Three Essays in Environmental and Resource Economics Open Access
Kiebzak, Stephen (2012)
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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