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. 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.

iii

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

iv

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

v

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

vi

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

vii

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

viii

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

ix

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

x

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.

xi

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,

1 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).

2

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.

3

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,

4 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.

5

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

6 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

7 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.

8

Table 1.1 Base Year Output, Trade, and Consumption from Hybrid IMPLAN (Million Dollars) Industrial Sector Output Foreign Domestic Foreign Domestic Final Import Import Export Export 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 116,845.42 2,433.47 18,981.12 11,585.10 20,120.68 34,810.53 manufacture Durable 100,726.37 4,551.41 23,027.73 21,047.88 41,521.60 6,121.09 manufacture 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 gas 4,761.56 352.31 1,549.82 37.29 310.48 1,771.99 utility 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).

9

Table 1.2 Classification of Durable and Non-Durable Industries Non-Durable Manufacturing Durable Manufacturing Industries Industries z Wood Product Manufacturing z Food Manufacturing z Non Metallic Mineral Product Manufacturing z Beverage and Tobacco Product z Primary Metal Manufacturing Manufacturing z Fabricated Metal Product Manufacturing z Textile and Product Mills z Machinery Manufacturing z Clothing Manufacturing z Computer and Electronic Product z Leather and Allied Product Manufacturing Manufacturing z Paper Manufacturing z Electric Equipment, Appliance and z Printing and Related Support Activities Component Manufacturing z Petroleum and Coal Products z Transportation Equipment Manufacturing Manufacturing z Furniture and Related Product z Chemical Manufacturing Manufacturing z Plastics and Rubber Products z Miscellaneous Manufacturing 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.

10

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

11 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

12

Figure 1.1 Expenditure Trend on Energy by Sources in PA

35000

30000

25000

Electricity 20000 Biofuel Nuclear Oil 15000 Gas Million Dollars Coal 10000

5000

0

4 2 74 82 8 90 9 00 9 976 9 9 9 9 994 0 1970 1972 1 1 1978 1980 1 1 1986 1988 1 1 1 1996 1998 2

(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

13 and the main energy source of electricity was coal (46%), followed by nuclear

(23%) (Figure 1.1).

Figure 1.2 Energy Sources of Electricity

Energy source of electricity

14% coal 5% oil 46% gas nuclear

hyroelectric 23% others

6% 6%

(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

14 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.

15

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

16 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

17 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

18 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

19 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.

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Figure 2.1 Partial Equilibrium Analysis on Efficiency Effects of Environmental Taxes

MCs B MED R A MCp

MB

Q1 Q2 Q

(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

21 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).

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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).

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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)

24 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,

25 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

26 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

27 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

28 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.

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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.

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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.

31 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

32 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

33 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

34 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.

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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

36 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

37 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

38 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 Production and Macro-

and Double Dividend Economic Changes

Goulder (1995) Nordhous (1993)

Goulder et al. (1997) Boyd et al. (1995) Closed economy Bovenberg and Goulder (1997)

Parry and Bento (2000)

Inter-regional No study found Li and Rose (1995)

mobility

Open No inter-regional Bohringer and Rutherford Kamat et al. (1999) economy mobility (1997) Bruvoll and Ibenholt (1998)

Bruvoll and Ibenholt (1998)

Andre et al. (2003)

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.

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(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 ( Pe → Pe ),

assuming that the world demand price of the good ( Pt ) in the export market is constant. The supply curve of the good in the export market shifts upward due to

the environmental tax. Then, Pt = Pe + 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.

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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.

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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 ∂V / ∂s > 0 in equation (2.3).

V (w,r;s) = k (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.

C(w,r;s) = 1 (2.4)

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Cs < 0 if amenity is unproductive, while Cs > 0 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).

dw 1 = (−V C + C V ) < 0 (2.5) ds ∆ s r s r

dr 1 p = (−VwCs + CwVs ) 0 (2.6) ds ∆ f

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

43 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

L1 + L2 = N1 + N 2 ≡ N (2.7)

0 0 In the initial stage, wages of both regions are different ( w1 〉w2 ) assuming that there are migration costs. With free mobility, workers will flow into region 1,

e e leading to equilibrium status with L1 and w (Figure 2.3).

Figure 2.3 Two Region Equilibrium Model

0 w1 we we 0 w2

N L N e L 1 1 L1 2

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

44 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

A C (wA ,rA ;nA ,t A ) = P (2.8)

B C (wB ,rB ;nB ,t B ) = 1 (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.

i cr X i + ϕi H i = Li i=A, B (2.10)

i cw X i = H i i=A, B (2.11)

i c i = ∂c Where j ∂j (j = w,r) denotes use of a factor for producing one unit

of goods, ϕi denotes residential use of land, and Hi denotes the level of labor.

H A + H B = H (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

45 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

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(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

47 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.

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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

49 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.

50

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

51 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,

52 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.

53

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,

54 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

55 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)

56 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.

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Figure 2.4 Labor Migration Effects on the Deadweight Loss

Ls +mig+tL Case II W Ls + tL Case I Ls Ls +mig

B

Case III E A

C

D Ld

L

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

58 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).

∆ABD > ∆ABC > ∆ABE (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

59 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.

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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.

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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.

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Figure 2.5 Feedback of Environmental Taxes in the Long Run

Resource depletion Innovation Production cost (+)

Environmental Profit (-) pollution + Alternative climate change Consumer fuels price (+)

Environmental tax Consumption (-) (-)

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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

64 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.

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Table 3.1 Description of Industrial Sectors Industry Description Coal* Coking coal and steam coal Asphalt and road oil, aviation gasoline, distillate fuel, Oil* jet fuel, kerosene, LPG, lubricants, motor gasoline, and residual fuel Natural gas* Natural gas, Natural gas liquid Nuclear, hydropower, solar, fuel cell, geothermal, Alternative fuels* bio-fuel and wind Agriculture, Mining, Construction, Electric and gas utility, Utility, Durable and non-durable Materials** manufacturing, Trade, Financial, insurance, real estate, services, and others Railroad and related service, Local, interurban passenger, Motor freight transport, Water transport, Transportation** Air transport, Transportation service, Local government passenger

Electric services, State and local electric utility, Electricity** 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,

66 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.

67

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

68 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

69 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).

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Figure 3.1 Flow Diagram of Pennsylvania CGE Modeling

① IMIP

PROD

(21) ⑤ (20) ② ③ ⑥ ⑩

FED (19) LAB ⑦ LAND SI ④ ⑧ SLNE SLED CAP

(16)

(22) (17) (15) (18) (11) (23)

ROW/ROUS HH ENT

(12) (14)

Source: Revised from Thorbecke (1988)

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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

72 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

F(VA,IM) NCES production function

1st CES VA IM VA: factor inputs IM: intermediate goods LF: composite of labor and proprietary service 2st CES KE: composite of capital and energy M TRAN M: all other materials LF KE

L F K E

3st CES

ALTF FOSSIL FUELS

4st CES

ELEC GAS OIL COAL

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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 / ρ 1 ⎡ VA ρ 1 IM ρ 1 ⎤ X i = X i γ 1 ( ) + (1 − γ 1 )( ) (3.1) ⎣⎢ VA IM ⎦⎥

Primary inputs are substitutable for intermediate inputs with the elasticity of

1 substitution (σ 1 = ) (Varian, 1992). 1 − ρ1

A share parameter (γ 1 ) 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.

Pva VA γ 1 = (3.2) Pva VA + Pim IM

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.

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1 / ρ 21 ⎡ TRANS ρ 21 M ρ 21 ⎤ IM = IM γ 21 ( ) + (1 − γ 21 )( ) (3.3) ⎣⎢ TRANS M ⎦⎥

Ptrans TRANS γ 21 = (3.4) Ptrans TRANS + Pm M

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).

1 / ρ 22 ⎡ LF ρ 22 KE ρ 22 ⎤ VA = VA γ 22 ( ) + (1 − γ 22 )( ) (3.5) ⎣⎢ LF KE ⎦⎥

Plf LF γ 22 = (3.6) Plf LF + Pke KE

The composite of labor and proprietary services is divided into labor and proprietary services using a third level CES function.

1/ ρ 31 ⎡ L ρ 31 F ρ 31 ⎤ LF = LF γ 31 ( ) + (1 − γ 31 )( ) (3.7) ⎣⎢ L F ⎦⎥

Pl L γ 31 = (3.8) Pl L + Pf F

The composite of capital and energy is divided into capital service and combined energy in the other third level CES function.

1/ ρ 32 ⎡ K ρ 32 E ρ 32 ⎤ KE = KE γ 32 ( ) + (1 − γ 32 )( ) (3.9) ⎣⎢ K E ⎦⎥

75

Pk K γ 32 = (3.10) Pk K + Pe E

The combined energy consists of alternative fuels and the composite of fossil fuels in the fourth CES function.

1/ ρ 4 ⎡ ALTF ρ 4 FFUEL ρ 4 ⎤ E = E γ 4 ( ) + (1 − γ 4 )( ) (3.11) ⎣⎢ ALTF FFUEL ⎦⎥

Paltf ALTF γ 4 = (3.12) Paltf ALTF + Pffuel FFUEL

The composite of fossil fuels consists of coal, oil, gas, and electricity in the fifth CES function,

1/ ρ 5i ⎡ FUEL i ρ 5i ⎤ FFUEL = FFUEL ⎢∑ γ 5i ( ) ⎥ (3.13) ⎣ i FUEL i ⎦

Pfuel ,i FUEL i γ 5i = (3.14) ∑ Pfuel ,i FUEL i i

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;

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The unit cost function and factor demand functions in the first CES function are:

1 ⎛ P P ⎞ 1−σ 1 PX = e (P , P ) = e ⎜γ ( va )1−σ 1 + (1 − γ )( im )1−σ 1 ⎟ 1 va im 1 ⎜ 1 1 ⎟ (3.15) ⎝ Pva Pim ⎠

Where e1 (Pva , Pim ) = Pva VA + Pim I M

σ X ⎛ PX P ⎞ 1 VA(P , P ) = VA× × ⎜ va ⎟ va im ⎜ ⎟ (3.16) X ⎝ PX Pva ⎠

σ X ⎛ PX P ⎞ 1 IM (P , P ) = IM × × ⎜ im ⎟ va im ⎜ ⎟ (3.17) X ⎝ PX Pim ⎠

The unit cost function and factor demand functions for the aggregated intermediate goods and aggregated value added inputs in the second CES functions are:

1 ⎛ ⎞ 1−σ 21 Ptrans 1−σ 21 Pm 1−σ 21 P = e (P , P ) = e 21 ⎜ γ ( ) + (1 − γ )( ) ⎟ im 21 trans m ⎜ 21 21 ⎟ ⎝ Ptrans Pm ⎠

(3.18)

Where e 21 (Ptrans , Pm ) = Ptrans TRANS + Pm M

σ IM ⎛ P P ⎞ 21 TRANS(P , P ) = TRANS × × ⎜ im trans ⎟ trans m ⎜ ⎟ (3.19) IM ⎝ Pim Ptrans ⎠

σ IM ⎛ P P ⎞ 21 M (P , P ) = M × × ⎜ im m ⎟ trans m ⎜ ⎟ (3.20) IM ⎝ Pim Pm ⎠

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1

1−σ 22 ⎛ Plf P ⎞ P = e (P , P ) = e ⎜γ ( )1−σ 22 + (1 − γ )( ke )1−σ 22 ⎟ va 22 lf ke 22 ⎜ 22 22 ⎟ ⎝ Plf Pke ⎠

………… (3.21)

Where e 22 (Plf , Pke ) = Plf LF + Pke KE

σ 22 VA ⎛ P P ⎞ LF(P , P ) = LF × × ⎜ va lf ⎟ lf ke ⎜ ⎟ (3.22) VA ⎝ Pva Plf ⎠

σ VA ⎛ P P ⎞ 22 KE(P , P ) = KE × × ⎜ va ke ⎟ lf ke ⎜ ⎟ (3.23) VA ⎝ Pva Pke ⎠

The unit cost function and factor demand functions of composite of labor and proprietary services in the third CES functions are:

1

1−σ 31 ⎛ P Pf ⎞ P = e (P , P ) = e ⎜γ ( L )1−σ 31 + (1 − γ )( )1−σ 31 ⎟ Lf 31 L f 31 ⎜ 31 31 ⎟ ⎝ PL Pf ⎠

…………. (3.24)

Where e 31 (PL , Pf ) = PL L + Pf F

σ 32 LF ⎛ P P ⎞ L(P , P ) = L × × ⎜ Lf L ⎟ L f ⎜ ⎟ (3.25) LF ⎝ PLf PL ⎠

σ32 LF ⎛ P P ⎞ F(P , P ) = F × × ⎜ Lf f ⎟ L f ⎜ ⎟ (3.26) LF ⎝ PLf Pf ⎠

The unit cost function and factor demand functions of composite of capital and energy in the third CES functions are:

78

1 ⎛ P P ⎞ 1−σ 32 P = e (P , P ) = e ⎜γ ( k )1−σ 32 + (1 − γ )( e )1−σ 32 ⎟ ke 32 k e 32 ⎜ 32 32 ⎟ ⎝ Pk Pe ⎠

(3.27)

Where e 32 (Pk , Pe ) = Pk K + Pe E

σ KE ⎛ P P ⎞ 32 K(P , P ) = K × ×⎜ ke k ⎟ f e ⎜ ⎟ (3.28) KE ⎝ Pke Pk ⎠

σ 32 KE ⎛ P P ⎞ E(P , P ) = E × ×⎜ ke e ⎟ f e ⎜ ⎟ (3.29) KE ⎝ Pke Pe ⎠

The unit cost function and factor demand functions of composite of energy in the fourth CES functions are:

1

1−σ 4 ⎛ Paltf Pffuel ⎞ P = e (P , P ) = e ⎜ γ ( )1−σ 4 + (1 − γ )( )1−σ 4 ⎟ e 4 altf ffuel 4 ⎜ 4 4 ⎟ (3.30) ⎝ Paltf Pffuel ⎠

e 4 (Paltf , Pffuel ) = Patf ALTF + Pffuel FFUEL (3.31)

σ 4 E ⎛ P P ⎞ ATLF(P , P ) = ALTF × ×⎜ e altf ⎟ altf ffuel ⎜ ⎟ (3.32) E ⎝ Pe Paltf ⎠

σ 4 E ⎛ P P ⎞ FFUEL(P , P ) = FFUEL× ×⎜ e ffuel ⎟ altf ffuel ⎜ ⎟ (3.33) E ⎝ Pe Pffuel ⎠

The unit cost function and factor demand functions of composite of energy in the fourth CES functions are:

79

1

1−σ 5 i ⎛ Pfuels ,i ⎞ P = e (P ) = e ⎜ γ ( )1−σ 5 ⎟ ffuel 5 fuels ,i 5 ∑ ⎜ 5i ⎟ (3.34) i ⎝ Pfuels ,i ⎠

E Where e 5 (Pfuel ,i ) = ∑ Pfuels ,i FUELS i (3.35) i

σ 5i E ⎛ ⎞ E E Pe Pfuels,i FUELS i = FUELS i × × ⎜ ⎟ (3.36) ⎜ ⎟ E ⎝ Pe Pfuels,i ⎠

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.

80

Figure 3.3 Consumer’s Utility System

U(R, C ) First tier CES utility function

U(R) U( C ) R: demand for leisure C : aggregated market commodity (Second tier CES function)

C1……………C17

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 ( I1 ) includes labor income ( IL ), opportunity cost of

leisure, and other kinds of incomes such as capital ( IK ), proprietary income ( IF ), 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

81 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.

I1 =1.75*I L + I K + I F + G (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).

σU −1 σ U 1 −1 1 σU1 σU1 σU1 1 1 σU −1 R σU C 1 σu1 1 MaxU 1 (R, C ) = [γ U 1 + (1 − γ U 1 ) ] (3.38) R0 C 0

Subject to I1 = wR + P *C , 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.

PQ × Q PL(1− tL) ∑ i i w = , where p = i (3.39) P TQ

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:

82

wR γ = (3.40) U1 wR + P *C

The unit expenditure function is derived by solving the dual problem of

utility maximization. The composite price of the first utility ( PTC ), which is the sum of R and C , is referred to as the unit expenditure function.

1 ⎛ w P ⎞1−σu1 P = e (w, P) = ⎜γ ( )1−σu1 + (1− γ )( )1−σu1 ⎟ (3.41) TC u1 ⎜ u1 O u1 O ⎟ ⎝ w P ⎠

The expenditure function ( eu1 (w, P) ) is calculated from multiplying the unit expenditure function by household expenditure on the consumption of commodities and leisure.

eu1 (w, P) = EXP1 × PTC , EXP1 = R + C (3.42)

The indirect utility function is derived by plugging the Marshallian demand functions into the original utility function.

I1 V (w, P, I1 ) = O (3.43) I1 × eu1 (w, P)

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).

σu ⎛ e (w, P) × wO ⎞ 1 R(w, P, I ) = RO ×V (w, P, I ) ×⎜ u1 ⎟ 1 1 ⎜ ⎟ (3.44) ⎝ w ⎠

83

σ u1 ⎛ e (w, P) × PO ⎞ C(w, P, I ) = C O ×V (w, P, I ) ×⎜ u1 ⎟ (3.45) 1 1 ⎜ ⎟ ⎝ P ⎠

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:

1 / ρU 2i ⎡ C i ρU 2i ⎤ C = ⎢∑ γ U 2i ( O ) ⎥ (3.46) ⎣ i C i ⎦

The share parameter is calibrated:

O O Pi C i γ U 2i = O O (3.47) ∑ Pi C i i

The unit expenditure function, indirect utility function, and Hicksian demand for the second CES utility function are:

1 ⎛ P ⎞ 1−σ U 2 i P = e (P , P ,... P ) = ⎜ γ ( i )1−σ U 2 i ⎟ u 2 1 2 i ∑ ⎜ U 2i O ⎟ (3.48) i ⎝ Pi ⎠

I 2 V (P1 ,...Pi , I 2 ) = O (3.49) I 2 × eu2 (P1 ,...Pi )

σU 2i O ⎛ P i ⎞ O ⎜ ⎟ Ci = C i ×V (P1,...Pi , I 2 ) × ⎜ P ⎟ (3.50) ⎝ Pi ⎠

84

where the household income (I2) in the second level is defined as I − wl .

The expenditure function is calculated by multiplying the unit expenditure function by the household expenditure on commodity.

EXP PC eu 2 (P1 , P2 ,... Pi ) = P × EXP 2 , and 2 = ∑ i i (3.51) i

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

85 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.

86

Figure 3.4 Structure of Trading Sector

Import side

First tier Armington Second tier Armington

XXD(PD)

XD(PXD)

Q (PQ) DM(PDM)

FM(PFM)

Export side First tier CET function Second tier CET function

XXS(PD)

SD(PSD)

X (PX) DE(PDE)

FE (PFE)

(Parentheses are the prices of each product and demand level)

87

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.

τ f τ f + 1 τ f + 1 τ f + 1 τ τ X i = ATF i ( γ fi FE i f + (1 − γ fi ) SD i f ) (3. 52)

where

SD i = X i − FE i (3.53)

PX i × X i − PFE × ER × FEi PSDi = (3.54) SDi

ATF i CET function shift parameter of the first tier CET function

γ fi First tier CET function share parameter

FE i Foreign exports of industry output

τ fi Transformation elasticity (CET function exponent)

SD i Domestic supply of commodity

X i Regional supply of production

PSD i Price of SD

PX i Output price (price of X)

PFE Foreign export price

ER Exchange rate

88

The shift parameter (ATF) in the first tier CET function is calibrated by

solving the CET function in reverse. The elasticity of transformation (τ f ) is

1 related with the CET exponent ( ρ ) given that ρ = (Robinson et al., 1 + τ f

1990).

The share parameter (γf) is calibrated by the first order conditions (FOCs) in the profit maximization problem subject to the CET production function.

Max π = PFE × ER × FE i + PSD i × SD i

τ f 1+τ f 1+τ f (3.55) 1+τ f τ τ s.t.X i = ATF i [γ f FE i f + (1 − γ )SD i f ]

The Lagrangian is,

τ f 1+τ f 1+τ f 1+τ f L = PFE × ER × FE + PSD × SD + λ[ X − ATF [γFE τ + (1 − γ )SD τ ] ] i i i i i i f i f

Differentiating with regard to FE and SD, then the F.O.C. will be

τ 1 + τ 1 + τ f ∂ L i f τ f /( 1 + τ f ) −1 f −1 = PFE × ER − ATF × B γ FE τ f = 0 ∂ FE 1 + τ i i τ i i f f

…….. (3.56)

1+τ f ∂Li τ f τ /(1+τ )−1 1+τ f −1 f f τ = PSDi − ATFi × Bi (1− γ f ) SDi f = 0 (3.57) ∂SDi 1+τ f τ f

1+τ f 1+τ f τ τ where Bi = γ f FE i f + (1− γ f )SDi f

89

Dividing (3. 56) by (3.57) yields

1/τ f γ f ⎛ FEi ⎞ PFE × ER ⎜ ⎟ = (3.58) 1 − γ f ⎝ SDi ⎠ PSDi

Rearranging equation (3.58) with regard to Gamma yields

(ٛ 1 (3.59 γ f = 1/τ PSDi ⎛ FEi ⎞ 1 + × ⎜ ⎟ PFE × ER ⎝ SDi ⎠

and the demand of foreign export is derived from equation (3.59):

1− γ PFE * ER f τ f FEi = SDi ( × ) (3.60) PSDi γ f

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).

τ 1+τ 1+τ d d d 1+τ τ τ d SD i = ATD i [γ d DE i d + (1 − γ d ) XXS i d ] (3.61)

Using profit maximization subject to the CET production function, we get the supply of domestic export,

PDE 1− γ d τ d DEi = XXS i ( × ) (3.62) PXDi γ d

90

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).

PX i X i = PSD i SD i + PFE × ER × FE i (3.63)

The price of domestic supply is determined by the value of domestic export, regional supply, and domestic supply in equation (3.64).

PDE × DE i + PXD × XXS i PSD i = (3.64) SD i

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.

91

Min(PFM × ER × FM i + PXDi × XDi ) σ f (3.65) σ f −1 σ f −1 σ f −1 σ σ s.t.Qi = ACFi [(δ fi FM i f + (1− δ f )XDi f ]

Shift parameters (ACF) are calibrated by reverse solving the Armington

function the share parameter (δ f ) is calibrated from the first order conditions of equation (3.65)

σ f σ f −1 σ f −1 σ f −1 σ σ L = PFM × ER × FM i + PXDi × XDi + λ(Qi − ACFi [(δ f FM i f + (1− δ f )XDi f ] )

F.O.C)

σ f −1 σ σ f −1 σ f −1 σ −1 σ f −1 ∂L f σ f −1 f −1 σ σ σ = PFM × ER − × λ × ACFi ×[(δ f FMi f + (1− δ f )XDi f ] δ f × FMi f = 0 ∂FM σ f −1 σ f ……….(3.66)

σ −1 σ f f −1 σ σ f −1 σ −1 σ f −1 ∂L f σ f σ f −1 f −1 σ σ = PXDi − ×λ × ACFi ×[(δ f FM + (1−δ f )XDi f ] (1−δ f )× XDi f = 0 ∂XD σ f −1 σ f ………. (3.67)

Dividing (3.66) by (3.67) produces equation (3.68):

σ −1 f −1 σ f PFM × ER δ f ⎛ FM i ⎞ − ⎜ ⎟ = 0 (3.68) PXDi 1− δ f ⎝ XDi ⎠

Rearranging (3.68) with regard to the demand of foreign import yields

σ ⎡ ⎤ F δ f PXDi FM i = XDi ⎢ ⎥ (3.69) ⎣⎢1− δ f PFM × ER⎦⎥

Finally, equation (3.69) is used to get the calibration of share parameter.

92

1/σ PFM × ER ⎛ FM ⎞ f δ ' δ ' = ×⎜ ⎟ f f where δ f = ' (3.70) PXD ⎝ XD ⎠ 1 + δ f

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).

σ σ −1 σ −1 d d d σ −1 σ σ d XD i = ACD i [(δ di DM i d + (1 − δ d ) XXD i d ] (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).

σ d ⎡ δ d PD i ⎤ DM i = XXD i ⎢ ⎥ (3.72) ⎣1 − δ d PDM ⎦

The calibration procedure for the share parameter (δ d ) at the second tier is the same as with the first tier calibration.

1/σ d ' PDM ⎛ DM ⎞ δ d = *⎜ ⎟ (3.73) PD ⎝ XXD ⎠

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.

PQ i × Q − PFM × ER × FM i PXD i = (3.74) XD i

93

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.

PXD i XD i − PDM × DM i PD i = (3.75) XXD i

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.

94

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).

SSTAX k SSTAXR k = * , where W* is a nominal wage rate (3.76) ∑ Li × W i

PPRTAX k PPTAXR k = , where PP is a nominal return to proprietary ∑ Fi × PP i services (3.77)

ETAX k ENTAXRk = * , where R* is a nominal return to capital services (3.78) ∑ Ki R i

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

95 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.

ITAXRk,i = ITAX k,i / PX i × X i (3.79)

Indirect business tax payments and rates in each industry are shown in table

3.3.

96

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

97 household taxes. The household tax rate is calculated by dividing household tax payment by household income (HHY):

HTAXRh = HTAX h / HHYh (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

98 borrowing (GOVBRO), government income from trading (GOVTRO), transfer income from state/local non education (NEDFED), and education (EDFED).

FEDEXP = ∑Gi,k + FMPGk + DMPGk + ∑TRANSOk,h + FEDNEDk + FEDEDk i h

+ ENTFEDk + GOVSVOk

………(3.81)

FEDREVh = STAX k + PPRTAX k + ENTAX k + ∑GSk,i + GOVTROk i (3.82)

+ ITAX k + HTAX k + NEDFED + EDFED

Government expenditures (G) are calibrated by the proportion of government commodity expenditures (DRG) and total government expenditure on commodities (TOTG).

Gi,k DRGi,k = (3.83) TOTGk

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,

99 household taxes, government borrowing, government income from trading, and transfer income from the federal government (FEDNED).

NEDFL = ∑Gk,i Pi + FMPGk + DMPGk + ∑TRANSk,h + NEDFEDk ih

+ EDTRANSk + ENTNEDk − STAX k + GOVSVOk − CATAX k − ENTAX k − PPRTAX k

− ∑GSk,i PDi − ∑OTHERTAk,i − ∑ BPROTAX k,i − ∑ SALETAX k,i i i i i

− ∑ HINCTAX k,h − ∑ HHPROTAX k,h − ∑ HHOTHTAX k,h − GOVBROk − GOVTROk h h h

− FEDNEDk ………(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).

EDTRANS = ∑GI ,ED PI + NCIMPG + ∑TRANSOED,HH + EDNED IHH + EDFED + GOVSVO (3.85)

100

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).

π AMEN = ENV − ×TCE (3.86) 2 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

101 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.

102

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).

Lmig = ε1 log(wPA / wRUS ) + ε 2 log(AMEN PA / AMEN RUS ) (3.87)

Labor migration ( Lmig ) is a function of the natural log of relative net real

wage and natural amenities; ε1 and ε 2 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

103 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).

GLS = Lmig + LS (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.

X i = Ei + IM i + Ci + ∑G + ITi + FEi + DEi (3.89) gov

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).

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TLS = GLS + ∑ STAX + EXOINCOME + LADJ (3. 90) gov

TLS = ∑ LDi (3.91) i

TFS = ∑ FDi (3.92) i

TKS = ∑ KDi (3.93) i

Next, government revenue should be equal to government expenditure.

FEDREV = FEDEXP (3.94)

NEDREV = NEDEXP (3.95)

EDUTRAN = EDEXP (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).

TOTSAV = ∑INVNTRi + HHSAVING+ ∑GOVSV + RET + LADJ + PADJ i gov (3.97)

+ ROWSAV+ RUSSAV

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Total investment is the sum of industry’s investment demand.

TINT = ∑ ITi (3.98) i

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.

RUSEX = ∑ DEi + HHRED + ∑ DEXTX + RUSSAV (3.99) i gov

ROWEX = ∑ FEi + HHREF + ∑ FEXTX + ROWSAV (3.100) i gov

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.

RUSY = ∑ DM i + PADJD + CADJD + RUSDEFF + DMPFG + DMPNEG + DMPEG i

ROWY = ∑ FM i + PADJF + CADJF + ROWDEFF + FMPFG + FMPNEG + FMPEG i

……..(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.

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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.

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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

108 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

109 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 Revenue Endogenous Carbon Wage Amenity Authority Recycling Labor Tax Elasticity of Elasticity of Migration Rate Migration 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

110 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.

Carbon content × Carbon taxes Ad valorem tax rate = (4.1) Fuel prices

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).

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Table 4.2 Carbon Tax and Equivalent Ad‐Valorem Fuel Tax Rates Carbon tax × Carbon Content by Equivalent Ad-valorem Fuel Tax Fuel Type (A) (A / Fuel Price) Carbon Coal Oil Gas Electricity Coal Oil Gas Electricity Tax Tax Tax Tax Tax ($/Ton) 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 Coal Tax Oil Tax Gas Tax Electricity Tax Total Rate Revenue Revenue Revenue Revenue 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

112 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

113 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:

⎡ 1 ρ ρ ⎤ Max ⎢ ln(C + R )⎥ ⎣ ρ ⎦

subject to M* + w(T − R) = PC 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

∂Γ = C ρ −1 ln(C ρ + R ρ ) − Pλ = 0 (4.2) ∂C

∂Γ = R ρ −1 ln(C ρ + R ρ ) − wλ = 0 (4.3) ∂R

∂Γ = M * + wT − wR − PC = 0 (4.4) ∂λ 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

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C P P ( ) ρ −1 = , thus C * = ( )1/(ρ −1) R* (4.5) R w w

Plugging equation (4.5) into equation (4.4) and rearranging terms provides the

Mashallian demand for commodities and leisure.

wσ −1 (M * + wT ) Pσ −1 (M * + wT ) ρ R* = , C * = , where σ = (4.6) wσ + Pσ wσ + Pσ ρ −1

Using the derived Mashallian demands in equation (4.6), the indirect utility function (V) can be derived as

1 (Pσ + wσ )(M * + wT ) ρ V (P, M ,T) = { } ρ = (Pσ + wσ ) −1/σ (M * + wT ) (4.7) (wσ + Pσ ) ρ

Solving the indirect utility function with regard to exogenous household income yields the pseudo‐expenditure function (Just et al., 1986).

e* (P, w,V ,T ) = (Pσ + wσ )1/σ V − wT (4.8)

This function is used to compute the compensating variation of wage and price changes using the definition

CV = e* (P 0 , w0 ,U 0 ,T 0 ) − e* (P1 , w1 ,U 0 ,T 0 )

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.

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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.

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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

117 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.

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Table 4.4 Relative Changes in Major Economic Variables for The $5/T Carbon Tax without Revenue Recycling (%) $5/t Carbon Tax Without Revenue Scenario Recycling No Labor Endogenous Economic Variable Migration Labor migration Gross CV -3.44 ($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.)

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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

120 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

121 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 $5/T Carbon Tax With The Tax Revenue Scenario Recycling Endogenous Economic Variable No Labor Migration Labor migration Gross CV 92.0 ($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.)

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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.

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Table 4.6 Relative Changes in Major Economic Variables for the $5/T of Federal Carbon Tax with the Tax Revenue Recycling Scenario $5/T Of Federal Carbon Tax with the Tax Revenue Recycling Economic Variable No Labor Migration Endogenous Labor migration gross CV 84.15 ($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

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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.

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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.

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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 Migration Economic 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.

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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.

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Table 4.8 Reduction in the Carbon Emission for $5 of Carbon Tax Fossil Fuel Intermediate Final Total Conversion Fuel Quantity Carbon Carbon Type Demand Consumption ($Million) Factor Price (ton) (MTCE) (MMTCE) ($Million) ($Million) ($) 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 $10 of Carbon Tax Fossil Fuel Intermediate Final Total Conversion Fuel Quantity Carbon Carbon Type Demand Consumption ($Million) Factor Price (ton) (MTCE) (MMTCE) ($Million) ($Million) ($) 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 $15 of Carbon Tax Fossil Fuel Intermediate Final Total Conversion Fuel Quantity Carbon Carbon Type Demand Consumption ($Million) Factor Price (ton) (MTCE) (MMTCE) ($Million) ($Million) ($) 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

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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.

130

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 High Migration Elasticity Elasticity gross 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 -1.15 -1.19 electricity Labor in-migration ++ Final consumption on fossil fuels -2.29 -2.29 and electricity 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 0.909 0.814 fuels Intermediate demand for electricity -1.09 -1.14 Household exogenous income ++ Total intermediate demand for fossil -0.957 -1.005 fuels Total intermediate demand for 0.521 0.485 materials

131

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

132 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

133 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.

134

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

135 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.

136

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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 variable PRIVAPRICE(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 .

154

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 .

155

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 .

156

Table A.1(Contd.) Count of Independent Equations and Endogenous Variables Equation Number Variable Number Fixed variable COMEQA 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 .

157

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)

158

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.

159

Table B.2 Changes in CO2 Emission by Sectors (Unit: MMTCE) Emission Source 1990 % of 1990 1999 % of 1999 Fossil Fuel 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.

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Table B.3 Energy Consumption by Sectors (Unit: MMTCE) Energy sectors Electricity Transport Materials Final demand TOTAL (%) Coal 1210.6 0 295.3 2.2 1508.1 (thousand short tons) (41.10) Gas 21.3 40.2 394 272 727.5 (billion cubic feet) (19.83) Petroleum 45.1 955.2 279.3 153.8 1433.4 (thousand barrels) (39.07) Alternative fuels 31.6 1.1 54.1 14.8 101.6 (million kWh) Electricity 788.6 1.4 301.8 153.6 1245.4 (million kWh) 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).

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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).

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Table B.5 Carbon Emission of Pennsylvania by Emission Sources (Unit: MMTCE) Emission Source 1990 % of 1990 1999 % of 1999 Fossil Fuel 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).

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Table B.6 Pennsylvania Coal Statistics (Unit: MMTCE) Percent Average Annual Change Percent Change Category 1999 1998- 1995- 1990- 1999 1999 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 (Source: Pennsylvania coal profile at http://www.eia.doe.gov/cneaf/coal/statepro/imagemap/pa.htm)

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.

164

Table B.7 Oil Prices in 2000, Pennsylvania (Unit: Dollar per barrel) 2000 price Regular Midgrade Premium Jet Kerosene No 2 No 2 Residual Gasoline Gasoline Gasoline Fuel Heating Diesel Fuel Oil 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 1.031 1.103 1.185 0.92 1.189 1.224 1.088 0.593 Average (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.

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Table B.8 Natural Gas Price (Unit: Dollar Per Thousand Cubic Feet (MCF)) 2000 At City Residential Commercial Industrial Electric Gate 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 5.09 8.49 7.72 5.12 3.83 Average (Source: EIAʹs Natural Gas Prices for Pennsylvania at http://www.eia.doe.gov/emeu/states/ngprices/ngprices_pa.html)

166

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).

167

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

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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

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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

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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

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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.

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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

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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 0.1* other intermediate inputs

σ21 Elasticity of substitution between transportation inputs and 0.2** other material inputs

σ31 Elasticity of substitution between labor and proprietary 0.3** service

σ22 Elasticity of substitution between composite of labor and 0.2*** proprietary service and composite of capital and energy

σ32 Elasticity of substitution between capital and energy 0.5***

σ 4 Elasticity of substitution between alternative fuels and fossil 0.2*** fuels

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)

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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.

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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

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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 1999 Department of Agricultural Economics, Seoul National University, South Korea BA, Agricultural Economics 1996

Related Experience

AEREC, Pennsylvania State University, State College, PA Research Assistant 2003 ~ PRESENT

• Analysis of Bank Consolidation Trends

• Analysis of State Tax Reform

Seoul Development Institute, Seoul, South Korea Researcher 2001

• The Evaluation of Urban Environmental Change By Intensifying FAR Regulation

Korea Environmental Institute, Seoul, South Korea Researcher 2000

• The Efficient Way of Privatization of Public Toxic Waste Management Facilities

• The Improvement of Institutions for the Conservation of Paldang Source Water

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