Three Essays in Environmental and Resource Economics 公开

Kiebzak, Stephen (2012)

Permanent URL: https://etd.library.emory.edu/concern/etds/gt54kn68q?locale=zh
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Abstract


Abstract
Three Essays in Environmental and Resource Economics
This dissertation studies three important and timely topics related to the use of
natural resources and the generation of pollutants. Chapter One explores the impact
that state water management frameworks have on farm productivity. Prior
appropriation law states allow greater access to surface waters for use in irrigation, a
critical input for crops. I find that states operating under this form of water law have
corn yields that are approximately 20 to 30 bushels per acre higher than their riparian
law counterparts. This represents an 18 to 28% increase on average. Chapter Two
addresses to what extent oil producers respond to changes in price and whether higher
royalties on oil production result in a reduction in the life of a producing lease. By
exploiting a unique lease-level data set of monthly sales of oil from leases on federal
properties, I estimate that production from the vast majority of currently producing
leases is highly inelastic. Estimated elasticities are small and generally not significantly
different than zero. This data set also includes data from the fourteen-year period during
which marginal leases were granted royalty reductions by the Bureau of Land
Management to stimulate production during periods of low price. The most marginal of
this class of leases, those that do not report sales regularly, have significantly higher
elasticities. Further, leases that participated in the royalty reduction program had a 15%
lower probability of being shut-in than those leases that were not eligible for the
program. Finally, Chapter Three investigates a novel method to predict carbon dioxide
emissions from developing countries, the primary driver of emissions growth over the past
decade. I employ an environmental Kuznets curve-type analysis to predict emissions, but
rather than relating the level of per capita pollutant to a country's gross domestic
product, I use a socio-economic status measure constructed from household
characteristics and possessions survey data from developing countries. This approach
improves on in-sample prediction of emissions which rely on gross domestic product
alone, although data limitations prevent formal testing of this conclusion.

Table of Contents

Contents
1 An Economic Analysis of The Role of Water Law in Improving Corn
Yields 1
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Empirical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.4 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.5.1 Regression using individual climate variables . . . . . . . . . . . . . 13
1.5.2 Regression using principal components . . . . . . . . . . . . . . . . . 14
1.5.3 Robustness Checks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
1.6 Conclusions and Extensions . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Appendix A: Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
Appendix B: Summary Statistics and Regression Results . . . . . . . . . . . . . . 24
2 The Eect of Production Royalties on a Non-Renewable Resource: Oil
Production from Marginal Wells 33
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.2 Stripper Well Royalty Reduction Program . . . . . . . . . . . . . . . . . . . 38
2.3 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
2.3.1 Supply Elasticity of Oil . . . . . . . . . . . . . . . . . . . . . . . . . 39
2.3.2 Taxation of Exhaustible Resources . . . . . . . . . . . . . . . . . . . 43
2.4 Empirical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
2.4.1 Static Panel Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
2.4.2 Error Correction Model . . . . . . . . . . . . . . . . . . . . . . . . . 47
2.4.3 Lease Survival Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 47
2.5 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
2.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
2.6.1 Static Panel Regressions . . . . . . . . . . . . . . . . . . . . . . . . . 51
2.6.2 Dynamic Panel Regressions . . . . . . . . . . . . . . . . . . . . . . . 54
2.6.3 Lease Survival Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 54
2.7 Conclusions and Extensions . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
Appendix C: Schedule of Royalty Rate Reductions . . . . . . . . . . . . . . . . . 57
Appendix D: Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
Appendix E: Stripper Oil Well Lease Summary Statistics and Regression Results 62
Appendix F: Non{Stripper Oil Well Lease Summary Statistics and Regression
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
3 Predicting CO2 Emissions in Developing Countries 91
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
3.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
3.3 Empirical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
3.3.1 Predicting Carbon Emissions from GDP Forecasts . . . . . . . . . . 96
3.3.2 Predicting Carbon Emissions from a SES Measure for Developing
Countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
3.4 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
3.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
3.5.1 Results of GDP EKC Analysis . . . . . . . . . . . . . . . . . . . . . 100
3.5.2 Results of SES Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 101
3.6 Conclusions and Extensions . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
Appendix G: Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
Appendix H: Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
References 116
List of Figures
1 U.S. Water Laws by State . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2 Estimated density of the estimated county-specic eects, f ^ ig234
i=1, from xed
eects regressions using (a) climate variables, and (b) principal components. 23
3 Random Eects Estimates of the Eect of Law on Corn Yields by Varying
Percent Corn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
4 U.S. Wellhead First Purchase Crude Oil Price 1900 - 2011 . . . . . . . . . . 58
5 Rate of Change of U.S. Oil Price, 1900 - 2011 . . . . . . . . . . . . . . . . . 58
6 Log of Avg Daily Production and Price net of Royalty and Severance Tax
(May 2011 $) for a sample of Stripper Well Leases (Jan. 1990 { May 2011) 59
7 Log of Avg Daily Production and Price net of Royalty and Severance Tax
(May 2011 $) for a sample of Non{Stripper Well Leases (Jan. 1996 { May
2011) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
8 Estimated density of the estimated lease-specic eects, f ^ ig2;657
i=1 (left), and
time series plots of f ^ itg from a sample of ve leases (right) . . . . . . . . . 60
9 Estimated density of the estimated lease-specic eects, f ^ ig4;116
i=1 (left), and
time series plots of f ^ itg from a sample of ve leases (right) . . . . . . . . . 60
10 Kaplan{Meier Survival Estimates for Stripper Well and Non{Stripper Well
Leases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
11 Comparing Prediction Models: Actual (1950 through 2008) and Predicted
Global Carbon Emissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
12 Comparing Prediction Models: Actual (1985 through 2008) and Predicted
Global Carbon Emissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
13 Global Carbon Emissions from Developing and Developed Countries . . . . 106
14 Developing Country CO2 Emissions per capita: 122 Countries between 1980
{ 2007 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
15 SES vs CO2 Emissions in Developing Countries . . . . . . . . . . . . . . . . 107
16 Comparing SES and GDP per capita predictors of per capita CO2 Emissions
in Developing Countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
17 SES Trends for non{African Countries . . . . . . . . . . . . . . . . . . . . . 109
18 SES Trends for African Countries . . . . . . . . . . . . . . . . . . . . . . . . 110
19 Comparing Actual Carbon Emissions to in-sample Predictions using the SES
EKC Combined with the GDP EKC (SES + GDP), as well as the GDP EKC
on its own (GDP) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
20 Comparing Actual Carbon Emissions Excluding China to in-sample Predictions112
List of Tables
1 Summary Statistics for Counties with greater than 70% Corn (1995, 2000,
2005) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2 Summary Statistics by type of Water Law for Counties with greater than
70% Corn (1995, 2000, 2005) . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3 Impact of Water Law on Corn Yields: 2SLS, Random Eects, and Pooled
OLS Regression Results Using Climate Variables . . . . . . . . . . . . . . . 26
4 Climate Data Correlation Matrix . . . . . . . . . . . . . . . . . . . . . . . . 28
5 Variable Weights from Principal Component Analysis . . . . . . . . . . . . 29
6 Impact of Water Law on Corn Yields: 2SLS, Random Eects, and Pooled
OLS Regression Results Using Principal Components . . . . . . . . . . . . . 30
7 Impact of Water Law on Corn Yields: Random Eects Regressions of Single
Stage Model With Climate Variables(CVs) and Principal Components(PCs) 31
8 Base RE Results (1) Compared to RE Regressions without Nebraska (2) and
without California and Southwestern States (3) . . . . . . . . . . . . . . . 32
9 Summary Statistics for Stripper Well Leases with more than 200 Reported
Sales (Jan. 1990 { May 2011) . . . . . . . . . . . . . . . . . . . . . . . . . . 62
10 Oil Production Regression Results for the Stripper Well Lease Full Sample:
OLS, BE, and RE Model Comparisons Treating no Sales Report for a Month
as Zero Lease Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
11 Oil Production Regression Results for the Stripper Well Lease Full Sample:
Panel Fixed Eects Model Treating no Sales Report for a Month as Zero
Lease Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
12 Oil Production Regression Results for the Stripper Well Lease Full Sam-
ple: Panel Fixed Eects Model Using Production Averaging to Account for
Intermittent Sales of Cumulative Production . . . . . . . . . . . . . . . . . 65
13 Oil Production Regression Results for the Stripper Well Lease Full Sample:
Panel Fixed Eects Model Using Quarterly Aggregation . . . . . . . . . . . 66
14 Oil Production Regression Results for the Stripper Well Lease Full Sample:
Panel Fixed Eects Model Using Yearly Aggregation . . . . . . . . . . . . . 67
15 Oil Production Regression Results for Stripper Well Leases With More Than
150 Reported Sales: Panel Fixed Eects Model Averaging Production for
Months with no Sales Report . . . . . . . . . . . . . . . . . . . . . . . . . . 68
16 Oil Production Regression Results for Stripper Well Leases With Less Than
150 Reported Sales: Panel Fixed Eects Model Averaging Production for
Months with no Sales Report . . . . . . . . . . . . . . . . . . . . . . . . . . 69
17 Oil Production Regression Results for Stripper Well Leases With More Than
200 Reported Sales: Panel Fixed Eects Model Averaging Production for
Months with no Sales Report . . . . . . . . . . . . . . . . . . . . . . . . . . 70
18 Oil Production Regression Results for Stripper Well Leases With More Than
250 Reported Sales: Panel Fixed Eects Model Averaging Production for
Months with no Sales Report . . . . . . . . . . . . . . . . . . . . . . . . . . 71
19 Oil Production Regression Results for Stripper Well Leases: Panel Fixed
Eects Model Using 3-month Rolling Average Product and Price . . . . . . 72
20 Oil Production Regression Results for Stripper Well Leases: Panel Fixed
Eects Model Using 12-month Rolling Average Product and Price . . . . . 73
21 Oil Production Regression Results for Stripper Well Leases: First{Dierence
Regressions Averaging Production for Months with no Sales Report . . . . 74
22 Oil Production Regression Results for Stripper Well Leases: Dynamic Panel
Fixed Eects Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
23 Results from Cox Proportional Hazard Analysis of Lease Survival Based on
Age Since First Production (Jan. 1996 - May 2011) . . . . . . . . . . . . . . 76
24 Summary Statistics for Non{Stripper Well Leases with more than 150 Re-
ported Sales (Jan. 1996 { May 2011) . . . . . . . . . . . . . . . . . . . . . . 77
25 Oil Production Regression Results for the Non{StripperWell Lease Full Sam-
ple: OLS, BE, and RE Model Comparisons Treating no Sales Report for a
Month as Zero Lease Production . . . . . . . . . . . . . . . . . . . . . . . . 78
26 Oil Production Regression Results for the Non{StripperWell Lease Full Sam-
ple: Panel Fixed Eects Model Treating no Sales Report for a Month as Zero
Lease Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
27 Oil Production Regression Results for the Non{StripperWell Lease Full Sam-
ple: Panel Fixed Eects Model Using Production Averaging to Account for
Intermittent Sales of Cumulative Production . . . . . . . . . . . . . . . . . 80
28 Oil Production Regression Results for the Non{StripperWell Lease Full Sam-
ple: Panel Fixed Eects Model Using Quarterly Aggregation . . . . . . . . 81
29 Oil Production Regression Results for the Non{StripperWell Lease Full Sam-
ple: Panel Fixed Eects Model Using Yearly Aggregation . . . . . . . . . . 82
30 Oil Production Regression Results for Non{Stripper Well Leases With More
Than 100 Reported Sales: Panel Fixed Eects Model, Averaging Production
for Months with no Sales Report . . . . . . . . . . . . . . . . . . . . . . . . 83
31 Oil Production Regression Results for Non{Stripper Well Leases With Less
Than 100 Reported Sales: Panel Fixed Eects Model, Averaging Production
for Months with no Sales Report . . . . . . . . . . . . . . . . . . . . . . . . 84
32 Oil Production Regression Results for Non{Stripper Well Leases With More
Than 150 Reported Sales: Panel Fixed Eects Model, Averaging Production
for Months with no Sales Report . . . . . . . . . . . . . . . . . . . . . . . . 85
33 Oil Production Regression Results for Non{Stripper Well Leases With More
Than 180 Reported Sales: Panel Fixed Eects Model, Averaging Production
for Months with no Sales Report . . . . . . . . . . . . . . . . . . . . . . . . 86
34 Oil Production Regression Results for Non{Stripper Well Leases: Panel
Fixed Eects Model Using 3-month Rolling Average Product and Price . . 87
35 Oil Production Regression Results for Non{Stripper Well Leases: Fixed Ef-
fects Model Using 12-month Rolling Average Product and Price . . . . . . . 88
36 Oil Production Regression Results for Non{Stripper Well Leases: First{
Dierence Regressions Averaging Production for Months with no Sales Report 89
37 Oil Production Regression Results for Non{Stripper Well Leases: Dynamic
Panel Fixed Eects Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
38 Mean Household Percentages and Component Weights from SES PCA . . . 113
39 Developing Countries Included in the Demographic and Health Surveys for
which Household Characteristic and Possession Data is Available . . . . . . 114
40 Per Capita CO2 Emissions Pooled OLS and GLS Fixed Eects Regression
Results for the Full Sample of Developing and Developed Countries . . . . . 114
41 Per Capita CO2 Emissions pooled OLS Regression Results using the SES
Measure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
42 Results from Estimation of per capita GDP Growth Rate Model . . . . . . 115

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