supports
the assumption that high quality‐workers who prefer to live in amenity‐rich
locations cause service‐based firms to locate in amenity‐abundant counties.
Judson et al. (1999) surveyed the immigrants of Oregon according to different life
cycles. They found that retirees referenced high amenity as a main reason of
moving while young wage workers did not reveal strong preferences for amenity.
Deller et al. (2001) assume there are four factors affecting regional
economic growth: markets, labor, government, and amenity attributes. The
market variable has sub groups according to different race, age, and income
groups while the labor variable is categorized to capture different education
levels, medical services, unemployment rates, and crime rates. The government
variable represents property tax rates and total government general expenditures.
The empirical analysis consists of principal component analysis and
reduced form regression. From the principal component analysis, Deller et al.
extracted main factors that explain each amenity category. For example, in the
climate category, average temperature and annual precipitation, January
48
temperature, and July humidity components have higher values. Using the
information from principal component analysis, they performed reduced form
regression on the regional growth model. There are three dependent variables:
changes in population, changes in employment and changes in per capita income.
Thus, there are three simultaneous equations.
Independent variables are population, employment, percent of nonwhite,
percent under seventeen, percent above sixty‐five, income distribution, percent
of households under poverty line, unemployment rate, education, crime rate,
number of physicians, property tax, government expenditure, climate, developed
recreational infrastructure, land, water, and winter variable. Growth rate and
percentage of over sixty five population have a strong negative relationship with
each other. The amenity attributes are most important in the explanation of
regional growth. For example, climate has a strong effect on growth in
population, but no significant effect on employment growth. Higher level of
water amenity is associated with higher population growth, but not significantly
associated with job growth. Developed recreational infrastructure is positively
related with population, employment, and job growth rates. Land and winter
attributes also have significant effects on population, employment, and job
growth.
49
Lewis et al. (2002) examined the effects of public forest conservation land
on employment growth and net migration in the northern forest regions. Their
empirical tests show that since migration due to amenity is generated by
multiple land uses, policy makers should not regard conservation of forest or
public lands as persuasive policy for economic development.
McCool and Kruger’s paper (2003) is policy‐ and management‐oriented in
the sense that they attempt to find main reasons for the increase of in‐migration
to the Pacific Northwest area and propose policies to increase populations in the
rural counties. Ninety‐four of 104 counties in the interior Columbia basin
experienced population increases between 1990 and 1994. They observe that the
main pull factor of population increases is natural resource based amenity.
Counties with increased populations have higher expenditures on amusement,
recreation, and lodging. They discuss the gains and costs of population changes
in the counties. Population changes mainly due to in‐ and out‐migration have
positive effects such as more employment opportunities, increased expenditures,
and growing tax bases. However, the increased population will lead to the
changes in ethnic and racial characteristics of communities, and this change may
affect the interactions of community with natural resources.
McCool and Kruger categorize the components of this interaction into
four factors: population growth driver, growth consequence, and social and
50
psychological links with natural resource and measurement issues. As a
population growth driver, they propose first that amenity becomes more
important as a fundamental factor of in‐migration to rural areas. In the past,
suburban areas were attractive places to live since they satisfied the needs for
natural amenity as well as urban facility. But as telecommunications,
transportation, transfer payments and internet develop rapidly, rural areas can
provide both of these needs, so people will move into the rural area rather than a
suburban area. Second, public lands provide environmental based amenity as a
pull factor of in‐migration, but this should be harmonized with other demands
such as highways, airport, high speed internet and cellular phones. Third,
‘hidden’ people who are employed in the agricultural sector or natural resource
related fields may have different interaction with the natural environment and
different demands for public services.
McCool and Kruger suggest several possible growth consequences of
population increases in the rural area. As population increases, the communities
will require more housing, schools, and other infrastructure which may lead to
the destruction of natural resources such as forest and wildlife habitat. New
landowners will have management approaches different from those of the old
residents and lifestyles of migrants will be different from those of prior residents,
so they will create new and various demands for recreation and public lands.
51
As social and psychological aspects of populations change, migrants’
culture will be different from the current residents’ culture. Many of the new
residents may move from a metropolitan area and this urban culture will have
different features than rural culture. Regarding the valuation of natural
environments, new people may put higher value on aesthetic and recreational
aspects while old residents may regard forests as their working places. To the old
residents, places have special relationships with them in terms of friendship
networks and family links for which small rural society is known, while new
residents will have less personal relationships with public places. Sometimes,
new residents from urban areas may view the natural amenity as symbolic while
old residents view it as functional.
The final component of the examination of population change and
natural amenity is measurement issues. The authors raise questions about
appropriate temporal and spatial scales and the application of new geographical
analysis.
Hunter et al.’s (2003) paper is unique in that the authors use disamenity
factors to explain migration. The environmentally hazardous facilities are push
factors of migration and it is said that there exists racial and income
differentiation in avoiding environmental hazards. Put another way, racial
minority and low income groups tend to have higher probability of living within
52
the proximity of an environmental hazard. They test if socio‐economically
disadvantaged populations such as non‐white or poor groups are less responsive
to avoid environmentally hazardous facilities as a location‐specific disamenity.
They use four different dependent variables: white, black, Asian, and Hispanic
out‐migrants, for a nation wide, county level dataset mostly between 1980 and
1990. As explanatory variables, they include population, economic, housing,
geographic characteristics, environmental amenities, environmentally hazardous
facility indicators, proposed Superfund sites, and hazardous waste large quantity
generators.
The results show that among 44 risk coefficients, only 5 coefficients have
statistical significance, and no clear patterns appear regarding to the direction of
the coefficients. Besides, there are no statistical differences between white and
non‐white residents on out‐migration in counties with environmentally
hazardous facilities, so this outcome does not support the proposed hypothesis.
Garber‐Yonts (2004) synthesizes diverse fields of study in order to identify
relationships among the natural amenity, migration, and federal land
management. His literature review covers demographic studies, urban and
regional economics and non market valuation, land use change, and economics
of forest preservation and wilderness designation. The main finding of the
review is that natural amenity is a powerful factor that explains migration flow,
53
especially with increasing mobility and the aging U.S. population. But the effect
of natural amenity on wage, housing cost, and employment is ambiguous. As
implication of the review is that natural amenity is static and changes slowly,
while the tastes and preference of people on the location change rapidly.
In Stewart’s (2004) paper, people in‐migrated to rural counties in the 1970s
but the trend declined in the 1980s, and increased again in the 1990s. He analyzes
the main factors and social contexts that influence amenity migration. First of all,
retirees prefer amenity‐rich counties, and new financial tools facilitate those
retirees’ migration to high amenity counties. Second, improvements in
transportation, telecommunication, and computer technology allows people to
work at home more readily; in the near future, people may live in the rural
county with high natural amenity and work in the urban county through internet
or cyber meeting. Third, once people visit some amenity area for the first time,
they may want to visit again. They may return to visit that place and rent cottage.
Now, as they choose to stay longer or visit more often, they will buy a second
home, and at last they will migrate to the amenity area. In this case, tourism
affects people’s decisions on migration.
As consequences of migration, Stewart points out six aspects. First, a rural
community with in‐migration will face a variety of cultures, races, and
conventions, so different people will have different expectation and needs.
54
Second, the community will have social conflict and turbulence. For example,
migrant people may not be cooperative in community decision making or public
expenditure. Third, as a positive effect, mailbox economies will grow. For
example, retirees’ main income is from external sources and this external income
will fund the community economy. Fourth, as more people live in the rural
community, they will require more land for housing and facilities. This may lead
to the reduction of forest area and destruction of natural environment through
air and water pollution. Fifth, therefore, natural resource management will have
to adjust to the changes arising from migration effects. Sixth, the increased
population will require more infrastructure to support the community.
Dissart and Marcouiller (2004) distinguish between natural amenity and
built amenity. They attempt to show the effect of recreation facilities on economic
growth in remote rural regions. Therefore, this paper is different from the
aforementioned literature in that it focuses on the effect of built amenity on
migration and regional economic growth. They use nation‐wide remote rural
county data on natural amenity, outdoor recreation facilities, and economic
growth between 1989 and 1999. The analysis consists of clustering and regression.
The dependent variable is income and independent variables are outdoor
recreation facilities and other control variables including natural amenities,
tourism budget, college education, proportion of population over sixty five,
55
population growth, public ownership of resources, and interstate mileage
density. Using clustering analysis, they divide rural counties into six categories
from negatively scored counties on natural amenity to counties with mountain,
forest, below average temperature, wildlife resource, and wetlands. Within each
category, they regress the income variable on outdoor recreation facilities and
other control variables. The overall results support the hypothesis that outdoor
recreation facilities are positively related with economic development, though
the relationship varies with clusters. The policy implication of this paper is that if
a remote rural county has abundant natural amenity, that county ought to invest
in outdoor recreation facilities to attract migrants, which will boost the region’s
economic growth.
Krupka (2004) addresses location‐specific human capital as an
opportunity cost of migration. If a person learns location‐specific skills, it will be
hard to get a job in the other region where that skill is not used or a different skill
is required. So, individuals will move if the difference between the net income of
staying and that of moving relying on region‐specific human capital is negative.
He uses data from interviews with 8,033 people in 1979 and 2000. For the
regression model, he uses level of amenity at the terminal date as a dependent
variable and individual characteristics and amenity level at the original location
as explanatory variables. The results of 2SLS (two stage least squares) regression
56
for six different amenities show that the initial county characteristics (exposure
variable) are highly significant. He concludes that people have different
valuations of local amenity since they have various kinds of location‐specific
human education which determines people’s tastes and amenity preferences.
In summary, numerous theoretical as well as empirical studies on labor
mobility show that wage and environmental amenities are important
determinants of migration, and environmental amenity variables can be defined
in various ways.
2.5 Partial Equilibrium Analysis on the Environmental Tax and
Labor Mobility
A survey of studies on labor migration shows the importance of wage and
amenity variables as determinants of labor mobility. However, the final purpose
of studies on labor mobility is to explore the impact of labor mobility on welfare
consequences of environmental taxes. This section briefly discusses how labor
mobility affects the welfare consequences of environmental taxes through labor
markets depending on different environmental tax regimes.
The equalization of factor prices leads to no inter‐regional factor mobility
given that Heckscher‐Ohlin conditions are satisfied. The conditions are i)
unequal relative factor endowments, ii) perfect competition in all markets, iii)
57
inter‐regionally identical constant return to scale of production technology for
each good, iv) no distortions in the regions, v) identical and homothetic
preferences of consumers, vi) no transaction (i.e., transportation) cost, and vii) no
factor intensity reversals (Batra, 1973). But if one region imposes environmental
taxes and reduces factor income taxes for tax revenue neutrality, then the
environmental amenities and real factor prices in the focus region will change.
Consequently, labor and other factors will move inter‐regionally until factor
prices in all regions are equalized. In migration studies reviewed so far, wage
differential and natural amenity differential are treated as important factors to
determine an individual’s migration decision.
Inter‐regional labor mobility can affect the welfare consequences of
environmental taxes in various ways. First, labor market distortions due to labor
income taxes (tL) are smaller in labor markets with labor migration (case II) than
in labor markets without mobility (case I). Based on partial equilibrium analysis
in figure 2.4, regional labor supply (Ls) without migration (mig) has a steeper
curve since the labor supply without migration is less elastic than that with
migration. Given that labor income taxes are levied, dead weight losses (∆ABD)
without labor mobility are bigger than those (∆ABC) with labor migration.
58
Figure 2.4 Labor Migration Effects on the Deadweight Loss
Secondly, the improvement in environmental quality due to state
environmental taxes will encourage in‐migration of labor in the other regions.
Federal environmental taxes will not change the relative environmental tax, since
all the regions will have a similar improvement in the environmental amenity.
The labor supply with an environmental quality differential as well as a wage
differential (the dotted lines) (case III) will be more elastic than the labor supply
curve where only real wage differential affects labor migration. Thus, the labor
market with environmental amenity and real wage variables will have smaller
L
W
Ld
Ls + tL
Ls Ls +mig
Ls +mig+tL
A
C
B
D
E
Case ICase II
Case III
59
distortions (∆ABE) than the labor market with only real wage variable. However,
there will be negative impacts on the location decision of pollution‐intensive
industries due to the reinforced environmental regulation (Pagoulatos et al.,
2004). In this study, this effect will be ignored since evidence shows the effect is
contentious (Dean, 1992; Jaffe et al., 1995; Levinson, 1996; Brunnermeier and
Levinson, 2004; Thomas and Ong, 2004; Raspiller and Riedinger, 2004).
To sum up, distortions in labor markets due to labor income taxes are the
largest in case I, and next in case II (federal environmental tax) followed by case
III (state environmental tax).
ABEABCABD ∆>∆>∆ (2.13)
Third, from the perspective of general equilibrium analysis, the tax recycling
and tax interaction effects will be amplified by labor migration. For the tax
revenue recycling effect, the reduction of labor income taxes for tax neutrality
given that environmental taxes are imposed will increase real wage rates. The
increase in real wage rates will not only increase labor supply within a region,
but labor in the other regions also will in‐migrate until the real wage rate is
equalized among regions. Labor migration affects the tax interaction effect as
well: as the price of polluting goods goes up due to the environmental tax, the
real wage rate will be reduced. Labor supply will be diminished and move out to
60
other regions with higher real wage rates. Again, the general equilibrium
approach can capture the whole impact of labor migration that the partial
equilibrium analysis cannot.
2.6 Other Theoretical Considerations
Other theoretical aspects to be considered in this review include possible
influences on welfare consequences when a backward bending labor supply
curve is assumed, long run effects of substituting state government revenues
from carbon taxes for the revenues from conventional taxes are considered, and
the possible economic effects due to the environmental tax and reduced labor
income tax are decomposed.
2.6.1 Potential Effects of Backward Bending Labor Supply Curve
Most CGE models assume positive own wage elasticity of labor supply,
implying that an increase in the real wage stimulates the labor supply
(Bovenberg and de Mooij, 1994). Empirical studies, however, show that the labor
supply elasticity can be negative, called the ‘backward bending’ labor supply
curve (Ballard et al., 1985). If the own after‐tax wage elasticity of labor supply is
negative, the welfare consequences will be adversely affected.
61
Wage elasticity of labor supply is derived by the relative magnitude of the
substitution and income effects. By substitution effect, as the price of leisure
increases, individuals reduce the amount of time allocated to leisure and increase
the amount of labor supplied. By income effect, as the wage increase,
individuals’ incomes increase. So, as long as leisure is a normal good, individuals
spend more time on leisure and reduce labor supply. In the upward sloping
labor supply curve, the substitution effect dominates the income effect; in other
words, the own wage elasticity of labor supply is positive. But in the downward
sloping labor supply curve, the income effect exceeds the substitution effect, so
the own wage elasticity of labor supply is negative.
Given that a backward bending labor supply curve is assumed, the welfare
consequences of environmental taxes can change in various ways. First of all, the
tax revenue recycling effect will be attenuated. With the assumption of positive
own wage elasticity, the decrease in labor income tax due to tax revenue
recycling raises the net real wage, leading to an increase in labor supply. But the
negative elasticity of labor supply results in the reduction of labor supply,
therefore the recycling of tax revenue dampens the welfare cost of environmental
taxes. Second, the tax interaction effect will be mitigated. The increase in the
price of dirty goods reduces the net real wage. So, with the backward bending
labor supply, a decrease in the net real wage increases the labor supply.
62
On the other hand, when the backward bending labor supply is applied to
all regions, labor migration will influence tax recycling and tax interaction effects
in different ways. The decrease in labor income tax due to the tax revenue
recycling will increase real wage but the labor will out‐migrate since negative
own wage elasticity is assumed, so the labor supply will decrease more. For the
tax interaction effects, the decrease in real wage due to the increase in the price of
dirty goods will lead to an increase in in‐migration.
However, there can be multi‐equilibrium when a backward bending labor
supply curve is assumed. Therefore, this study will not examine the case with the
backward bending labor supply.
2.6.2 Long Run Effects of the Environmental Tax Recycling Policy
In the long run, the flow of revenue from carbon taxes can be unstable. In
figure 2.5, carbon taxes reduce consumption of carbon‐emitting goods and this
effect encourages technological innovation. Firms will change from carbon‐based
fuels to alternative fuels. Accordingly, the carbon tax base will decline in the long
run, which leads to reduced welfare gains from tax recycling. As one of the state
government tax revenues, the carbon tax is an unstable source compared to other
conventional taxes such as labor income taxes, sales taxes, property taxes, and
corporate income taxes.
63
Figure 2.5 Feedback of Environmental Taxes in the Long Run
Resource depletion
Environmental pollution + climate change
Production cost (+)
Consumer price (+)
Consumption (-)
Profit (-)
Innovation
Environmental tax
Alternative fuels
(-)
64
CHAPTER 3
THE PENNSYLVANIA CGE MODEL
A static Pennsylvania CGE model is constructed for the year 2000. There are
17 industrial sectors and production is modeled using nested CES functions.
Primary factor inputs are labor, proprietors, and capital. Intermediate inputs
include energy composite, transportation and other materials; energy composite
consists of coal, oil, gas, alternative fuels, and electricity. Final demand is
modeled using a CES utility function and trade is modeled using two‐tier
Armington and CET functions. Labor migration is modeled as a function of
relative real wages and environmental amenities.
3.1 Main Features of CGE Model
Pennsylvania’s economic institutions are divided into a representative
household, 17 industries, federal and state/local non‐educational and educational
government, and rest of world (ROW) and rest of USA (ROUS). All flows of
payments or transfers among the entities of institutions are based on a SAM of
the Pennsylvania economy in the year 2000 and energy consumption data from
65
the EIA (Energy Information Administration). The SAM, which provides a
comprehensive snapshot of the economy during a given year (Decaluwe, et al.,
1999), was derived from IMPLAN (Impact Analysis for PLANning) for the year
2000 (IMPLAN Pro, 2000). The SAM with energy consumption data has two
different units: value (million dollars) and quantity (trillion Btu). The input‐
output transaction included in this transformed SAM is referred to as a ‘hybrid
commodity by commodity input‐output table’ (Miller and Blair, 1985; Brenkert et
al., 2004). This hybrid SAM was recalculated to transform the quantity unit into
value unit12.
There are 17 sectors for the production and consumption of goods in this
model: coal, oil, gas, alternative fuels, electricity, transportation, agriculture,
mining, construction, durable manufacturing, non‐durable manufacturing, trade,
utility, electric and gas utility, FIRE, services, and others. Energy production
sectors are coal, petroleum, natural gas, alternative fuels, and electricity. Table
3.1 describes the sub‐sectors within 7 main industrial sectors.
12 More details are explained in Appendix B.
66
Table 3.1 Description of Industrial Sectors Industry Description
Coal* Coking coal and steam coal
Oil*
Asphalt and road oil, aviation gasoline, distillate fuel,
jet fuel, kerosene, LPG, lubricants, motor gasoline,
and residual fuel
Natural gas* Natural gas, Natural gas liquid
Alternative fuels* Nuclear, hydropower, solar, fuel cell, geothermal,
bio-fuel and wind
Materials**
Agriculture, Mining, Construction, Electric and gas
utility, Utility, Durable and non-durable
manufacturing, Trade, Financial, insurance, real
estate, services, and others
Transportation**
Railroad and related service, Local, interurban
passenger, Motor freight transport, Water transport,
Air transport, Transportation service, Local
government passenger
Electricity** Electric services, State and local electric utility,
Federal electric utility
(Source: * State energy data 2001 in EIA, **Type codes (SA092) in IMPLAN)
Government sectors consist of federal government, state and local‐non
education government, and state and local‐education government in order to
look at the different effects on revenue and expenditures among federal, state
and local government with and without expenditure on education.
Production and consumption behavior is captured by the first order
optimality conditions derived from profit maximization and utility maximization
subject to budget constraints. Value added inputs are labor, proprietary services,
67
capital, and energy, and they are substitutable in a nested CES (constant
elasticity of substitution) function. Intermediate inputs are used to produce
commodities using a CES production function while household utility consists of
a nested CES function. In the first tier CES utility function, household utility is a
function of demand for leisure and a composite commodity. In the second tier
CES utility function, the demand for the composite commodity is composed of
consumption of market commodities.
There are several important features in the CGE model to examine the effects
of state‐specific conditions on the welfare consequences of carbon taxes, the first
of which is the endogenization of labor migration. Labor migration is a function
of the after‐tax real wage differential and the environmental amenities
differential. The real wage is affected by the market price index as well as labor
income taxes. Hence, changes in the price index or labor income taxes in the
region will cause in‐migration or out‐migration through the changes in the real
wage. Meanwhile the increased environmental amenities due to the
environmental tax will also boost in‐migration. The total changes of prices, taxes,
and environmental quality will determine the sign and the size of labor
migration.
The second important feature in the CGE model is that the trading sector is
divided into two levels; all other countries and the rest of the states in the U.S.
68
Nested Armington and Constant Elasticity of Transformation (CET) functions are
used to allow a cross‐hauling assumption implying imperfect substitution
between tradable goods and non‐tradable goods. In the first tier, foreign imports
to the rest of the other countries and domestic demands are derived from the
Armington function. The domestic demands are allocated between domestic
import by the rest of the states in the U.S., and the regional demands by the
second tier Armington function. The derived domestic imports are a function of
domestic import price, regional price, and the elasticity of substitution between
domestic import and regional supply. Foreign and domestic exports are derived
in the same way using the nested CET function. Output supply is allocated
between foreign export and domestic supply by the first level of CET function.
At the second stage, the domestic supply is allocated between domestic export
and regional supply by the second CET function.
Other activities such as tax payment, savings, remittance, and borrowing are
assumed to be proportional to total control. Most parameters, including shift and
share parameters in the production, utility, Armington and CET functions, and
various tax rates are calibrated by using IMPLAN SAM data in order to
reproduce the benchmark solution (Ballard et al., 1985).
The model is solved by a competitive set of prices that satisfies the
condition of zero excess demand. One of the properties in the excess demand
69
function is Walras’ law such that there exist (n‐1) independent excess demand
equations to determine (n‐1) relative price ratios (Dervis et al., 1982). The
equilibrium conditions consist of a commodity market, a factor market, and an
aggregate saving‐investment market. When supply of goods is equal to demand
of goods and supply of factors is equal to demand of factors in equilibrium
conditions, the last saving‐investment market equilibrium condition is satisfied
automatically by Walras’ law (Robinson et al., 1990). Finally, the objective
function, such as the sum of households’ consumption of commodity, is
maximized depending on all the non‐linear equations in the CGE model in order
to obtain mathematical solutions (Thissen, 1998; Lofgren et al., 2002). Relative
changes in endogenous variables due to the substitution of carbon taxes for labor
income taxes are derived from simulation of the CGE model.
3.2 Interactions among Institutions in PA CGE Model
The interactions among institutions in the Pennsylvania economy are
described in figure 3.1. Production activity (PROD) includes intermediate input
(IMIP) demand (1) and pays indirect business taxes (2). Government purchases
commodities from production activity (3). The household (HH) consumes
commodities (20) and investment activity (SI) consumes commodities (5) as well.
Production activity exports (imports) commodities to (from) the rest of the other
70
countries (ROW) and the rest of the U.S. (ROUS) (6, 10). Production activity pays
providers of factors such as labor (LAB), proprietary service (LAND), and capital
(CAP) (21).
Government consists of federal government (FED), state/local non education
(SLNE), and state/local education (SLED) and they transfer money to each other
(4). Factor activities such as LAB, LAND, and CAP pay factor taxes such as the
payroll tax, proprietary tax, and capital stock tax to governments (19).
Government activity transfers money to the household (17) and enterprise (22).
Government transfers money to saving/investment accounts, SI (7) and borrows
money (8).
The household pays income taxes, payroll taxes, and other taxes to
government (23), consumes imported goods, and receives remittance from
foreign countries or other USA regions (12). Enterprise activity pays part of
corporate profits to the household as a dividend (14). The household offers labor,
proprietary service, and capital service and receives wages and returns to
proprietary and capital service (18). The household saves and borrows money
from saving/investment accounts (11). Enterprise pays corporate profit tax to
governments (15) and saves retained earnings to SI account (16).
71
Figure 3.1 Flow Diagram of Pennsylvania CGE Modeling
Source: Revised from Thorbecke (1988)
FED
SLNE SLED
PROD
SI
ROW/ROUS HH ENT
LAB LAND CAP
IMIP ①
② ③
④
⑤ ⑥
⑦
⑧
⑩
(11)
(12) (14)
(15)
(16)
(17)(18)
(19)
(20) (21)
(22)
(23)
72
3.3 Derivation of Equations
Equations consist of production, consumption, trade markets, government,
equilibrium conditions, and closure rules. The production system is elaborated
upon more than the consumption system in order to describe the demand of
fossil fuels by industries as main sources of GHG emissions. The consumer’s
utility system is simplified to focus on the role of demand for leisure or labor
supply. As carbon taxes are imposed on the consumption of fossil fuels to reduce
GHG emissions, firms will substitute the consumption of alternative, clean, and
renewable fuels for the consumption of fossil fuels. Production and consumption
systems employ nested CES functions to reflect various substitutability. The
equilibrium conditions and macro closure rule will be discussed in section 3.4.
3.3.1 Production and factor demands
Figure 3.2 shows the overall production system. Output (Xi) of industry i is
produced by primary inputs (VA) and intermediate inputs (IM); primary inputs
consist of labor (L), proprietor’s service (F), capital (K), and energy (E). Labor and
proprietary services are combined in one CES function, while capital and energy
are bundled in the other CES function. Energy is a composite of gas, oil, coal,
electricity, and alternative fuels (ALTF) such as solar, hydroelectricity, nuclear
73
electricity, fuel cells and others. In the other group, the combined intermediate
inputs consist of materials (M) and transportation (TRAN).
Figure 3.2 NCES Production System
NCES production function
VA IM VA: factor inputs IM: intermediate goodsLF: composite of labor and proprietary serviceKE: composite of capital and energy M: all other materials
F(VA,IM)
LF KE
L F K E
ELEC GAS OIL COAL
M TRAN
1st CES
2st CES
3st CES
4st CES
ALTF FOSSIL FUELS
74
Production functions used in the model is presented by a nested constant
elasticity of substitution (NCES) production function. At the first level of NCES,
industry i produces goods (Xi) using primary inputs (VA) and intermediate
inputs (IM). A calibrated shared form of a CES function is employed for
convenience of programming (Rutherford, 1995).
1/11
11
1 ))(1()(ρ
ρρ γγ ⎥⎦⎤
⎢⎣⎡ −+=
IMIM
VAVAXX ii (3.1)
Primary inputs are substitutable for intermediate inputs with the elasticity of
substitution ( 111
1 ρσ
−= ) (Varian, 1992).
A share parameter ( 1γ ) is calibrated by the benchmark value of prices and
quantities (Rutherford, 1995). The share parameter can be regarded as the “cost
share” of one factor input relative to the total cost of production. The share
parameter and the elasticity of substitution affect the level of factor demand.
IMPVAPVAP
imva
va
+=1γ (3.2)
At the second tier of the NCES function, intermediate inputs are divided
into transportation and all other materials including agriculture, mining,
construction, manufacturing, utility, wholesale and retail trade, services, FIRE
(financial, insurance, and real estate), and public sectors. The functional form and
calibration follow the above procedure.
75
21/1
2121
2121 ))(1()(
ρρρ γγ ⎥⎦
⎤⎢⎣⎡ −+=
MM
TRANSTRANSIMIM (3.3)
MPTRANSPTRANSP
mtrans
trans
+=21γ (3.4)
On the other second level of the NCES function, primary inputs (VA) consist
of a composite of labor and proprietary service (LF), and a composite of capital
and energy (KE).
22/122
2222
22 ))(1()(ρ
ρρ γγ ⎥⎦⎤
⎢⎣⎡ −+=
KEKE
LFLFVAVA (3.5)
KEPLFPLFP
kelf
lf
+=22γ (3.6)
The composite of labor and proprietary services is divided into labor and
proprietary services using a third level CES function.
31/131
3131
31 ))(1()(ρ
ρρ γγ ⎥⎦⎤
⎢⎣⎡ −+=
FF
LLLFLF (3.7)
FPLPLP
fl
l
+=31γ (3.8)
The composite of capital and energy is divided into capital service and
combined energy in the other third level CES function.
32/132
3232
32 ))(1()(ρ
ρρ γγ ⎥⎦⎤
⎢⎣⎡ −+=
EE
KKKEKE (3.9)
76
EPKPKP
ek
k
+=32γ (3.10)
The combined energy consists of alternative fuels and the composite of fossil
fuels in the fourth CES function.
4/14
44
4 ))(1()(ρ
ρρ γγ ⎥⎦⎤
⎢⎣⎡ −+=
FFUELFFUEL
ALTFALTFEE (3.11)
FFUELPALTFPALTFP
ffuelaltf
altf
+=4γ (3.12)
The composite of fossil fuels consists of coal, oil, gas, and electricity in the
fifth CES function,
i
i
i
i
ii FUEL
FUELFFUELFFUEL5/1
55 )(
ρ
ργ ⎥⎦
⎤⎢⎣
⎡= ∑ (3.13)
∑=
iiifuel
iifueli FUELP
FUELP
,
,5γ (3.14)
where FUEL is a composite of coal, oil, gas, and electricity.
Conditional (i.e., Hickian or compensated) factor demands are derived from
cost minimization of firms using the above NCES production function.
The calibrated forms (Rutherford, 1995) of unit cost functions and
compensated factor demand functions for the NCES function are as follows;
77
The unit cost function and factor demand functions in the first CES function
are:
111
11
11
1111 ))(1()(),(
σσσ γγ
−−−
⎟⎟⎠
⎞⎜⎜⎝
⎛−+==
im
im
va
vaimva P
PPPePPePX (3.15)
Where MIPVAPPPe imvaimva +=),(1
1
),(σ
⎟⎟⎠
⎞⎜⎜⎝
⎛××=
va
vaimva P
PPXPX
XXVAPPVA (3.16)
1
),(σ
⎟⎟⎠
⎞⎜⎜⎝
⎛××=
im
imimva P
PPXPX
XXIMPPIM (3.17)
The unit cost function and factor demand functions for the aggregated
intermediate goods and aggregated value added inputs in the second CES
functions are:
212121
11
121
1212121 ))(1()(),(
σσσ γγ
−−−
⎟⎟⎠
⎞⎜⎜⎝
⎛−+==
m
m
trans
transmtransim P
PPPePPeP
(3.18)
Where MPTRANSPPPe mtransmtrans +=),(21
21
),(σ
⎟⎟⎠
⎞⎜⎜⎝
⎛××=
trans
trans
im
immtrans P
PPP
IMIMTRANSPPTRANS (3.19)
21
),(σ
⎟⎟⎠
⎞⎜⎜⎝
⎛××=
m
m
im
immtrans P
PPP
IMIMMPPM (3.20)
78
222222
11
122
1222222 ))(1()(),(
σσσ γγ
−−−
⎟⎟⎠
⎞⎜⎜⎝
⎛−+==
ke
ke
lf
lfkelfva P
PPP
ePPeP
………… (3.21)
Where KEPLFPPPe kelfkelf +=),(22
22
),(σ
⎟⎟⎠
⎞⎜⎜⎝
⎛××=
lf
lf
va
vakelf P
PPP
VAVALFPPLF (3.22)
22
),(σ
⎟⎟⎠
⎞⎜⎜⎝
⎛××=
ke
ke
va
vakelf P
PPP
VAVAKEPPKE (3.23)
The unit cost function and factor demand functions of composite of labor
and proprietary services in the third CES functions are:
313131
11
131
1313131 ))(1()(),(
σσσ γγ
−−−
⎟⎟⎠
⎞⎜⎜⎝
⎛−+==
f
f
L
LfLLf P
PPPePPeP
…………. (3.24)
Where FPLPPPe fLfL +=),(31
32
),(σ
⎟⎟⎠
⎞⎜⎜⎝
⎛××=
L
L
Lf
LffL P
PPP
LFLFLPPL (3.25)
32
),(σ
⎟⎟⎠
⎞⎜⎜⎝
⎛××=
f
f
Lf
LffL P
PPP
LFLFFPPF (3.26)
The unit cost function and factor demand functions of composite of capital
and energy in the third CES functions are:
79
323232
11
132
1323232 ))(1()(),(
σσσ γγ
−−−
⎟⎟⎠
⎞⎜⎜⎝
⎛−+==
e
e
k
kekke P
PPPePPeP
(3.27)
Where EPKPPPe ekek +=),(32
32
),(σ
⎟⎟⎠
⎞⎜⎜⎝
⎛××=
k
k
ke
keef P
PPP
KEKEKPPK (3.28)
32
),(σ
⎟⎟⎠
⎞⎜⎜⎝
⎛××=
e
e
ke
keef P
PPP
KEKEEPPE (3.29)
The unit cost function and factor demand functions of composite of energy
in the fourth CES functions are:
444
11
14
1444 ))(1()(),(
σσσ γγ
−−−
⎟⎟⎠
⎞⎜⎜⎝
⎛−+==
ffuel
ffuel
altf
altfffuelaltfe P
PPP
ePPeP (3.30)
FFUELPALTFPPPe ffuelatfffuelaltf +=),(4 (3.31)
4
),(σ
⎟⎟⎠
⎞⎜⎜⎝
⎛××=
altf
altf
e
effuelaltf P
PPP
EEALTFPPATLF (3.32)
4
),(σ
⎟⎟⎠
⎞⎜⎜⎝
⎛××=
ffuel
ffuel
e
effuelaltf P
PPP
EEFFUELPPFFUEL (3.33)
The unit cost function and factor demand functions of composite of energy
in the fourth CES functions are:
80
∑−
−
⎟⎟⎠
⎞⎜⎜⎝
⎛==
i ifuels
ifuelsiifuelsffuel
i
PP
ePeP5
5
11
1
,
,55,5 )()(
σσγ (3.34)
Where ∑=i
iE
ifuelsifuel FUELSPPe ,,5 )( (3.35)
i
ifuels
ifuels
e
ei
Ei
E
P
P
PP
EEFUELSFUELS
5
,
,
σ
⎟⎟⎠
⎞⎜⎜⎝
⎛××= (3.36)
3.3.2 Consumption Sector
The household utility function has two stage CES functions. The first tier
utility function consists of demand for leisure (R) and aggregated market goods
(C ). In the second tier utility function, the aggregated market good (C ) is divided
into seven market commodities; market goods are produced by industries which
employ primary inputs and intermediate inputs. Utility functions have a nested
system, represented in Figure 3.3.
81
Figure 3.3 Consumer’s Utility System
To set up the first utility optimization problem, budget and time constraints
first should be built. Households are assumed to consume market commodities
and leisure within the constraints of the household income.
Household income ( 1I ) includes labor income ( LI ), opportunity cost of
leisure, and other kinds of incomes such as capital ( KI ), proprietary income ( FI ),
and government transfer (G ). This expanded income is different from observed
household income in that the former income includes the opportunity cost of
spending time on leisure (Ballard et al., 1985).
The amount of time spent on leisure is derived from the ratio between total
endowment of time (T) and time to spend on work (Ls). Assuming that total
possible labor hours per week is seventy and average labor hours per week is
First tier CES utility function
U(R) U( C )
C1……………C17
R: demand for leisure C : aggregated market commodity (Second tier CES function)
U(R, C )
82
forty, the ratio of leisure time and working time is 0.75 (Ballard et al., 1985). Thus,
the amount of time dedicated to leisure is calculated from multiplying working
time by 0.75. The total endowment of time (T) is 1.75 times the working time.
GIIII FKL +++= *75.11 (3.37)
The representative household maximizes the CES utility function subject to
the budget constraint. For convenience of programming, a calibrated share form
of the CES utility function is applied to the CGE model (Rutherford, 1995).
11
11
11
11
1
11
11
])1([),(0
10
11
−
−−
−+=U
UUU
U
UU
u
CC
RRCRMaxU UU
σσσ
σ
σ
σσ
σ γγ (3.38)
Subject to CPwRI *1 += , T = R + Ls
The price of leisure is the opportunity cost of working, which is the after‐tax
real wage (w), and the price of the aggregated market commodity (P ) is derived
from cost minimization. The net real wage rate is affected by the nominal wage
rate, labor income tax rate, and price index.
PtLPLw )1( −
= , where TQ
QPQp i
ii∑ ×= (3.39)
The elasticity of substitution between leisure and aggregated consumption
of goods is given exogenously as 0.96 (Goulder et al., 1999).
From the conventional solution to the utility optimization problem, the share
parameter is calibrated:
83
CPwRwR
U *1 +=γ (3.40)
The unit expenditure function is derived by solving the dual problem of
utility maximization. The composite price of the first utility ( TCP ), which is the
sum of R and C , is referred to as the unit expenditure function.
111
11
11
111 ))(1()(),(
uu
Ouu
OuuTCP
PwwPweP
σσσ γγ
−−−
⎟⎟⎠
⎞⎜⎜⎝
⎛−+== (3.41)
The expenditure function ( ),(1 Pweu ) is calculated from multiplying the unit
expenditure function by household expenditure on the consumption of
commodities and leisure.
TCu PEXPPwe ×= 11 ),( , CREXP +=1 (3.42)
The indirect utility function is derived by plugging the Marshallian demand
functions into the original utility function.
),(),,(
11
11 PweI
IIPwV
uO ×
= (3.43)
The Hicksian demand functions for leisure and composite commodity are
derived by applying Roy’s identity to the indirect utility function in (3.43)
(Varian, 1992).
1),(),,(),,( 1
11
uOuO
wwPwe
IPwVRIPwRσ
⎟⎟⎠
⎞⎜⎜⎝
⎛ ×××= (3.44)
84
1
),(),,(),,( 1
11
u
PPPwe
IPwVCIPwCO
uO
σ
⎟⎟⎠
⎞⎜⎜⎝
⎛ ×××= (3.45)
When the demand for leisure is derived, labor supply (Ls) can be derived
from the difference between the total endowment of time and the demand for
leisure. P is discussed at the end of this section. All variables with the
superscript 0 denote initial values.
The second tier of the CES utility function is written:
iU
i
iUO
i
iiU C
CC2/1
22 )(
ρ
ργ ⎥⎦
⎤⎢⎣
⎡= ∑ (3.46)
The share parameter is calibrated:
∑=
i
Oi
Oi
Oi
Oi
iU CPCP
2γ (3.47)
The unit expenditure function, indirect utility function, and Hicksian
demand for the second CES utility function are:
∑−
−
⎟⎟⎠
⎞⎜⎜⎝
⎛==
iO
i
iiUiu
iUiU
PPPPPeP
22
11
12212 )(),...,(
σσγ (3.48)
),...(),,...(
122
221
iuOi PPeI
IIPPV×
= (3.49)
iU
i
iO
iiO
i PPPIPPVCC
2
21 ),,...(σ
⎟⎟⎠
⎞⎜⎜⎝
⎛××= (3.50)
85
where the household income (I2) in the second level is defined as wlI − .
The expenditure function is calculated by multiplying the unit expenditure
function by the household expenditure on commodity.
2212 ),...,( EXPPPPPe iu ×= , and ∑=i
iiCPEXP2 (3.51)
When the carbon tax is levied on the consumption of carbon‐emitting
commodities, consumers will reduce expenditures on consumption of fossil fuels
while increasing consumption of clean fuels such as hydro power, solar, wind,
and fuel cells.
3.3.3 Trading Sector
The trading sector is divided into foreign and inter‐regional sub‐sectors. The
foreign trade sector includes foreign export and import between the rest of the
world (ROW) and Pennsylvania while inter‐regional trade involves domestic
export and import between other USA regions (ROUS) and Pennsylvania.
Armington and CET functions are applied to the allocation of export,
domestic supply, import, and domestic demand given that cross hauling is
assumed. With this assumption, imported and exported goods are imperfect
substitutes for domestic goods. In particular, the imperfect substitutes of
86
imported goods assume that a qualitative difference exists between imported
input and domestic inputs (Ballard et al., 1985)
Regional output (X) is allocated between domestic supply (SD) and foreign
export (FE) using a first tier CET function. Domestic output supply is allocated
between regional supply (XXS) and domestic export (DE) using a second tier
CET function. Regional absorption (Q) is decomposed into domestic demand
(XD) and foreign import demand (FM) using a first tier Armington function.
Domestic demand (XD) is divided into regional demand (XXD) and domestic
import (DM) using a second tier Armington function. Finally, regional supply
(XXS) is equal to regional demand (XXD) by the above trade structure. The
export and import price in ROW and ROUS levels are assumed to be fixed as one.
Figure 3.4 shows the structure of the trading sector.
87
Figure 3.4 Structure of Trading Sector
(Parentheses are the prices of each product and demand level)
Q (PQ)
XD(PXD)
FM(PFM)
XXD(PD)
DM(PDM)
X (PX)
SD(PSD)
FE (PFE)
XXS(PD)
DE(PDE)
Import side
Export side
First tier Armington Second tier Armington
First tier CET function Second tier CET function
88
3.3.3.1 Regional Supply and Export
The output (Xi) of industry i is allocated into the export to ROW (FE) and
domestic supply (SD) of commodities using a CET (constant elasticity of
transformation) function.
111
))1(( +++
−+= ff
f
f
f
f
ifiifiii SDFEATFX ττ
ττ
ττ
γγ (3. 52)
where
iii FEXSD −= (3.53)
i
iiii SD
FEERPFEXPXPSD
××−×= (3.54)
iATF CET function shift parameter of the first tier CET function
fiγ First tier CET function share parameter
iFE Foreign exports of industry output
fiτ Transformation elasticity (CET function exponent)
iSD Domestic supply of commodity
iX Regional supply of production
iPSD Price of SD
iPX Output price (price of X)
PFE Foreign export price
ER Exchange rate
89
The shift parameter (ATF) in the first tier CET function is calibrated by
solving the CET function in reverse. The elasticity of transformation ( fτ ) is
related with the CET exponent ( ρ ) given that fτ
ρ+
=1
1 (Robinson et al.,
1990).
The share parameter (γf) is calibrated by the first order conditions (FOCs) in
the profit maximization problem subject to the CET production function.
f
f
f
f
f
f
iifii
iii
SDFEATFXts
SDPSDFEERPFEMax
ττ
ττ
ττ
γγ
π
+++
−+=
×+××=
111
])1([.. (3.55)
The Lagrangian is,
]])1([[ 111
f
f
f
f
f
f
iiiiiii SDFEATFXSDPSDFEERPFEL τ
τ
τ
τ
τ
τγγλ +
++−+−+×+××=
Differentiating with regard to FE and SD, then the F.O.C. will be
01
11
11)1/( =
+×
+−×=
∂∂ −
+−+
f
fff
if
fii
f
f
i
i FEBATFERPFEFEL
ττ
ττ
ττ
γτ
τ
…….. (3.56)
01
)1(1
11
1)1/( =+
−×+
−=∂∂ −
+−+
f
fff
if
ffii
f
fi
i
i SDBATFPSDSDL
τ
τττ
ττ
γτ
τ (3.57)
where f
f
f
f
ififi SDFEB ττ
ττ
γγ++
−+=11
)1(
90
Dividing (3. 56) by (3.57) yields
ii
i
f
f
PSDERPFE
SDFE f ×
=⎟⎟⎠
⎞⎜⎜⎝
⎛−
τ
γγ /1
1 (3.58)
Rearranging equation (3.58) with regard to Gamma yields
τγ /1
1
1
⎟⎟⎠
⎞⎜⎜⎝
⎛×
×+
=
i
ii
f
SDFE
ERPFEPSD
(3.59)
and the demand of foreign export is derived from equation (3.59):
f
f
f
iii PSD
ERPFESDFE τ
γγ
)1*(−
×= (3.60)
Regional output will be allocated between the supply of the foreign export
and domestic supply, which is composed of regional supply (XXS) and domestic
export (DE). In the same way, domestic supply (SD) is allocated between
domestic export and regional supply. The CET function for this allocation is
presented in (3.61).
d
d
d
d
d
d
ididii XXSDEATDSD ττ
ττ
ττ
γγ +++
−+= 111
])1([ (3.61)
Using profit maximization subject to the CET production function, we get
the supply of domestic export,
d
d
d
iii PXD
PDEXXSDE τ
γγ )1( −
×= (3.62)
91
The price of total supply (X) is given as one, and the price of domestic
supply (PSD) is determined by the equality of total revenue to the sum of
domestic production value and export revenue in equation (3.63).
iiiii FEERPFESDPSDXPX ××+= (3.63)
The price of domestic supply is determined by the value of domestic export,
regional supply, and domestic supply in equation (3.64).
i
iii SD
XXSPXDDEPDEPSD ×+×= (3.64)
Equations (3.63) and (3.64) reflect the homogeneity of export transformation
by the CET function. The value of the composites such as Xi and SDi should be
equal to the value of component parts, regardless of functional form (Robinson et
al., 1990).
3.3.3.2 Regional Consumption and Imports
Consumers demand the composite of foreign imports and domestic goods in
the first tier. An Armington CES function is used to determine the demand for
foreign imports and domestic goods. In the CGE model, total absorption of
commodity (Qi) of each industry i is allocated between foreign imports (FMi)
and domestic demand (XDi) by minimization of the cost function subject to the
Armington production function constraint.
92
111
])1([(..
)(
−−−
−+=
×+××
f
f
f
f
f
f
ififiii
iii
XDFMACFQts
XDPXDFMERPFMMin
σ
σ
σ
σ
σ
σ
δδ (3.65)
Shift parameters (ACF) are calibrated by reverse solving the Armington
function the share parameter ( fδ ) is calibrated from the first order conditions of
equation (3.65)
)])1([(( 111
−−−
−+−+×+××= f
f
f
f
f
f
ififiiiii XDFMACFQXDPXDFMERPFML σ
σ
σ
σ
σ
σ
δδλ
F.O.C)
01
])1([(1
111
111
=−
×−+×××−
−×=∂∂ −
−−−
−−
f
ff
f
f
f
f
f
if
ffififi
f
f FMXDFMACFERPFMFML
σ
σσ
σ
σ
σ
σ
σ
σσ
δδδλσσ
……….(3.66)
01
)1(])1([(1
111
11
1
=−
×−−+×××−
−=∂∂ −
−−−
−−
f
ff
f
f
ff
f
if
ffiffi
f
fi XDXDFMACFPXD
XDL
σ
σσ
σ
σ
σσ
σ
σσ
δδδλσσ
………. (3.67)
Dividing (3.66) by (3.67) produces equation (3.68):
01
11
=⎟⎟⎠
⎞⎜⎜⎝
⎛−
−×
−−
f
f
i
i
f
f
i XDFM
PXDERPFM σ
σ
δδ
(3.68)
Rearranging (3.68) with regard to the demand of foreign import yields
F
ERPFMPXD
XDFM i
f
fii
σ
δδ
⎥⎥⎦
⎤
⎢⎢⎣
⎡
×−=
1 (3.69)
Finally, equation (3.69) is used to get the calibration of share parameter.
93
f
XDFM
PXDERPFM
f
σ
δ/1
' ⎟⎠⎞
⎜⎝⎛×
×= where
'
'
1 f
ff δ
δδ
+= (3.70)
The domestic import demand (DM) can be derived using the same steps.
The Armington production function for the allocation of domestic demand
(XD) to domestic import (DM) and regional demand (XXD) is presented in
equation (3.71).
111])1([( −
−−
−+= d
d
d
d
d
d
ididiii XXDDMACDXD σσ
σσ
σσ
δδ (3.71)
From minimization of the cost function subject to the second tier Armington
production function, we get the domestic import demand (DM) as equation
(3.72).
d
PDMPDXXDDM i
d
dii
σ
δδ
⎥⎦
⎤⎢⎣
⎡−
=1
(3.72)
The calibration procedure for the share parameter ( dδ ) at the second tier is
the same as with the first tier calibration.
d
XXDDM
PDPDM
d
σ
δ/1
' * ⎟⎠⎞
⎜⎝⎛= (3.73)
The price of regional absorption is given as one and the price of domestic
demand(XD) is determined by the equality of total value of regional absorption
to the sum of value of foreign import and the value of domestic demand.
i
iii XD
FMERPFMQPQPXD
××−×= (3.74)
94
Within the domestic level, the price of regional demand (PD) is determined
by the relationship that the total value of domestic demand should be equal to
the sum of the value of domestic import demand and the value of regional
demand.
i
iiii XXD
DMPDMXDPXDPD ×−= (3.75)
Equation (3.74) and (3.75) reflect the homogeneity of the import aggregation
by the Armington function. The same argument holds in the determination of
prices in import sectors such that the value of the composites like regional
absorption or domestic demand should be equal to the value of component parts,
regardless of functional form.
3.3.4 Government Account
The government institution is composed of federal government and
state/local government. The state/local government account is divided into
education and non‐education accounts. Government revenues include tax
payments by industry, household, and enterprise, transfer from other
governments, and debts from finance institutes. Expenditures are composed of
purchase of commodities, imports, transfers to the household, enterprise, and
other governments.
95
3.3.4.1 Tax Payments
Governments get revenues from payroll tax, proprietor tax, enterprise tax
(corporate profit tax), indirect business tax, and household taxes. In addition,
governments receive transfers from other government, income from trading, and
borrowed money from financial institutions.
Payroll tax (STAX), proprietors’ payment (PPRTAX), and enterprise taxes
(corporate profit tax (ENTAX)) rates are determined by equations (3.76), (3.77),
and (3.78).
*WLSSTAXSSTAXR
ii
kk ×=∑ , where W* is a nominal wage rate (3.76)
PPFPPRTAXPPTAXR
ii
kk ×=∑ , where PP is a nominal return to proprietary
services (3.77)
∑=
ii
kk RK
ETAXENTAXR * , where R* is a nominal return to capital services (3.78)
The payroll tax rate (or labor income tax rate) is calibrated by dividing the
payroll tax payment by labor income in equation (3.76). The proprietary payment
rate is defined as the ratio of proprietary payment and proprietary income in
96
equation (3.77). Enterprise income tax or corporate profit tax rates are calibrated
from dividing capital stock tax payment by capital income in equation (3.78).
Table 3.2 Labor Income Tax, Proprietary Tax, and Corporate Taxes and Rates Taxes Federal government State government
Labor income tax 28175.77 355.243
proprietary tax 1515.652 .
corporate income tax 9382.803 1693.078
Indirect business taxes (ITAX) include specific sales, business property tax,
general retail sales tax, and other taxes. The Pennsylvania CGE model assumes a
combination of indirect business taxes and the indirect business tax rate is
derived from dividing industry output value by the tax payment.
iiikik XPXITAXITAXR ×= /,, (3.79)
Indirect business tax payments and rates in each industry are shown in table
3.3.
97
Table 3.3 Indirect Business Tax Payments Industry indirect business tax payment ($ million)
Federal government State government
AGR 43.78 94.042
MIN 9.133 19.619
COAL 168.898 345.174
GAS 11.23 24.122
OIL 22.126 47.529
ALTF 5.601 12.031
CNT 105.376 226.355
NDMNF 427.437 918.164
DMNF 313.695 673.838
TRAN 249.948 536.906
UTIL 357.614 768.179
ELEC 442.272 950.03
EGUTIL 66.227 142.26
TRADE 3768.795 8095.63
FIRE 2786.793 5986.223
SER 730.21 1568.541
(There is no indirect business tax payment from the other public sector, since
public sectors are supposed to pay no indirect business taxes in IMPLAN)
Household tax (HTAX) consists of household income tax, household
property tax, and household other taxes. Household other taxes include those on
estate and gift, non taxes such as fines and fees, motor vehicle license, fishing and
hunting licences, but the Pennsylvania CGE model only includes the combined
98
household taxes. The household tax rate is calculated by dividing household tax
payment by household income (HHY):
hhh HHYHTAXHTAXR /= (3.80)
Household taxes to the federal government are $40,777.434 million, and
$14,537.746 million are paid to the state and local governments. The federal
household tax rate is 11.5% and the state household tax rate is 4.1%.
3.3.4.2 Government Budget Balances
In equilibrium, governments are assumed to have no deficit, which implies
that total revenues are equal to or larger than total expenditures.
The federal government budget deficit is government expenditures less
government revenues. Federal government expenditures include government
purchase of commodities (G), commodity imports from ROW and ROUS (FMPG
and DMPG), transfer expenditure to households (TRANS), transfer to non
educated state/local government (FEDNED) and educated state/local
government (FEDED), transfer to enterprise (ENTFED), and government savings
(GOVSVO). Sources of federal government revenues are labor income tax (STAX),
corporate income tax (ENTAX), proprietary taxes (PPRTAX), government sales
(GS), indirect business taxes (ITAX), household taxes (HTAX), government
99
borrowing (GOVBRO), government income from trading (GOVTRO), transfer
income from state/local non education (NEDFED), and education (EDFED).
kk
ikk
hhkkkki
GOVSVOENTFED
FEDEDFEDNEDTRANSODMPGFMPGGFEDEXP
++
+++++= ∑ ∑ ,,
………(3.81)
EDFEDNEDFEDHTAXITAX
GOVTROGSENTAXPPRTAXSTAXFEDREV
kk
ki
ikkkkh
++++
++++= ∑ , (3.82)
Government expenditures (G) are calibrated by the proportion of
government commodity expenditures (DRG) and total government expenditure
on commodities (TOTG).
k
kiki TOTG
GDRG ,
, = (3.83)
Non‐educated state/local government deficit (NEDFL) is government
expenditures less government revenues. Non‐educated state/local government
expenditures include government purchase of commodities, non‐comparable
imported commodity from ROW and ROUS, transfer expenditure to households
(TRANS), transfer to federal government (NEDFED) and educated state/local
government (EDTRANS), transfer to enterprise (ENTNED), and government
savings (GOVSVO). Non‐educated state/local government revenues are labor
income taxes, corporate income tax, government sales, indirect business taxes,
100
household taxes, government borrowing, government income from trading, and
transfer income from the federal government (FEDNED).
k
kkh
hkh
hkh
hk
iik
iik
iik
iiik
kkkkkkk
ki h
hkkkiik
FEDNED
GOVTROGOVBROHHOTHTAXHHPROTAXHINCTAX
SALETAXBPROTAXOTHERTAPDGSPPRTAXENTAXCATAXGOVSVOSTAXENTNEDEDTRANS
NEDFEDTRANSDMPGFMPGPGNEDFL
−
−−−−−
−−−−
−−−+−++
++++=
∑∑∑
∑∑∑∑
∑ ∑
,,,
,,,,
,,
………(3.84)
Transfers from non‐education state/local government to education‐
state/local government (EDTRANS) are the whole revenue source of education‐
state/local government. There is no tax revenue in education‐state/local
government, and all the revenue is from transfers from other governments.
Expenditures consist of government purchase of goods, demand of imported
commodities from ROW and ROUS (FMPG, DMPG), transfers to the household
(TRANS), transfers to non‐educational state/local government (EDNED) and
federal government (EDFED), and government savings (GOVSVO).
GOVSVOEDFED
EDNEDTRANSONCIMPGPGEDTRANSI HH
HHEDIEDI
++
+++= ∑ ∑ ,,
(3.85)
101
3.3.5 Environmental Indicator
The Pigouvian effect occurs as the consumption of fossil fuels is diminished
due to the imposition of carbon taxes. To evaluate the Pigouvian effect
completely, environmental amenities should enter the household utility function
as a non‐separable variable (Schwartz and Repetto, 2000; Williams III, 2002 and
2003). However, it is not verified yet how to include non‐separable
environmental amenities in the CGE model. In this study, an alternative
approach, which does not reflect the full effect, is employed to evaluate the
Pigouvian effect.
As an environmental indicator, the level of environmental amenities is a
diminishing function of consumption of fossil fuels, since the use of fossil fuels
emits carbon‐based gases which result in degradation of the natural environment.
The equation is revised from a study by Böhringer et al. (2003).
TCEENVAMEN ×−=2π
(3.86)
where AMEN is the condition of environmental amenity, ENV is the endowment
of environmental amenity in Pennsylvania, π is an emission coefficient, and TCE
is total consumption of energy‐intensive goods.
As the consumption of fossil fuels is reduced because of fuel taxes, the
environmental amenities are improved. The value of endowed environmental
102
amenities is assumed to be 20% of gross state product (GSP) in 2000 and the
emission coefficient is given as 0.001.
As fuel taxes are imposed on the consumption of fossil fuels, the primary
effect is the mitigation of global warming since the emission level of GHGs will
be reduced. However, unless all other states and countries invest in mitigation of
GHG emissions,, the overall emission level of GHGs will not be reduced.
Therefore, a unilateral carbon tax by one region will not lead to the reduction of
total GHG emissions. But there can be a co‐benefit from the fuel tax which
includes avoided human health from air pollution, eco‐system effects such as
avoided crop damage, reduced soil erosion, and reduced bio‐diversity loss, and
economic effects such as technological innovation and job creation (McKinstry et
al., 2004). In this study, fuel taxes are supposed to provide the co‐benefit
confined to local air quality improvement, but the value of co‐benefit is not
evaluated since co‐benefit analysis is complex and inter‐related with numerous
factors. The environmental indicator used in this study only provides an
approximation of the environmental improvement, therefore it cannot be
interpreted as the Pigouvian effect. The main purpose of developing the
environmental indicator is to use it as an explanatory variable for labor migration
explained in the next section.
103
3.3.6 Factor Mobility
Literature review on labor migration and environmental amenities supports
that labor will move into the focus region as net real wage rates or environmental
amenities in the destination region increase. After‐tax real wage rates reflect
labor income taxes and prices of commodities. As labor income taxes are reduced
due to tax reform policy, the wages are increased, so labor flows into
Pennsylvania. On the other hand, as the price of market commodities goes up as
a result of imposing carbon taxes, the wages are diminished, thus workers move
out of Pennsylvania. Changes in relative environmental amenities affect the
decision of labor migration. Better environmental quality in Pennsylvania
resulting from carbon taxation attracts households to move into the focus region.
The labor migration decision is represented in equation (3.87).
)/log()/log( 21 RUSPARUSPAmig AMENAMENwwL εε += (3.87)
Labor migration ( migL ) is a function of the natural log of relative net real
wage and natural amenities; 1ε and 2ε imply wage elasticity of migration and
amenity elasticity of migration, respectively. A working paper (Shields et al.,
2005) estimated earned income elasticity of out‐migration from Pennsylvania as
0.017, and cancer risk elasticity of out‐migration from Pennsylvania as 0.025.
Cancer rate can be regarded as an approximation of the environmental quality in
104
a state; these figures are applied in this study. A sensitivity analysis is conducted
to explore the reliability of the model for the migration variable.
Labor migration affects the total amount of regional labor supply (GLS). The
total labor supply is the sum of labor migration and net regional labor supply
(LS).
LSLGLS mig += (3.88)
3.4 Equilibrium and Macro Closure Rule
In equilibrium, all the markets should be cleared by the equilibrium prices:
commodity market, factor market, and macro balances (Robinson et al., 1990).
Total commodity supply (X) should be equal to total commodity demand, which
is the sum of energy input demand (E), other intermediate input demand (IM),
consumer’s demand (C), government demand of commodities (G), investment
demand, and export demand.
iiigov
iiii DEFEITGCIMEX ++++++= ∑ (3.89)
For the factor market equilibrium, total labor (TLS), proprietors (TFS), and
capital supply (TKS) are equated to the sum of each industry’s demand for labor
(LD), proprietors (FD), and capital (KD).
105
∑ +++=gov
LADJEXOINCOMESTAXGLSTLS (3. 90)
∑=i
iLDTLS (3.91)
∑=i
iFDTFS (3.92)
∑=i
iKDTKS (3.93)
Next, government revenue should be equal to government expenditure.
FEDEXPFEDREV = (3.94)
NEDEXPNEDREV = (3.95)
EDEXPEDUTRAN = (3.96)
Macro closure includes saving‐investment, government deficit, and balance
of foreign exchange. For the saving and investment market equilibrium, total
saving (TOTSAV) is the sum of inventory of industries (INVNTR), household
saving (HHSAVING), government saving (GOVSV), retained earnings of
enterprise (RET), labor and proprietary capital adjustment (LADJ and PADJ),
and savings of the rest of the world (ROWSAV) and the rest of the states
(RUSSAV).
RUSSAVROWSAV
PADJLADJRETGOVSVHHSAVINGINVNTRTOTSAVgovi
i
++
+++++= ∑∑ (3.97)
106
Total investment is the sum of industry’s investment demand.
∑=i
iITTINT (3.98)
The foreign exchange market should be in equilibrium. The expenditure of
the rest of the world and the rest of the states is the sum of exports, household
remittances, export revenue of governments, and savings.
RUSSAVDEXTXHHREDDERUSEXgovi
i +++= ∑∑ (3.99)
ROWSAVFEXTXHHREFFEROWEXgovi
i +++= ∑∑ (3.100)
Revenue of the rest of the world and the rest of the states is the sum of
imports, outflows of labor, proprietary and capital income, debt from finance,
federal government imports, state/local non‐education imports, and state/local
education imports.
DMPEGDMPNEGDMPFGRUSDEFFCADJDPADJDDMRUSYi
i ++++++= ∑FMPEGFMPNEGFMPFGROWDEFFCADJFPADJFFMROWY
ii ++++++= ∑
……..(3. 101)
The labor supply and wage rate are not fixed to allow labor migration and
reflect flexible wage rates. Supplies of proprietary and capital services are fixed;
public purchase of commodities, the exchange rate and exogenous savings are
fixed as well.
107
To close the CGE model, the number of independent equations and the
number of endogenous variables should be the same. By Walras’ law, not all
variables are independent, so any one equation can be eliminated (Robinson et
al., 1990). The total number of equations will be N‐1 when the total number of
endogenous variables is N. In the Pennsylvania CGE model, the saving‐
investment equilibrium condition is removed from the equations.
The total number of equations except the saving‐invest equilibrium
condition for Walras’ law is 457, the total number of endogenous variables is 474,
and the total number of fixed variables is 16. The total number of net endogenous
variables is 458 (Appendix A); this satisfies Walras’ law for equilibrium
conditions.
108
CHAPTER 4
SCENARIOS AND RESULTS
This chapter presents the experimental design and results. Six scenarios are
developed to investigate the economic consequences of a carbon emissions tax in
Pennsylvania, levied either by state or national authorities, and how those
consequences are influenced by inter‐regional labor mobility. Four additional
scenarios are developed to examine the economic effects of the carbon tax on tax
rates and migration elasticities.
4.1 Experimental Design
Counterfactual simulations are used to examine the impacts of a carbon
tax. The analysis begins with simulations that are used to explore the
implications of labor migration for the economic consequences of carbon taxes
imposed either unilaterally (i.e., not in cooperation with other states or the
national government) by the state or by the national government. Taxes imposed
unilaterally by the state will lead to labor migration, driven by changes in
relative real wages and differences in environmental quality between
Pennsylvania and other regions in simulations in which labor migration is
109
endogenous; otherwise, labor supply is constant. Taxes imposed by the national
government are assumed to produce no differentials in relative real wages or
environmental quality, and thus, not to induce changes in labor supply. State
and federal taxes also clearly differ in the recipient of tax revenues.
Next, several simulations are conducted to test if revenue recycling, in
which carbon tax revenues replace labor income tax revenues, generates a double
dividend. These tests are conducted for both state and federal taxes, with and
without endogenous labor migration. The simulations are all conducted using a
$5/ton carbon tax. The $5 rate is selected because under recycling it results in
revenue neutrality, a norm for comparing the effects of alternative types of taxes.
Under revenue neutrality, economic outcomes reflect the effects of the tax
substitution alone; that is, there are no effects resulting from changes in the
overall level of spending.
While revenue neutrality is appropriate for analyzing the consequences of
substituting a carbon tax for labor income taxes, carbon taxes with higher rates
than $5/ton rate are of interest. Additional simulations are performed to explore
the impacts of alternative tax rates, specifically $10/ton and $15/ton. The tax
rates and procedures used to model carbon taxes are discussed further in the
next section. Because of the novelty of the endogenous migration analysis and
uncertainty about the impacts of real wage and environmental differentials on
110
migration, simulations are conducted to explore the sensitivity of the results to
migration elasticities.
Table 4.1 summarizes the main components of the ten scenarios.
Table 4.1 Scenarios Scenario Tax
Authority Revenue Recycling
EndogenousLabor Migration
Carbon Tax Rate
Wage Elasticity of Migration
Amenity Elasticity of Migration
1 PA No No $5/t 0.017 0.025
2 PA No Yes $5/t 0.017 0.025
3 PA Yes No $5/t 0.017 0.025
4 PA Yes Yes $5/t 0.017 0.025
5 Federal Yes No $5/t 0.017 0.025
6 Federal Yes Yes $5/t 0.017 0.025
7 PA No No $10/t 0.017 0.025
8 PA No No $15/t 0.017 0.025
9 PA No Yes $5/t 0.01 0.02
10 PA No Yes $5/t 0.02 0.03
4.2 Implementation of Carbon Taxes
Since carbon emissions are not modeled explicitly, carbon taxes are
modeled as equivalent taxes on the carbon content of fossil fuel consumption.
The procedure follows Boyd et al. (1995), Li and Rose (1995), and Kamat et al.
(1999) to convert taxes on the carbon contained in fossil fuels, the combustion of
111
which generates carbon emissions, into ad‐valorem taxes on fuels. In the model,
fuel consumption occurs directly in the production of goods; accordingly, the
taxes are imposed on the intermediate demand for fossil fuels and electricity.
Specifically, following Boyd et al. (1995), ad‐valorem tax rates are obtained
as.
Ad valorem tax rate =prices Fuel
esCarbon taxcontentCarbon × (4.1)
The carbon contents of coal, oil, gas, and electricity are, respectively, 0.605
tons/short ton, 0.1299 tons/barrel of oil, 0.0163 tons/cubic feet, and 0.574
tons/MWh13 (Boyd et al., 1995). The prices of coal, oil, natural gas, and electricity
used to compute the tax rates are, respectively, $23.5/short ton, $43.68/barrel,
$6.05/cubic feet, and $68.1/MWh (see Table B in the Appendix for sources of
fossil fuel prices). Table 4.2 shows the resulting ad‐valorem tax rates for carbon
taxes ranging from $5/ton to $15/ ton. As noted above, this study uses $5/t of
carbon tax in most scenarios to maintain revenue neutrality.
13 Energy information administration website provides a carbon content of electricity sector since electricity is generated from various fossil fuels including coal, gas, and oil. More details are found at http://www.eia.doe.gov/oiaf/1605/ee-factors.html (accessed on April. 2005).
112
Table 4.2 Carbon Tax and Equivalent Ad‐Valorem Fuel Tax Rates Carbon tax ×Carbon Content by
Fuel Type (A) Equivalent Ad-valorem Fuel Tax
(A / Fuel Price) Carbon Tax ($/Ton)
Coal Oil Gas Electricity Coal Tax
Oil Tax
Gas Tax
Electricity Tax
5 3.025 0.650 0.082 2.870 0.129 0.015 0.013 0.042
10 6.050 1.299 0.163 5.740 0.257 0.030 0.027 0.084
15 9.075 1.949 0.245 8.610 0.386 0.045 0.040 0.126
Table 4.3 presents carbon tax revenue by fuel types and total carbon tax
revenue before the model is simulated. Most revenue is from the electricity sector
and the portion of revenue from the gas sector in the total revenue is the smallest.
Table 4.3 Tax Revenue of Carbon Taxes by Fuel Type ($ Million) Carbon Rate
Coal Tax Revenue
Oil Tax Revenue
Gas Tax Revenue
Electricity Tax Revenue
Total Revenue
$5/ton 74.63 12.98 11.67 250.9 350.2
$10/ton 149.2 25.95 23.3 501.9 700.5
$15/ton 223.9 38.9 35.0 752.9 1050.7
4.3 Welfare Measurement
Welfare consequences of carbon taxes will result from changes in
environmental quality and economic variables (Freeman, 1999). As in most
previous studies, this analysis is limited to the welfare consequences of price and
income changes, thus excluding welfare impacts of changes from environmental
113
quality improvements. This is accomplished by assuming that market goods are
strongly separable from environmental quality in consumers’ utility (Schwartz
and Repetto, 2000).
Given highly limited information about the benefits of reduced air emissions
from the combustion of fossil fuels, research on the double dividend assumes
that environmental quality is strongly separable from market goods and leisure
in consumer preferences. This assumption means that household demands for
goods and supply of labor are unaffected by environmental quality; this study
follows in that tradition.
Compensating variation (CV) is commonly used in CGE studies to measure
changes in economic welfare change due to changes in prices and incomes, and is
adopted for this study. The CV of an economic change is the amount of money
that can be taken away from an economic agent after the change such that the
agent’s utility after the change is the same as before (Freeman, 1999). CV has a
positive sign for economic changes that improve welfare and negative for
changes that decrease welfare.
Following standard procedure for cases in which welfare is affected by both
changes in prices and wages, CV is computed using a ‘pseudo expenditure’
function (Freeman, 1999; Hisnanick and Coddington, 2000). A standard result is
114
that the pseudo expenditure function can be derived by inverting the indirect
utility function.
Given the CES utility specification, the pseudo expenditure is derived from
utility maximization subject to budget and time constraints:
⎥⎦
⎤⎢⎣
⎡+ )ln(1Max ρρ
ρRC
subject to PCRTw =−+ )(M* and T = L + R
where U is the household’s utility level, R is leisure, C is consumption of
commodities, M* is the household’s exogenous income level (which is the sum of
other factor incomes and transfers from government), w is the after‐tax wage rate,
T is the total endowment of time, ρ is an exponent related to the elasticity of
substitution (σ), P is the consumer price index, and L is labor supply.
The first order conditions of utility maximization are
0)ln(1 =−+=∂Γ∂ − λρρρ PRCCC
(4.2)
0)ln(1 =−+=∂Γ∂ − λρρρ wRCRR
(4.3)
0* =−−+=∂Γ∂ PCwRwTMλ
(4.4)
where Γ denotes a Lagrangian equation of the utility maximization problem, and λ is the
Lagrange multiplier.
Dividing equation (4.2) by equation (4.3) yields
115
wP
RC
=−1)( ρ , thus *)1/(1* )( RwPC −= ρ (4.5)
Plugging equation (4.5) into equation (4.4) and rearranging terms provides the
Mashallian demand for commodities and leisure.
σσ
σ
PwwTMwR
++
=− )( *1
* , σσ
σ
PwwTMPC
++
=− )( *1
* , where 1−
=ρρσ (4.6)
Using the derived Mashallian demands in equation (4.6), the indirect utility
function (V) can be derived as
)()(})(
))(({),,( */11*
wTMwPPw
wTMwPTMPV ++=+
++= − σσσρ
ρσσ
ρσσ
(4.7)
Solving the indirect utility function with regard to exogenous household income
yields the pseudo‐expenditure function (Just et al., 1986).
wTVwPTVwPe −+= σσσ /1* )(),,,( (4.8)
This function is used to compute the compensating variation of wage and
price changes using the definition
),,,(),,,( 0011*0000* TUwPeTUwPeCV −=
Note that in this formulation we do not consider the welfare impacts of a
change in the per capita labor endowment, T, although we do consider changes
in the aggregate labor endowment with labor migration. CV is calculated on a
per capita basis to exclude changes in the economy‐wide time endowment from
CV calculations.
116
4.4. Results
4.4.1 $5/t State Carbon Tax without Revenue Recycling
Carbon taxes levied at the modest rate of $5/ton have small and primarily
adverse effects on economic welfare without revenue recycling. The impacts are
less in scenarios with labor migration than in those without labor migration.
Carbon taxes increase production costs by increasing the costs of fossil fuels.
Subsequently, price increases are passed on to consumers, reducing consumer
welfare. In‐migration reduces cost increases by facilitating substitution of labor
for fossil fuels.
Household welfare is reduced: the per capita CV of a $5/ton of carbon tax
when the revenue is not recycled is ‐$0.7 without labor migration and is ‐$0.4
with endogenous labor migration (table 4.4). As shown in table 4.6, there is little
change in the after‐tax wage, but there is a small increase in the consumer price
index for both the cases of no labor migration and labor in‐migration. The per
capita welfare effect (CV) of the $5/ton carbon tax without revenue recycling is
negative since household income does not change, but the cost of living increases
slightly. The effect of labor migration on per capita CV is positive even though
the difference is very small.
117
The gross state product (GSP) declines by 0.16% for the carbon tax without
labor migration and 0.06% for the carbon tax with the labor migration. Overall,
the effect of labor in‐migration is positive on the Pennsylvania economy. It is
possible to explain why in‐migration affected the GSP positively in several ways,
including the price, substitution, output, and trading effects.
Total foreign import and domestic import increase by 1.29% and 1.47% for
the carbon tax policy without labor migration, and increase by 1.55% and 1.17%
for the carbon tax policy with the labor in‐migration. On the other hand, the total
intermediate demands for other materials increase by 0.32% for the carbon tax
policy without labor migration, and also increase by 0.5% for the carbon tax
policy with the labor in‐migration.
Hence, import and intermediate input sectors are affected positively by the
carbon tax policy, while the demand for energy input sectors decreases due to
the fuel taxes on fossil fuels and electricity. This change can be explained by the
effect of substitution among imports, other material inputs, and energy inputs.
The increase in the cost of purchasing energy inputs lowered the relative costs of
purchasing imported goods and other intermediate inputs; in this way,
industries could mitigate the burden of the carbon tax.
The demand for the coal industry declines substantially by 4.7% and 4.1%
relative to other fossil fuels, reflecting the higher carbon content of coal. The
118
demand for the electricity industry drops by 1.1 % and 1.5%. The demand for the
oil industry decreases slightly, while the natural gas demand increases by 0.6%
and 1.1%. The tax rate on gas is the lowest among fossil fuel taxes, owing to the
low carbon content of gas, resulting in its substitution for other fuels. The
demand for the alternative fuel industries increases by 1.3% and 0.9%, since the
relative costs of purchasing alternative fuels are lower than those of fossil fuels.
119
Table 4.4 Relative Changes in Major Economic Variables for The $5/T Carbon Tax without Revenue Recycling (%)
Scenario $5/t Carbon Tax Without Revenue Recycling
Economic Variable No Labor Migration
Endogenous Labor migration
Gross CV -3.44 ($million) -1.77 ($million)
Per capita CV -0.72($) -0.37 ($)
GSP -0.16 -0.06
Total final consumption -0.002 -0.001
Total foreign import 1.29 1.55
Total domestic import 1.47 1.17
Total foreign export 1.17 1.39
Total domestic export 0.97 1.00
Labor supply 0.0 +
Labor demand 0.0 0.0
After tax wage rate + +
Total demand for fossil fuels and electricity -1.55 -1.16
Consumer price index 0.005 0.002
Labor migration 0.00 +
Final consumption on fossil fuels and electricity -2.30 -2.29
Demand for leisure and commodity - -
Total intermediate demand for fossil fuels -1.426 -0.983
Intermediate demand for coal -4.73 -4.10
Intermediate demand for gas 0.56 1.10
Intermediate demand for oil -0.54 -0.03
Intermediate demand for alternative fuels 1.32 0.87
Intermediate demand for electricity -1.52 -1.12
Total intermediate demand for materials 0.32 0.5
Exogenous income + +
(+ represents very small increase and – denotes very small decrease.)
120
4.4.2 $5/t State Carbon Tax with Revenue Recycling
Carbon taxes with revenue recycling increase welfare, with the gains being
greater in the cases without labor in‐migration than in those with labor in‐
migration. In‐migration supplies labor to the PA labor market leading to the
reduction in the wage rate. Thus, per capita labor income of the PA household
decreases. Given that other household exogenous income does not change,
households in the case with the in‐migration have less purchasing power
resulting in less welfare gains.
Household welfare increases: the per capita CV of the $5/ton carbon tax with
recycling is $19.0 with no labor migration, and $18.0 with labor in‐migration
(table 4.5). The double dividend hypothesis is supported for both cases, since per
capita CVs are positive.
Changes in the after‐tax wage rate and consumer prices determine the sign
and magnitude of the per capita CV. After‐tax wage rates increased by 0.21% and
0.198% and the consumer price index decreased by 0.07% and 0.08% for both
cases. The increase in the after‐tax wage rate due to the reduction in the labor
income tax raises household income given that exogenous incomes are
unchanged. The income gains encourage household consumption and
production. When labor in‐migrates to Pennsylvania, the increase in the after‐tax
wage rate enlarges the income gains, leading to even more household
121
consumption. The amplified consumer demand due to labor in‐migration raises
the consumer price and expands the output level. Price increases induced by
increased production costs are not large enough to offset the consumer gains
from wage increases.
The GSP decreases by 0.03% for the case without migration and 0.3% for the
case with migration. Total consumption does not change for both cases, so there
is no effect from the demand side, but there is significant change in the exports.
When there is no labor migration, total foreign and domestic exports increase by
0.24% and 0.14%. For the case with labor in‐migration, total foreign and domestic
exports increase by 0.02% and 0.9%. Thus, it is not clear in which case the output
increases from exports are larger.
Total demand for fossil fuels decreases by 0.6% for the case without
migration and by 1.2% for the case with the migration. Industries substitute
materials and imports for fossil fuels and electricity. Total demand for materials
increases by 0.2% for the case without migration and by 0.27% for the case with
migration. Total foreign and domestic imports increase by 0.27% and 0.17% for
the case without migration, and by 0.04% and 1.97% for the case with migration.
Hence, import and material inputs substitute for energy inputs.
Again, there are changes in energy portfolios due to the carbon tax and
changes are analogous to those in the previous section. For example, coal is again
122
the most negatively affected fossil fuel because the coal tax rate is the highest.
The demand for gas increases by 0.6% and 0.5% for two scenarios.
Table 4.5 Relative Changes in Economic Variables for The $5/T Carbon Tax with
the Tax Revenue Recycling
Scenario $5/T Carbon Tax With The Tax Revenue Recycling
Economic Variable No Labor Migration Endogenous Labor migration
Gross CV 92.0 ($million) 87.5($million)
Per capita CV 19.276($) 18.317($)
GSP -0.033 -0.319
Total foreign import 0.271 0.039
Total domestic import 0.172 1.972
Total foreign export 0.236 0.019
Total domestic export 0.138 0.896
Total demand for fossil fuels and electricity -0.857 -1.368
Labor in-migration 0.000 0.003
Final consumption on fossil fuels and electricity -2.341 -2.338
Demand for leisure and commodities -0.027 -0.027
Intermediate demand for coal -4.085 -4.340
Intermediate demand for gas 0.628 0.500
Intermediate demand for oil -0.396 -0.041
Intermediate demand for alternative fuels 1.041 0.403
Intermediate demand for electricity -0.475 -1.327
Consumer price index -0.067 -0.079
Demand for leisure -0.084 -0.084
After tax wage rate 0.211 0.198
Labor supply 0.063 0.059
Labor demand -0.101 -0.103
Total intermediate demand for fossil fuels -0.604 -1.203
Total final consumption - -
Total intermediate demand for materials 0.202 0.271
Household exogenous income - -
(‐ denotes very small decrease.)
123
4.4.3 $5/t Federal Carbon Tax with Revenue Recycling
Results for a national carbon tax are generally analogous to those of a state
tax both with and without endogenous migration, although the magnitudes of
welfare changes are relatively lower. Analytically, the main difference between
state carbon tax and federal carbon tax is on the sign and magnitude of labor
migration since the environmental quality differential variable is assumed to
have no significant effect on the decision of labor migration in the case of federal
carbon tax. However, the result shows little changes in labor migration for the
scenarios with state and federal carbon tax. Thus, the only difference between the
state and federal tax scenarios is the tax revenue. This difference could cause
small changes in welfare and other economic variables.
Compared to the scenarios with the state carbon tax, changes in the wage
rate are not different and consumer price decreases less in the scenarios with the
federal tax. Therefore, the relatively smaller gains from lowered living cost result
in less improvement in the welfare effect of the scenarios with the federal tax.
These results suggest that the use of a state carbon tax with the revenue recycling
policy generates more welfare gains than that of a national carbon tax.
124
Table 4.6 Relative Changes in Major Economic Variables for the $5/T of Federal Carbon Tax with the Tax Revenue Recycling Scenario $5/T Of Federal Carbon Tax with the
Tax Revenue Recycling Economic Variable No Labor Migration Endogenous
Labor migrationgross CV 84.15 ($million) 71.56($million)
Per capita CV 17.6($) 15.0($)
GSP -0.077 -0.293
Total foreign import -0.1 0.678
Total domestic import 1.202 -6.174
Total foreign export -0.159 0.967
Total domestic export 0.919 -2.826
Labor demand -0.1 -0.102
Labor supply 0.064 0.06
After tax wage rate 0.212 0.2
Total demand for fossil fuels and electricity -1.622 -2.11
Labor in-migration 0 0.003
Final consumption on fossil fuels and electricity -2.353 -1.58
Demand for leisure and commodities -0.031 -0.033
Intermediate demand for coal -4.142 -4.398
Intermediate demand for gas 0.565 -0.268
Intermediate demand for oil 0.612 -0.403
Intermediate demand for alternative fuels -0.374 -0.461
Intermediate demand for electricity -1.849 -2.53
Consumer price index -0.053 -0.05
Household exogenous income -0.003 -0.003
Total final consumption -0.005 -0.01
Total intermediate demand for fossil fuels -1.498 -2.2
Total intermediate demand for materials 0.436 -0.08
125
4.4.4. Alternative Carbon Tax Rates
The results above assume a $5/ton carbon tax to maintain approximate
revenue neutrality in the case of PA tax substitution. However, carbon taxes
needed to achieve targets established by international agreements are likely to be
higher (Kamat et al., 1999). Accordingly, it is relevant to explore the
consequences of higher tax rates. Alternatives of $10 and $15 per ton are
considered for the case without labor migration and no revenue recycling.
In both cases there is little adjustment in the labor market because there is no
change in the labor income tax. The effect on labor migration is very small
because the carbon tax lowers the after‐tax real wage rate, which reduces the
effect of environmental improvement on migration. There is a large difference in
the relative changes of consumer prices between the scenario with $10/ton and
the scenario with $15/ton (in Table 4.7). The consumer price index for the
scenario with $15/ton increases by 0.09%, while it increases by 0.01% for the
scenario with $10/ton. Final consumption decreases by 0.028% for the scenario
with $15/ton, while it decreases by 0.003% for the scenario with $10/ton due to
the increase in the consumer price index. As the carbon tax is imposed on the
demand for fossil fuels, the increased production costs raise the consumer price
index.
126
On the whole, the demands for coal, electricity, and oil decline more in the
scenario with $15/ton than in the scenario with $10/ton of carbon. The demand
for gas increases more in the scenario with the $15/ton than the $10/ton of carbon,
but the demand for alternative fuels does not increase more in the scenario with
$15/ton than the $10/ton of carbon. The GSP for the case with $15/ton is larger
than with the $10/ton of carbon. Thus, this simulation shows that as the carbon
tax increases, the economy is affected more adversely.
127
Table 4.7 Relative Changes in Major Economic Variables for the $10/t and $15/t of Carbon Taxes without Revenue Recycling Scenario Different Carbon Tax Rates without
Revenue Recycling and No MigrationEconomic Variable $10/t Carbon Tax $15/t Carbon Tax Gross CV -5.83 -48.4
Per capita CV -1.2 -10.1
GSP -0.158 -0.458
Total foreign import 1.30 -0.19
Total domestic import 1.67 4.68
Total foreign export 1.19 0.14
Total domestic export 1.189 3.32
Labor demand - -
Labor supply + +
After tax wage rate + +
Total demand for fossil fuels and electricity -1.679 -4.473
Labor in-migration + +
Final consumption on fossil fuels and electricity -4.491 -6.523
Demand for leisure and commodities -0.002 -0.02
Total final consumption -0.003 -0.028
Consumer price index 0.011 0.09
Intermediate demand for coal -6.793 -12.976
Intermediate demand for gas 0.822 1.298
Intermediate demand for oil -0.748 -0.559
Intermediate demand for alternative fuels 2.703 0.143
Intermediate demand for electricity -1.017 -4.573
Household exogenous income + +
Total intermediate demand for fossil fuels -1.201 -4.124
Total intermediate demand for materials 0.442 1.085
In addition to the relative changes in economic variables, the amount of
mitigated carbon emission for different carbon tax rates is calculated and
compared to the total carbon emission level in Pennsylvania.
128
In 1999, the total amount of carbon emissions in Pennsylvania was
79.8MMTCE and the amount of emissions in 1990 was 77.4MMTCE. Based on the
$5/t of carbon charge in the first scenario, the amount of reduction in the total
carbon emission was 1.714MMTCE. Therefore, the total amount of the carbon
emission reduction due to the carbon tax is 2.15%.
When the carbon charge increases to $10/t, the total amount of carbon
emissions decreases to 77.8MMTCE (2.5% reduction). Thus, Pennsylvania can
maintain its carbon emission level at 1990 with a $10 of carbon charge per ton.
Tables 4.8, 4.9, and 4.10 show how the amount of reduction in carbon
emissions can be calculated for three carbon tax rates. First, intermediate and
final demands for coal, gas, oil, and electricity are calculated for the three
different carbon taxes. Second, the sum of intermediate and final demands in the
value term is divided by the fuel price per ton, which produces the quantity of
reduction in total demand for fossil fuels. Third, conversion factors for each fossil
fuel type are multiplied by the quantity of each fuel demand, which provides the
reduction in carbon emissions for each fuel type.
129
Table 4.8 Reduction in the Carbon Emission for $5 of Carbon Tax Fossil Fuel
Type
Intermediate
Demand
($Million)
Final
Consumption
($Million)
Total
($Million)
Conversion
Factor
Fuel
Price
($)
Quantity
(ton)
Carbon
(MTCE)
Carbon
(MMTCE)
coal -27.429 -0.045 -27.47 0.605 23.5 -1169106 -707309 -0.70
gas 4.814 -4.988 -0.174 0.0163 6.05 -28760.3 -468.793 -0.0005
oil -4.739 -1.117 -5.856 0.1299 43.68 -134066 -17415.2 -0.0174
electricity -90.67 -26.646 -117.3 0.574 68.1 -1722702 -988831 -0.9888
total -150.8 total -1.7140
Table 4.9 Reduction in the Carbon Emission for $10 of Carbon Tax Fossil Fuel
Type
Intermediate
Demand
($Million)
Final
Consumption
($Million)
Total
($Million)
Conversion
Factor
Fuel
Price
($)
Quantity
(ton)
Carbon
(MTCE)
Carbon
(MMTCE)
coal -39.388 -0.082 -39.47 0.605 23.5 -1679574 -1016143 -1.016
gas 7.117 -9.874 -2.757 0.0163 6.05 -455702 -7427.95 -0.007
oil -6.527 -2.208 -8.735 0.1299 43.68 -199977 -25977 -0.026
electricity -60.541 -51.627 -112.17 0.574 68.1 -1647107 -945440 -0.945
total -163.13 total -1.995
Table 4.10 Reduction in the Carbon Emission for $15 of Carbon Tax Fossil Fuel
Type
Intermediate
Demand
($Million)
Final
Consumption
($Million)
Total
($Million)
Conversion
Factor
Fuel
Price
($)
Quantity
(ton)
Carbon
(MTCE)
Carbon
(MMTCE)
coal -75.233 -0.114 -75.35 0.605 23.5 -3206255 -1939784 -1.94
gas 11.236 -14.525 -3.289 0.0163 6.05 -543636 -8861.27 -0.0089
oil -4.876 -3.247 -8.123 0.1299 43.68 -185966 -24157 -0.024
electricity -272.325 -74.878 -347.2 0.574 68.1 -5098429 -2926498 -2.93
total -433.96 total -4.899
130
4.4.5 Sensitivity Analysis: Migration Elasticities
The results above with endogenous labor migration have assumed that the
elasticity of the wage rate is 0.017 and that of environmental amenity is 0.025.
Given limited information to support these assumptions, it is important to
explore the sensitivity of the model to alternative values. The alternatives
considered are lower and higher elasticities (0.01 and 0.02) in combination with a
carbon tax of $5/t and no revenue recycling.
There is little difference in the labor in‐migration between the low migration
elasticity and high migration elasticity cases, so the sensitivity of the model to the
changes in the migration elasticity must be very small. There is little difference
between the per capita CV. Except for the within the trading sector, there are
little differences between the two cases in most economic variables. When the
migration elasticities are low, foreign import and export are affected negatively,
while all exports and imports increase in the case of high migration elasticities.
Due to the change in the trading sector, the GSP has different changes for the two
cases. Therefore, the model does not respond sensitively to the change in
migration elasticities.
131
Table 4.11 Relative Changes in Major Economic Variables for the Sensitivity of Migration Elasticities on $5/t of Carbon Taxes without the Tax Revenue Recycling Scenario Sensitivity of Different Migration
Elasticities Economic Variable Low Migration
ElasticityHigh Migration
Elasticitygross CV -3.3 -1.4
Per capita CV -0.7 -0.3
GSP -0.29 -0.06
Total foreign import -0.28 1.51
Total domestic import 2.48 1.14
Total foreign export -0.22 1.36
Total domestic export 1.739 0.986
Labor demand - -
Labor supply + +
After tax wage rate + +
Total demand for fossil fuels and
electricity -1.15 -1.19
Labor in-migration + +
Final consumption on fossil fuels
and electricity -2.29 -2.29
Demand for leisure and commodities -0.001 -
Total final consumption - -
Consumer price index 0.006 0.003
Intermediate demand for coal -4.064 -4.101
Intermediate demand for gas 1.128 1.031
Intermediate demand for oil -0.054 -0.045
Intermediate demand for alternative
fuels 0.909 0.814
Intermediate demand for electricity -1.09 -1.14
Household exogenous income + +
Total intermediate demand for fossil
fuels -0.957 -1.005
Total intermediate demand for
materials 0.521 0.485
132
CHAPTER 5
CONCLUSIONS
5.1 Summary and Main Findings
This study explores the economic consequences of carbon taxes in
Pennsylvania. Key questions addressed are the effects of labor migration
(induced by changes in after‐tax wages) and environmental quality in
Pennsylvania (relative to other regions) on the economic consequences of the
carbon taxes. Further, the study explores how the level of government (i.e.,
either state or national) that levies the tax affects the economic outcomes. These
questions are motivated by current interest in carbon taxes at both the state and
federal levels, and the need for research that addresses the impacts of carbon
taxes in sub‐national economies. The innovative feature of this study is to test
the double dividend hypothesis at the sub‐national level using a model that
incorporates interregional labor mobility.
A static CGE model of the Pennsylvania economy is developed for the
analysis. Ten different scenarios are simulated to examine changes in economic
133
welfare, prices, consumption, gross state product, and trade variables. The
scenarios differ in their assumptions about the effects of wage and environmental
quality change on labor migration, the tax authority levying the tax (state or
national), the tax rates considered, and the use of revenues (deficit reduction or
revenue recycling).
A key finding of this study is that a double dividend results from the
substitution of a carbon tax for a labor income tax in Pennsylvania. Specifically,
a $5/ton carbon tax levied at the state level produces a per capita welfare gain of
$19 without labor in‐migration, and $18 with in‐migration. Thus, the negative
welfare cost from the tax interaction effect is dominated by the positive welfare
gain from the tax revenue recycling effect. The relative changes in the after‐tax
wage rate and the consumer price index explain the result. The after‐tax wage
rate increases and the consumer price index decreases. Thus, consumers enjoy
higher incomes and lower prices. The lower gains with labor in‐migration occur
because in‐migration increases the labor supply and diminishes the magnitude of
the after‐tax wage gain.
A double dividend is also found for the national tax, although the welfare
gain is smaller in this case ($17.6 without labor in‐migration and $15.0 with labor
migration). The relative increases in the after‐tax wage rate in the state tax
scenarios are larger than those in the federal tax scenarios. Also, decreases in the
134
consumer price index is greater in the state tax scenarios than in the federal tax
scenarios. The difference in the tax revenue system between the state and federal
tax authorities possibly affects the result. But overall economic impacts are not
considerably different between the state tax and the federal tax scenarios.
Another key finding is that the carbon taxes in all simulations induce
substitution of 1) non‐energy and alternative energy inputs for fossil fuels, and 2)
fossil fuels with lower carbon content for fuels with higher carbon content. The
result is a reduction in fossil fuel consumption except for natural gas, the
consumption of which (because of its comparatively low carbon) increases. The
state carbon taxes also result in a substitution of imports from other regions of
the U.S. and from foreign countries for goods produced in Pennsylvania. These
substitutions help to limit production cost increases resulting from the carbon tax
and upward pressure on prices. In all scenarios, the Gross State Product (GSP) of
Pennsylvania is affected minimally.
Finally, as the carbon tax rates increase from $5/ton to $10/ton and $15/ton,
the per capita welfare gain, GSP, and intermediate demand for fossil fuels
decrease more than in previous scenarios. For low and high elasticities of after
tax‐wage and environmental quality on labor migration, the model does not
respond sensitively.
135
5.2 Further Study
One limitation of this study that could be usefully addressed in subsequent
research is the assumption of non‐separable environmental effects. There are
several theoretical studies regarding the non‐separability of the environmental
effect (Schwartz and Repetto, 2000; Williams, 2002 and 2003). However, there are
few empirical studies on this issue. It will be quite interesting if the non‐
separability of the environmental effect is assumed in the household utility, since
the environmental quality variable will affect the demand for leisure as well as
final consumption resulting in changes in welfare gains and other economic
variables.
Second, capital mobility could be included as an endogenous variable in the
model, since capital mobility also can affect welfare consequences and other
economic variables of environmental taxes through changes in the capital supply
and demand market.
Third, in this study, alternative fuels including nuclear power, hydro
power, fuel cells, geo‐thermal, wind, bio‐fuel and solar energy are combined into
one sector for simplicity of analysis. However, production technology and costs
are different among fuel types. Thus, a combined alternative fuel sector could be
disaggregated, by the different production costs and technologies. In the future,
as the portion of alternative fuels in the primary energy supply is expected to
136
increase, disaggregated alternative energy sectors may affect the CGE modeling
significantly.
Fourth, environmental taxation without taxes on imported goods that use
fossil fuels as intermediate inputs can lead to increased demand for imports of
fossil fuel‐intensive goods. This study shows that industries substituted
imported goods for regionally produced goods to lower the increased production
costs due to the carbon tax. This result can have a bias in favor of imported
goods; the bias can be removed if the simulation includes carbon taxes on the
import of fossil fuel‐intensive goods. To generate meaningful results, the model
will require more information on imported goods, including detailed import
structure for each industry.
137
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APPENDIX A: Number of Equations and Variables Table A.1 Count of Independent Equations and Endogenous Variables
Equation Number Variable Number Fixed variablePRIVAPRICE(I) 7 PVA(I) 7 .
RXCOST(I) 7 CX(I) 7 . VADEMAND(I) 7 VA(I) 7 . OIMDEMAND(I) 7 OIM(I) 7 .
OIMCOST(I) 7 COIM(I) 7 . OIMPRICE(I) 7 POIM(I) 7 .
TRANDEMAND(I) 7 DTRAN(I) 7 . MATDEMAND(I) 7 DMAT(I) 7 .
VACOST(I) 7 CVA(I) 7 . LFDEMAND(I) 7 LF(I) 7 . KEDEMAND(I) 7 KE(I) 7 .
LFCOST(I) 7 CLF(I) 7 . LDEMAND(I) 7 LD(I) 7 . FDEMAND(I) 7 FD(I) 7 . KECOST(I) 7 CKE(I) 7 .
KDEMAND(I) 7 KD(I) 7 . EDEMAND(I) 7 ENER(I) 7 .
ECOST(I) 7 CEN(I) 7 . DFUELC(EN,WCOA) 3 FUEL("COAL",WCOA) 3 . DFUELG(EN,WGAS) 5 FUEL("GAS",WGAS) 5 . DFUELO(EN,WOIL) 5 FUEL("OIL",WOIL) 5 . DFUELA(EN,WALT) 3 FUEL("ALTF",WALT) 3 . DFUELE(EN,WELE) 6 FUEL("ELEC",WELE) 6 .
TFUELC 1 TCOAL 1 . TFUELG 1 TGAS 1 . TFUELO 1 TOIL 1 . TFUELA 1 TALTF 1 . TFUELE 1 TELEC 1 .
CESUTIL1(HH) 1 UTIL(HH) 1 . CESUTIL2(HH) 1 CT(H) 1 .
EUTIL1(HH) 1 PU(HH) 1 . EUTIL2(HH) 1 PCT(HH) 1 .
VIRTUALHHY(HH) 1 VHHY(HH) 1 . INDU1(HH) 1 V1(HH) 1 . INDU2(HH) 1 V2(HH) 1 . LEDEMAND 1 LE 1 .
CTDEMAND(HH) 1 CT(HH) 1 . CDEMAND(NFEN,HH) 4 IRC(NFEN,HH) 4 .
DFOSSIL(FEN,HH) 3 IRC(FEN,HH) 3 . TFOSSIL 1 CE 1 .
155
Table A.1 (Contd.) Count of Independent Equations and Endogenous Variables Equation Number Variable Number Fixed variable
HHFIMPORT(I,HH) 7 HHFIMP(I,HH) 7 . HHDIMPORT(I,HH) 7 HHDIMP(I,HH) 7 .
LSUPPLY 1 LS 1 . LMOBILITY 1 LMG 1 . GLSUPPLY 1 GLS 1 .
FROW 1 PADJF 1 . FRUS 1 PADJD 1 . KROW 1 CADJF 1 . KRUS 1 CADJD 1 .
TOTENV 1 TED 1 . AMENITY 1 AMEN 1 .
NETWAGE 1 W 1 . NETPP 1 PPNET 1 .
NETRENT 1 R 1 . LABINCOM 1 LABY 1 .
PROPINCOM 1 PROPY 1 . CAPINCOM 1 CAPY 1 .
CAPDEP 1 DEP 1 . ENTINCOM 1 ENTY 1 . ENTSAVE 1 RET 1 .
HHENINCOM 1 HHENTY 1 . INCOME(HH) 1 HHY(HH) 1 .
HHSAVINGS(HH) 1 HHSAVING(HH) 1 . HHDEBT(HH) 1 HHBOR(HH) 1 FIXED HHREMF(HH) 1 HHREF(HH) 1 FIXED HHREMD(HH) 1 HHRED(HH) 1 FIXED
HHSUPPLY(HH,I) 7 HHSUP(HH,I) 7 . THHINCOME(HH) 1 THHY(HH) 1 . DPINCOME(HH) 1 HHYDP(HH) 1 .
TEXPEN(HH) 1 TEX(HH) 1 . ARMINGTONF(I) 7 Q(I) 7 .
SUPRICE(I) 7 PQ(I) 7 . FIMPORT(I) 7 FM(I) 7 .
ARMINGTOND(I) 7 DS(I) 7 . . RS(I) 7 .
DSPRICE(I) 7 PDS(I) 7 . DIMPORT(I) 7 DM(I) 7 . CETF(FEIS) 6 X(FEIS) 6 .
CETF(NFEIS) 1 X(NFEIS) 1 . DEMPRICE(FEIS) 6 PX(FEIS) 6 .
156
Table A.1 (Contd.) Count of Independent Equations and Endogenous Variables Equation Number Variable Number Fixed variable
FEXPORT(FEIS) 6 FE(FEIS) 6 . CETM(DEIS) 6 DC(DEIS) 6 .
CETM(NDEIS) 1 DC(NDEIS) 1 . DCPRICE(DEIS) 6 PDC(DEIS) 6 .
DCPRICE(NDEIS) 1 PDC(NDEIS) 1 . DEXPORT(DEIS) 6 DE(DEIS) 6 .
. . RC(NDEIS) 1 .
. . RC(DEIS) 6 . GOVSUP(GOV,I) 21 GS(GOV,I) 21 . GOVEXPF(GOV) 3 FEXTX(GOV) 3 . GOVEXPD(GOV) 3 DEXTX(GOV) 3 . INDTAX(GOV,I) 21 ITAX(GOV,I) 21 . TAXCOAL(GOV) 3 TXCOAL(GOV) 3 . TAXGAS(GOV) 3 TXGAS(GOV) 3 . TAXOIL(GOV) 3 TXOIL(GOV) 3 . SOCTAX(GOV) 3 STAX(GOV) 3 . CAPTAX(GOV) 3 CATAX(GOV) 3 . ENTTAX(GOV) 3 ENTAX(GOV) 3 .
HOUSETAX(GOV,HH) 3 HTAX(GOV,HH) 3 . PROPIETAX(GOV,HH) 3 PPRTAX(GOV) 3 .
HCARBONTAX(GOV,HH) 3 COTH(GOV,HH) 3 . CARBONTAX(GOV) 3 TCARBONTX(GOV) 3 .
FREVENUE 1 FEDREV 1 . SLREVENUE 1 NEDREV 1 . EREVENUE 1 EDUTRAN 1 FIXED
. . GTOT(GOV) 3 FIXED GOVCON(I,GOV) 21 G(I,GOV) 21 .
GOVFMFP 1 FMPFG 1 . GOVFMNEP 1 FMPNEG 1 . GOVFMEP 1 FMPEG 1 . GOVDMFP 1 DMPFG 1 .
GOVDMNEP 1 DMPNEG 1 . GOVDMEP 1 DMPEG 1 . FEXPEND 1 FEDEXP 1 .
SLEXPEND 1 NEDEXP 1 . EDEXPEND 1 EDEXP 1 . COMEQC 1 X(COAL) 1 . COMEQO 1 X(OIL) 1 . COMEQG 1 X(GAS) 1 .
157
Table A.1(Contd.) Count of Independent Equations and Endogenous Variables Equation Number Variable Number Fixed variableCOMEQA 1 X(ALTF) 1 . COMEQE 1 X(ELEC) 1 . COMEQT 1 X(TRAN) 1 . COMEQM 1 X(MAT) 1 .
TOTLS 1 TLS 1 . LABEQ 1 . 0 .
PROPEQ 1 TFS 1 FIXED CAPEQ 1 TKS 1 FIXED
INVENTORY(I) 7 INVNTR(I) 7 . TFEXPORT 1 TFE 1 . TDEXPORT 1 TDE 1 .
ROWSAVING 1 ROWSAV 1 . RUSSAVING 1 RUSSAV 1 .
TOTSAV 1 TSAV 1 . . . GOVSV 3 FIXED
INVEST 7 IT(I) 7 . TFIMPORT 1 TFM 1 . TDIMPORT 1 TDM 1 . RWDEFICIT 1 ROWDEFF 1 . RUSDEFICIT 1 RUSDEFF 1 .
TOTINV 1 TINV 1 FIXED . . GOVBR(GOV) 3 FIXED
FEDEQ 1 . . . NEDEQ 1 . . . EDEQ 1 . . .
RUSEXPEND 1 RUSEX 1 . ROWEXPEND 1 RUSEX 1 . RUSINCOME 1 RUSY 1 . ROWINCOME 1 ROWY 1 .
RUSEQ 1 . . ROWEQ 1 . .
GROSPRD 1 GSP 1 . OBJ 1 OMEGA 1 .
TOTAL 457 TOTAL 474 . FIXED VARIABLE 16 . Net endogenous variables 458 .
158
APPENDIX B: Data and Statistics
Appendix B consists of data on fossil fuel consumption and price, carbon
emission of Pennsylvania, hybrid SAM, and elasticities in production, utility,
Armington, and CET functions. Since main variables in the study are
consumptions on fossil fuels, SAM should reflect energy data from survey. To
import quantitative data in monetary SAM system, hybrid input‐output
approach was applied to SAM system.
B.1 Carbon emission and Energy consumption
In 1990, total GHGs emissions of Pennsylvania were 77.4 million metric tons
carbon equivalent (MMTCE) of GHGs in 1990, and this total increased by 3.0% to
79.8 MMTCE by 1999 (Rose et al., 2004). Pennsylvania was ranked as the fourth
state among U.S. states in the emission of CO2 in 1998 (Science Daily, 2003). In
1990, Pennsylvania emitted 1.27% of total GHGs emission of U.S. and 1.18% in
199918. From table 5.1, fossil fuels have been the largest source of CO2 emission
in Pennsylvania and the share has increased to 90% in 1999 from 71% in 1990.
18 The total U.S. GHGs emissions in 1990 was 6088 teragrams and 6752.2 teragrams in1999 (USEPA, 2005)
159
Table B.1 Changes in The Share of GHGs Emission by Emission Source (Unit: MMTCE19)
Emission Source 1990 % of 1990 1999 % of 1999
CO2 from Fossil Fuels 71.57 92.42 72.23 90.53
GHGs from Non-Energy Industrial Processes 1.92 2.48 2.95 3.70
CH4 from Oil and Natural Gas 1.43 1.85 1.47 1.84
CH4 from Coal Mining 1.98 2.56 2.27 2.84
CH4 from Domestic Animals 0.08 0.10 0.07 0.09
GHGs from Municipal Waste Management 1.25 1.61 1.16 1.45
GHGs from Manure Management 0.79 1.02 0.81 1.02
CO2 from Forestry and Land-use Change -2.40 -3.10 -2.01 -2.52
GHGs from Burning Agricultural Waste * * * *
GHGs from Municipal Waste Water 0.15 0.19 0.15 0.19
CH4 & N2O from Mobile Combustion 0.42 0.54 0.44 0.55
CH4 & N2O from Stationary Combustion 0.25 0.32 0.25 0.31
Total 77.44 100.00 79.79 100.00
(Source: Rose et al., 2004, *: data does not exist)
38% of total CO2 emission in Pennsylvania is generated from the
consumption of fossil fuels by the electricity sector, 26% of total CO2 emission is
due to the industrial use of fossil fuel, and 22% of total CO2 emission is from the
consumption of fossil fuels by the transportation sector (table 5.2).
19 MMTCE stands for million metric tons carbon equivalent.
160
Table B.2 Changes in CO2 Emission by Sectors (Unit: MMTCE) Emission Source 1990 % of 1990
Fossil Fuel
1999 % of 1999
Fossil Fuel
Residential 6.00 8.38 6.38 8.83
Commercial 3.42 4.78 3.25 4.50
Industrial 18.69 26.11 14.97 20.73
Transportation 16.03 22.40 18.71 25.90
Electricity 27.43 38.33 28.92 40.04
Total 71.57 100.00 72.23 100.00
Each industry consumes fuels including coal, oil, natural gas, and alternative
(or renewable) fuels.
Carbon taxes will be imposed on the use of fossil fuels by industries as well
as final consumption20. Table 5.3 shows consumption of fossil fuels, alternative
fuels, and primary electricity by sectors and households in the year 2000.
Proportions of demand for coal, petroleum, and natural gas are 41%, 39%, and
20%.
20 Households consume fossil fuels for heat, electricity, and driving.
161
Table B.3 Energy Consumption by Sectors (Unit: MMTCE) Energy sectors Electricity Transport Materials Final demand TOTAL
(%)
Coal
(thousand short tons)
1210.6 0 295.3 2.2 1508.1
(41.10)
Gas
(billion cubic feet)
21.3 40.2 394 272 727.5
(19.83)
Petroleum
(thousand barrels)
45.1 955.2 279.3 153.8 1433.4
(39.07)
Alternative fuels
(million kWh)
31.6 1.1 54.1 14.8 101.6
Electricity
(million kWh)
788.6 1.4 301.8 153.6 1245.4
Total fossil fuels 1277 995.4 968.6 428 3669
(Recalculated from EIA, State Energy Data 2001: Consumption)
In 1990, total emission of carbon from fossil fuels was 71.57 million metric
tons carbon equivalent (MMTCE), absorption from forest and land‐use change
was ‐2.4 MMTCE, so the net carbon emission in 1990 was 69.17 MMTCE (table
5.3). The total emission from fossil fuels increased to 72.23 MMTCE in 1999, the
absorption from forest and others diminished to 2.01 MMTCE, which made the
net emission as 70.22 MMTCE (table B.4).
162
Table B.4 GHG Inventory in Pennsylvania (Unit: MMTCE) Emission Source 1990 % of 1990 1999 % of 1999
CO2 from Fossil Fuels 71.57 92.42 72.23 90.53
GHGs from Non-Energy Industrial Processes 1.92 2.48 2.95 3.70
CH4 from Oil and Natural Gas 1.43 1.85 1.47 1.84
CH4 from Coal Mining 1.98 2.56 2.27 2.84
CH4 from Domestic Animals 0.08 0.10 0.07 0.09
GHGs from Municipal Waste Management 1.25 1.61 1.16 1.45
GHGs from Manure Management 0.79 1.02 0.81 1.02
CO2 from Forestry and Land-use Change -2.40 -3.10 -2.01 -2.52
GHGs from Municipal Waste Water 0.15 0.19 0.15 0.19
CH4 & N2O from Mobile Combustion 0.42 0.54 0.44 0.55
CH4 & N2O from Stationary Combustion 0.25 0.32 0.25 0.31
Total 77.44 100.00 79.79 100.00
(Source: Rose et al., 2004, A Greenhouse Gas Emissions Inventory for
Pennsylvania)
For the use of fossil fuels, household demand for fossil fuels takes 8.38% of
total carbon emission and industry consumption of fossil fuels takes 91.17% of
total carbon emission (Table B.5).
163
Table B.5 Carbon Emission of Pennsylvania by Emission Sources (Unit: MMTCE) Emission Source 1990 % of 1990
Fossil Fuel1999 % of 1999
Fossil Fuel
Residential 6.00 8.38 6.38 8.83
Commercial 3.42 4.78 3.25 4.50
Industrial 18.69 26.11 14.97 20.73
Transportation 16.03 22.40 18.71 25.90
Electricity 27.43 38.33 28.92 40.04
Total 71.57 100.00 72.23 100.00
(Source: Adam Rose et al., 2004, A Greenhouse Gas Emissions Inventory for
Pennsylvania)
In 2000, coal price is estimated as $ 23.5 per short ton applying ‐2.6% of
average annual percent change in coal prices (table B.6).
164
Table B.6 Pennsylvania Coal Statistics (Unit: MMTCE) Average Annual
Percent ChangeCategory 1999
Percent
Change
1998-
1999 1995-1999
1990-1999
Supply (thousand short tons)
Recoverable Reserves at Producing Mines 657,416 -15.1 -2.8 -5.8
Productive Capacity 93,770 -0.8 5 NA
Production Total 76,399 -5.7 5.5 0.9
Number of Employees/Miners 9,318 -6 1 -5.8
Producer/Distributor Stocks 2,134 -20.4 -3.8 -
Imports - - -100 -
Distribution Total 75,669 -6 5 NA
Domestic Distribution Total 68,703 -5.4 6.2 NA
Within State 36,912 -11.9 0.5 NA
To Other States 31,791 3.5 15.6 NA
Foreign Distribution Total 6,966 -11.9 -4.2 NA
Demand (thousand short tons)
Consumption Total 45,414 -16.7 -4.8 -2.5
Coal Prices (nominal dollars per short ton)
Mine Total $24.14 -6.4 -2.6 -2.4
( S o u r c e : P e n n s y l v a n i a c o a l p r o f i l e a t
h t t p : / / w w w . e i a . d o e . g o v / c n e a f / c o a l / s t a t e p r o / i m a g e m a p / p a . h t m )
The average petroleum price in year 2000 is estimated as $43.68 per barrel
from table B.7 given that 1 barrel is equal to 42 gallon.
165
Table B.7 Oil Prices in 2000, Pennsylvania (Unit: Dollar per barrel) 2000 price Regular
Gasoline Midgrade Gasoline
Premium Gasoline
Jet Fuel
Kerosene No 2 Heating
Oil
No 2 Diesel
Residual Fuel Oil
December 1.007 1.092 1.173 0.989 1.174 1.351 1.176 0.646
November 1.072 1.155 1.238 1.058 NA 1.313 1.219 0.684
October 1.08 1.159 1.24 1.012 1.233 1.272 1.188 0.683
September 1.088 1.171 1.255 1.048 1.244 1.232 1.196 0.624
August 1.043 1.127 1.216 0.867 NA 1.097 1.074 0.57
July 1.118 1.194 1.279 0.84 1.001 1.04 1.008 0.608
June 1.112 1.185 1.266 0.798 NA 1.062 1.003 0.574
May 1 1.071 1.154 0.798 1.104 1.065 1.015 0.487
April 0.972 1.05 1.13 0.776 NA 1.082 0.992 0.517
March 1.047 1.119 1.194 0.823 1.143 1.143 1.03 0.534
February 0.905 0.978 1.059 0.899 1.329 1.331 1.159 0.555
January 0.852 0.929 1.009 0.848 1.056 1.173 1.011 0.525
2000
Average
1.031 1.103 1.185 0.92 1.189 1.224 1.088 0.593
(Source: EIAʹs Petroleum Product Prices for Pennsylvania at
http://www.eia.doe.gov/emeu/states/oilprices/oilprices_pa.html)
The average price of natural gas for the year 2000 is estimated as $6.05 from table
B.8.
166
Table B.8 Natural Gas Price (Unit: Dollar Per Thousand Cubic Feet (MCF)) 2000 At City
Gate
Residential Commercial Industrial Electric
Utility
December 6.32 9.21 8.67 NA NA
November 5.62 9.19 8.27 NA NA
October 6.4 10.08 8.67 NA NA
September 6.64 10.64 7.55 NA NA
August 5.43 11.88 8.99 NA NA
July 7.85 11.38 8.43 NA NA
June 7.13 10.07 7.92 NA NA
May 6.08 9.05 7.86 NA NA
April 4 8.17 7.5 NA NA
March 4.37 7.8 7.32 NA NA
February 3.64 7.33 7.11 NA NA
January 3.44 7.27 6.77 NA NA
2000
Average
5.09 8.49 7.72 5.12 3.83
(Source: EIAʹs Natural Gas Prices for Pennsylvania at
http://www.eia.doe.gov/emeu/states/ngprices/ngprices_pa.html)
167
B.2 Hybrid SAM of Pennsylvania
The production and consumption sectors are aggregated into 7 sectors; coal,
natural gas, petroleum, alternative fuels, electricity, transportation, and all other
materials. The transaction data of industries are abstracted from IMPLAN SAM
and energy consumption data from EIA (2001).
Energy production sectors consist of fossil fuels such as coal, natural gas,
petroleum, alternative fuels, and primary electricity. Since IMPLAN SAM
provides only the monetary transaction of composite of natural gas and
petroleum, quantity based energy consumption data on coal, natural gas,
petroleum, alternative fuels, and primary electricity replace the value of
transaction. The SAM including quantity based data and monetary data is
referred to as ‘hybrid SAM’.
Since the hybrid SAM incorporates quantity terms with value terms, the
quantitative terms are changed into value terms using virtual energy prices that
rebalance the total output value (Edmonds, et al., 2004). Except energy
production sectors, all the necessary data to construct Pennsylvania SAM are
derived from IMPLAN SAM. Since the energy sector is included in the IMPLAN
SAM, the final hybrid SAM should be rebalanced to have row sum equal to
column sum using financial account (table B.9).
168
Table B.9 Hybrid SAM for Pennsylvania, 2000 (million $) Industry 1 2 3 4 5 6 7
1 AGR 692.4514 0.182929 1.3197 0.018946 0.037329 0.038103 154.6179
2 MIN 0.892361 6.346512 0.195987 4.250936 8.375659 0.355076 30.20313
3 COAL 0.003123 0.300082 238.848 0.001441 0.00284 6.614171 0.016399
4 GAS 0.391889 0.024417 0.141224 21.58802 42.53507 1.779573 1.647434
5 OIL 0.099728 0.006214 0.141224 21.58802 42.53507 1.779573 0.419241
6 ALTF 0.051041 0.00318 0 0 0 0 0.214567
7 CNT 89.158 4.924158 11.61746 4.377538 8.625104 0.681767 46.60624
8 NDMNF 765.5891 48.65092 183.379 12.59677 24.81954 6.114145 3379.983
9 DMNF 64.91789 26.60837 114.3542 6.819094 13.43572 3.727522 3943.813
10 TRAN 139.6497 16.1916 149.8173 5.568113 10.97091 4.606651 957.2496
11 UTIL 34.047 2.261181 7.154589 1.994931 3.930631 0.362209 298.3019
12 ELEC 24.93026 8.974011 36.42466 4.646153 9.154358 1.390809 25.88469
13 EGUTIL 9.812771 6.440335 7.380586 14.04208 27.66725 1.359372 9.106511
14 TRADE 390.7088 27.38198 158.897 9.279347 18.28318 5.16334 4768.473
15 FIRE 260.619 12.31639 86.93235 70.38063 138.6716 8.196248 755.9304
16 SER 162.6084 29.83615 132.8111 21.25142 41.87187 5.425711 4432.842
17 OTHR 13.44988 2.604642 6.88834 7.930749 15.62603 0.84307 106.2217
18 LABOR 960.4545 296.335 560.3034 73.59021 144.9955 21.56858 13659.13
19 PROP 878.0259 43.7474 671.4225 47.7893 94.1597 22.52345 4559.95
20 CAPITAL 796.0944 214.9972 100.3682 179.2205 353.1198 17.52063 1961.765
21 IBTAX 137.8217 28.75179 531.7034 35.35217 69.65471 17.63144 331.7308
22 HHH 0.155384 0.041677 0.086829 0.146378 0.28841 0.014444 1.322397
23 FED 8.348684 0.195031 0.180393 0.675139 1.330233 0.060527 5.994804
24 SLNED 18.22266 1.764666 7.757429 1.194845 2.354214 0.313093 257.5034
25 SLED 0 0 0 0 0 0 0
26 ENTER 0 0 0 0 0 0 0
27 SAVING 15.16375 4.085728 1.010586 146.4826 -215.444 0.074686 32.65246
28 FIMP 60.02604 11.68487 49.36001 0 116.8387 4.602287 1424.976
29 DIMP 1298.028 141.9863 660.7712 656.9709 0 36.49022 8775.967
30 TOTAL 6821.722 936.6427 3719.267 1347.756 973.8392 169.2367 49922.52
169
Table B.9 (contd.) Hybrid SAM for Pennsylvania, 2000 (million $) Industry 8 9 10 11 12 13 14
1 AGR 3259.441 40.56068 1.21256 1.278308 0.231406 0.238731 122.1353
2 MIN 27.30272 40.95146 0.632248 0.103929 0.04173 0.035266 1.071147
3 COAL 16.42212 23.76668 0 0.000564 268.7637 24.57622 0.191665
4 GAS 549.3148 2.41128 70.59499 0.072613 37.40481 133.8894 1.477616
5 OIL 139.7903 0.613625 602.1755 0.018479 28.43186 34.07234 0.376025
6 ALTF 71.54435 0.314052 1.832287 0.009457 52.63661 17.43815 0.192449
7 CNT 774.8688 646.7701 353.1061 471.2899 315.8907 374.555 417.6473
8 NDMNF 24166.35 4935.044 1739.416 214.2473 79.91937 88.51181 3065.891
9 DMNF 1007.529 13115.53 363.7318 507.4419 38.29351 22.04936 476.7338
10 TRAN 2914.011 1800.495 2909.454 109.9715 99.39574 57.67062 507.0797
11 UTIL 526.0541 442.7894 324.1168 1249.239 11.5405 25.42763 811.2817
12 ELEC 456.4047 341.7098 7.590215 0.051909 4275.46 2.053559 280.6512
13 EGUTIL 1011.687 278.234 17.44931 18.66289 70.45893 457.6327 127.3596
14 TRADE 6604.412 6589.628 660.3757 148.512 35.43342 34.76474 1857.116
15 FIRE 1803.119 1411.766 590.1395 286.5556 130.4993 78.86544 2516.614
16 SER 6979.46 4999.037 2146.394 1885.478 207.1125 208.4908 7608.693
17 OTHR 751.5548 980.2775 535.6191 377.1078 16.10424 34.13904 578.5133
18 LABOR 20874.09 25934.97 8430.853 3899.34 1883.749 335.3142 34941.88
19 PROP 3832.73 1423.071 1301.518 1000.849 388.7295 40.77285 3461.67
20 CAPITAL 16583.07 8573.603 1599.479 4429.697 5504.745 396.4923 11541.51
21 IBTAX 1345.601 987.5333 786.8546 1125.793 1392.302 208.4867 11864.42
22 HHH 13.46259 19.64976 10.56057 6.328386 0.221929 0.178965 10.92791
23 FED 73.92358 11.47081 2.405189 0.987908 0.088201 5.314521 4.34932
24 SLNED 435.7805 296.2215 115.753 104.1826 10.90327 11.04558 406.9031
25 SLED 0 0 0 0 0 0 0
26 ENTER 0 0 0 0 0 0 0
27 SAVING 1212.905 250.8056 -582.018 39.07442 -4485.54 267.4079 135.4726
28 FIMP 2433.474 4551.413 186.9112 174.6004 19.33341 352.3139 240.5746
29 DIMP 18981.12 23027.73 2820.706 2600.734 492.3187 1549.819 5324.202
30 TOTAL 116845.4 100726.4 24996.86 18651.63 10874.47 4761.557 86304.94
170
Table B.9 (contd.) Hybrid SAM for Pennsylvania, 2000 (million $) Industry 15 16 17 18 19 20 21
1 AGR 360.1538 72.57996 9.842945 0 0 0 0
2 MIN 0.16398 1.827304 0.330754 0 0 0 0
3 COAL 0.067551 0.213729 0.001025 0 0 0 0
4 GAS 0.222931 2.320605 0.128122 0 0 0 0
5 OIL 0.056732 0.59055 0.032605 0 0 0 0
6 ALTF 0.029035 0.302242 0.016687 0 0 0 0
7 CNT 2326.516 1065.596 1016.18 0 0 0 0
8 NDMNF 671.4645 5221.666 266.9423 0 0 0 0
9 DMNF 93.25926 1749.224 105.5105 0 0 0 0
10 TRAN 372.0839 990.0973 188.5838 0 0 0 0
11 UTIL 722.4167 1595.327 93.85443 0 0 0 0
12 ELEC 148.565 262.6114 68.78266 0 0 0 0
13 EGUTIL 73.13231 148.6001 81.39548 0 0 0 0
14 TRADE 309.4446 2103.482 62.63672 0 0 0 0
15 FIRE 12275.84 5441.58 218.7379 0 0 0 0
16 SER 6397.91 19170.3 480.3692 0 0 0 0
17 OTHR 756.0173 1255.726 223.6282 0 0 0 0
18 LABOR 18660.92 69172.61 35160.56 0 0 0 0
19 PROP 3100.43 10976.26 0 0 0 0 0
20 CAPITAL 44516.97 8827.998 4575.042 0 0 0 0
21 IBTAX 8773.017 2298.751 0 0 0 0 0
22 HHH 14.03352 24.23109 4.618128 206456.9 30305.47 34348.91 0
23 FED 5.905954 6.714794 0.813587 28175.77 1515.652 -4.59495 9503.534
24 SLNED 337.7874 1006.11 25.64548 355.2431 0 175.0268 20414.24
25 SLED 0 0 0 0 0 0 0
26 ENTER 0 0 0 1.189571 0 31192.16 0
27 SAVING 97.05068 171.3691 36.33838 21.57384 22.52393 46151.75 17.63136
28 FIMP 62.60349 716.2648 43.60918 0 0 444.4044 0
29 DIMP 9059.921 11948.09 651.6481 0 0 -2135.96 0
30 TOTAL 109136 144230.4 43315.25 235010.7 31843.65 110171.7 29935.41
171
Table B.9 (contd.) Hybrid SAM for Pennsylvania, 2000 (million $) Industry 22 23 24 25 26
1 AGR 917.907 1.464821 108.8253 17.87492 0
2 MIN 12.491 0.451086 2.109133 0.286465 0
3 COAL 0.488419 0.359598 0.78215 0.62605 0
4 GAS 477.6576 0.654495 1.674533 0.615129 0
5 OIL 96.95832 0.654495 1.674533 0.615129 0
6 ALTF 24.65259 0 0 0 0
7 CNT 0 1059.069 9828.295 357.8559 0
8 NDMNF 34810.53 907.4966 2213.893 805.9871 0
9 DMNF 6121.09 1356.561 363.5667 104.3414 0
10 TRAN 5091.441 213.774 319.0674 231.6867 0
11 UTIL 4512.625 99.3083 307.0221 138.1223 0
12 ELEC 832.755 98.67387 714.1659 231.0703 0
13 EGUTIL 1771.993 24.51443 164.9879 90.26202 0
14 TRADE 53630.02 294.3712 747.9015 156.8566 0
15 FIRE 46949.79 167.0269 1263.121 47.85397 0
16 SER 62849.77 1848.327 2479.513 509.222 0
17 OTHR 6679.232 6458.367 10912.31 11415.33 0
18 LABOR 0 0 0 0 0
19 PROP 0 0 0 0 0
20 CAPITAL 0 0 0 0 0
21 IBTAX 0 0 0 0 0
22 HHH 9041.059 53149.36 15003.35 249.8723 16776.29
23 FED 40753.56 11721.57 28.72335 28.06237 9382.803
24 SLNED 16232.29 11239.07 7873.267 27.9402 1693.078
25 SLED 0 0 18046.79
26 ENTER 0 1838.838 16.83147
27 SAVING 22512.55 6142.493 609.9119 635.2562 5196.848
28 FIMP 4238.239 793.0388 176.9293 50.52653
29 DIMP 65203.32 3899.424 4888.283 2946.522
30 TOTAL 382760.4 101314.9 76073 18046.79 33049.02
172
Table B.9 (contd.) Hybrid SAM for Pennsylvania, 2000 (million $) Industry 27 28 29 30
1 AGR 0.755378 285.4159 773.1373 6821.722
2 MIN 1.593274 30.08293 766.5486 936.6427
3 COAL 0.014424 411.2439 2725.963 3719.267
4 GAS 1.209683 0 0 1347.756
5 OIL 1.209683 0 0 973.8392
6 ALTF 0 0 0 169.2367
7 CNT 29269.12 0 1479.771 49922.52
8 NDMNF 1531.14 11585.1 20120.68 116845.4
9 DMNF 8558.349 21047.88 41521.6 100726.4
10 TRAN 678.1817 3582.426 3647.389 24996.86
11 UTIL 275.4214 225.8141 6943.212 18651.63
12 ELEC 0.464707 22.33481 3019.717 10874.47
13 EGUTIL 1.608157 37.28798 310.4827 4761.557
14 TRADE 4239.497 3099.799 352.5023 86304.94
15 FIRE 1213.111 2715.577 30692.73 109136
16 SER 1082.476 1080.783 19470.46 144230.4
17 OTHR 352.7296 1177.593 657.4315 43315.25
18 LABOR 0 0 0 235010.7
19 PROP 0 0 0 31843.65
20 CAPITAL 0 0 0 110171.7
21 IBTAX 0 0 0 29935.41
22 HHH 14242.81 24.20365 3055.935 382760.4
23 FED 5.02202 21.77462 54.23493 101314.9
24 SLNED 13889.02 78.26553 1056.116 76073
25 SLED 0 0 0 18046.79
26 ENTER 0 0 0 33049.02
27 SAVING 3297.463 185.1119 39978.91 121898.9
28 FIMP 29458.97 0 0 45610.69
29 DIMP 13798.75 0 0 176626.8
30 TOTAL 121898.9 45610.69 176626.8 2086076
The variables used in the table B.9 are explained in table B.10.
173
Table B.10 Description of Variables Variable Description
1 AGR Agriculture
2 MIN Mining
3 COAL Coal
4 GAS Natural gas
5 OIL Petroleum
6 ALTF Alternative fuels
7 CNT Construction
8 NDMNF Non-durable manufacturing
9 DMNF Durable manufacturing
10 TRAN Transportation
11 UTIL Utility
12 ELEC Electricity
13 EGUTIL Electric and gas utility
14 TRADE Wholesale and retail sale
15 FIRE Finance, insurance, and real estate
16 SER Services
17 OTHR All other sectors not classified
18 LABOR Employed workers
19 PROP Proprietors (Self employed workers)
20 CAPITAL Capital
21 IBTAX Indirect business taxes
22 HHH Household
23 FED Federal government
24 SLNED State and local government for non-education
25 SLED State and local government for education
26 ENTER Enterprise
27 SAVING Saving and inventory
28 FIMP Foreign import/export
29 DIMP Domestic import/export
174
B.3 Data on Elasticities
Data on elasticities of substitution of a nested CES Production function and
household utility function are based on Böhringer and Rutherford’s study (1997)
and Oladosu’s study (2000). For the elasticities that were unable to find data, I
assume “best guesses” from other studies.
Table B.11 Elasticities of Substitution in Production Functions Index Description Values
σ 1 Elasticity of substitution between value added inputs and
other intermediate inputs
0.1*
σ21 Elasticity of substitution between transportation inputs and
other material inputs
0.2**
σ31 Elasticity of substitution between labor and proprietary
service
0.3**
σ22 Elasticity of substitution between composite of labor and
proprietary service and composite of capital and energy
0.2***
σ32 Elasticity of substitution between capital and energy 0.5***
σ 4 Elasticity of substitution between alternative fuels and fossil
fuels
0.2***
σ 5 Elasticity of substitution among fossil fuel inputs 0.5*
σU1 Elasticity of substitution between leisure and commodity 0.3**
σU2 Elasticity of substitution among market commodities 0.2***
(Source: * Böhringer and Rutherford (1997); **Oladosu (2000); ***Best
guesses)
175
Elasticities of substitution of Armington CES (Constant Elasticity of
Substitution) function and CET (constant elasticity of transformation) in foreign
and inter‐regional trading sectors (ROW and RUS) are obtained from Oladosu’s
study (2000), Mid‐Atlantic Regional Assessment (MARA) project 21 . The
elasticities of domestic export and import are set higher than those of foreign
export and import.
Table B.12 Elasticity Data for Armington and CET functions CET for ROW CET for RUS CES for ROW CES for RUS
coefficient 1.2 1.3 1.58 1.7
21 Elasticity of substitution and transformation in materials is recalculated from working paper
in MARA project, and documents and issues are found at
http://www.essc.psu.edu/mara/index.html.
176
B.4 Benchmark Value of Economic Variables
For reference, the values of major endogenous variables in the benchmark
model are summarized in Table B.13.
Table B.13 Benchmark Value of Economic Variables (Million U.S. Dollar Major economic variables base year value
After tax wage rate 0.879
Rate of net return to proprietor 0.952
Rate of net return to capital 0.297
labor supply 206,430
demand for leisure 154,823
Household expenditure for market commodity 294,221
labor income tax payment(federal) 28,176
labor income tax payment(state) 355
In-migration 0
Household income 356,290
Total fossil fuel consumption 9,682
Intermediate fuel demand
Coal 580.0
Gas 866.0
Oil 873.0
Alternative fuels 145.0
Electricity 5955.0
total foreign export 45,301
Total domestic export 132,482
total foreign import 10,449
Total domestic import 88,027
federal government revenue 89,599
state government revenue 68,200
GSP 407,363
VITA of JEONG HWAN, BAE 1400 Martin Street ParkCrest Apt 2113, State College, PA 16803 814-865-2702(Office), 814-235-7455(Home) , [email protected]
Education
The Pennsylvania State University, University Park, PA Ph.D. Agricultural, Environmental, & Regional Economics (AEREC) December 2005
Department of Economics, University College London, UK Department of Economics, University College London, UK 2002
Graduate School of Environmental Studies, Seoul National University, South Korea Master, Urban and Regional Planning 1999Department of Agricultural Economics, Seoul National University, South Korea
BA, Agricultural Economics 1996
Related Experience
AEREC, Pennsylvania State University, State College, PA Research Assistant
• Analysis of Bank Consolidation Trends
• Analysis of State Tax Reform
2003 ~ PRESENT
Seoul Development Institute, Seoul, South Korea Researcher • The Evaluation of Urban Environmental Change By Intensifying FAR Regulation
2001
Korea Environmental Institute, Seoul, South Korea
Researcher
• The Efficient Way of Privatization of Public Toxic Waste Management Facilities
• The Improvement of Institutions for the Conservation of Paldang Source Water
2000
Publications and presentations
• Bae, Jeong Hwan, James S. Shortle, The welfare consequences of green tax reform in small open
economies, Presented in AAEA Annual Meeting, July 2005
• Bae, Jeong Hwan, James S. Shortle, The Welfare Analysis of Green Tax Reform: The Case of Small Open Economies, Presented in NAREA Annual Meeting, June 2005
Shields, Martin, Jeffrey Stokes and Jeong Hwan Bae, An Analysis of Bank Consolidation Trends in Rural Pennsylvania, Presented in AAEA Annual Meeting, August 2004
Bae, Jeong Hwan, Solution to the conflict among development, conservation and equity in the evaluation of wetland conversion project, Presented in ISEE Biannual Meeting, July 2004
• Lee, Sang‐Kyeong, Jeong Hwan, Bae, and∙ Young‐Chul, Shin, 2001, Estimating the Value of Improvements of Built Environments: The Case of FAR Control by Seoul Metropolitan Government, Journal of Korea planners association, vol.36 No.5
