three essays on the economics of hydraulic fracturingsyed mortuza asif ehsan abstract hydraulic...

$: Three Essays on the Economics of Hydraulic FracturingSyed Mortuza Asif Ehsan Abstract Hydraulic fracturing has been increasingly used in the USA to economically extract natural gas$
Three Essays on the Economics of Hydraulic Fracturing

Syed Mortuza Asif Ehsan

Dissertation submitted to the Faculty of the

Virginia Polytechnic Institute and State University

in partial fulfillment of the requirements for the degree of

Doctor of Philosophy

in

Economics

T. Nicolaus Tideman, Chair

Klaus Moeltner, Co-Chair

Gregory S. Amacher

Kwok Ping Tsang

July 27, 2016

Blacksburg, Virginia

Keywords: Hydraulic fracturing, environmental externalities, dynamic optimization,

simulation, survival analysis, Act-13, unconventional wells

c©Copyright 2016, Syed Mortuza Asif Ehsan

Three Essays on the Economics of Hydraulic Fracturing

Syed Mortuza Asif Ehsan

Abstract

Hydraulic fracturing has been increasingly used in the USA to economically extract natural

gas and oil from newly discovered shale plays. Despite new, more severe, and long term

impacts of hydraulic fracturing compared to conventional drilling, regulatory practices are

mostly implemented by states that regulate with older regulations that were were written

before the widespread use of hydraulic fracturing. This dissertation presents three essays on

the economics of hydraulic fracturing. A standard renewable lease in hydraulic fracturing

runs for a five-year primary term. The first essay examines the effect of initial contract length

on extraction behavior and social costs. It finds that the rate of extraction decreases over

time for both, the social planner and the private extractor. In addition, the social planner

has a more stable extraction path compared to the private extractor. Holding other things

equal, if the social planner seeks to induce a private extractor to leave a higher in situ stock

un-extracted, then the optimal contract duration is longer. Simulations illustrate the magni-

tude of social costs inherent in hydraulic fracturing and non-optimal fixed contract lengths.

The second essay investigates the impact of the significantly increased bonding requirements

for horizontal wells introduced in West Virginia in December, 2011, on the probability of

violation committed by those wells. Results suggest that the increased bonding requirement

has reduced the probability of violation by 2.6 to 3.2 percentage points. Moreover, it slightly

reduces the number of violations done by horizontal wells. Finally, the third essay explores

several aspects of Act-13, introduced on February 14, 2012, by Pennsylvania. This act im-

poses new fees that are assessed annually for fifteen years, on all unconventional gas wells in

Pennsylvania. This chapter explores the impacts of Act-13 on the likelihood of an unconven-

tional well’s shut-down, rate of extraction, and probability of violation. Results suggest that

wells incurring this increased fee schedule have a significantly higher likelihood (more than

three times) of shut-down. Also, Act-13 have reduced the extraction rate, and the probability

of violation committed by unconventional wells in Pennsylvania.

Dedication

To my parents and my brother who have supported me along this path.

iii

Acknowledgments

I would like to express the deepest appreciation and thanks to Professor Dr. T. Nicolaus

Tideman for his continuous support and his advice on my research and career. I am ex-

tremely grateful to Professor Dr. Klaus Moeltner and Professor Dr. Gregory Amacher for

their tremendous help, precious time, brilliant comments, and suggestions in finishing my

dissertation. I would like to express special thanks to Dr. Kwok P. Tsang for serving as my

committee member. I am also thankful to Dr. Alec Smith for agreeing to participate in my

defense. I am grateful to the West Virginia Department of Environmental Protection office

and the Pennsylvania Department of Environmental Protection office for providing valuable

datasets that are used in this dissertation. A special thanks to my family. Words can not ex-

press how grateful I am to my parents and my brother for their support and prayer. Without

their encouragement, I could not have completed this journey. Finally, I thank the Almighty

God, for letting me through all difficulties.

iv

Contents

1 Contract Duration and Extraction with Hydraulic Fracturing 1

1.1 Motivation: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Background Section . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.3 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

1.3.1 Exogenous Contract Duration . . . . . . . . . . . . . . . . . . . . . . 7

1.3.2 Endogenous Contract Duration . . . . . . . . . . . . . . . . . . . . . 13

1.4 Model Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

1.5 Simulations: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

1.5.1 Function settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

1.5.2 Fixed 5-year time horizon . . . . . . . . . . . . . . . . . . . . . . . . 23

1.5.3 Open time horizon . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

1.5.4 Social cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

1.6 Conclusion: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

1.7 Appendix: A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

2 Increased Bonding Requirements and Hydraulic Fracturing:

A Case Study of West Virginia 37

2.1 Motivation: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

2.2 Data and Overview: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

2.3 Empirical Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

v

2.4 Results: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

2.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

2.6 Appendix: B . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

3 Increased Unconventional Well Fees in Pennsylvania:

Impacts on Survival, Production, and Well Violations 76

3.1 Motivation: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

3.2 Data and Overview: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

3.3 Empirical Strategy: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

3.3.1 Survival analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

3.3.2 Rate of extraction analysis . . . . . . . . . . . . . . . . . . . . . . . . 92

3.3.3 Probability of violation analysis . . . . . . . . . . . . . . . . . . . . . 93

3.4 Results: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

3.4.1 Survival analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

3.4.2 Rate of extraction analysis . . . . . . . . . . . . . . . . . . . . . . . . 98

3.4.3 Probability of violation analysis . . . . . . . . . . . . . . . . . . . . . 99

3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

3.6 Appendix: C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

4 References 121

vi

List of Figures

1.1 Figure 1.1: Instantaneous Functions . . . . . . . . . . . . . . . . . . . . . . . 33

1.2 Figure 1.2: Extraction and state, fixed time (β=200) . . . . . . . . . . . . . 33

1.3 Figure 1.3: Cumulative extraction, fixed time (β=200) . . . . . . . . . . . . 34

1.4 Figure 1.4: SP extraction and state under different damage parameters, fixed

time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

1.5 Figure 1.5: Extraction and state, open time (β=200) . . . . . . . . . . . . . 35

1.6 Figure 1.6: Cumulative extraction, open time (β=200) . . . . . . . . . . . . 35

1.7 Figure 1.7: SP extraction and state under different damage parameters, open

time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

1.8 Table 1.8: Summary of results . . . . . . . . . . . . . . . . . . . . . . . . . . 36

2.1 Table 2.1: Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . 56

2.2 Figure 2.2: Horizontal wells in West Virginia . . . . . . . . . . . . . . . . . . 57

2.3 Figure 2.3: Number of wells, inspections and violations per operator . . . . . 57

2.4 Figure 2.4: Inspections and violations per well . . . . . . . . . . . . . . . . . 58

2.5 Figure 2.5: Active Horizontal wells in West Virginia . . . . . . . . . . . . . . 59

2.6 Figure 2.6: Inspected wells in West Virginia . . . . . . . . . . . . . . . . . . 60

2.7 Figure 2.7: Violating wells in West Virginia . . . . . . . . . . . . . . . . . . 61

2.8 Table 2.8: Variation Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . 62

3.1 Table 3.1: State Regulations . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

vii

3.2 Table 3.2: Fee Schedule - Source: Sacavage and Bureau (2014) . . . . . . . . 102

3.3 Table 3.3: Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . 103

3.4 Table 3.4: Variation Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . 104

3.5 Figure 3.5: Horizontal wells in Pennsylvania . . . . . . . . . . . . . . . . . . 105

3.6 Figure 3.6: Number of wells, inspections and violations per operator . . . . . 105

3.7 Figure 3.7: Inspections and violations per well . . . . . . . . . . . . . . . . . 106

3.8 Figure 3.8: Active Horizontal wells in Pennsylvania . . . . . . . . . . . . . . 106

3.9 Figure 3.9: Inspected wells in Pennsylvania . . . . . . . . . . . . . . . . . . . 107

3.10 Figure 3.10: Violating wells in Pennsylvania . . . . . . . . . . . . . . . . . . 107

3.11 Table 3.11: Model’s Variables . . . . . . . . . . . . . . . . . . . . . . . . . . 108

3.12 Figure 3.12: Non-parametric analysis . . . . . . . . . . . . . . . . . . . . . . 109

3.13 Figure 3.13: Test of proportional hazard assumption . . . . . . . . . . . . . . 109

viii

Chapter 1

Contract Duration and Extraction

with Hydraulic Fracturing

1.1 Motivation:

Hydraulic Fracturing has been conducted in the United States since the 1940s. The recent

discovery of large new reserves of coal-bed or shale-bound gas and technological improve-

ments in combining this technology with horizontal drilling have resulted in increasing use of

fracturing as a mining method for tightly bound natural gas and oil from shale formations.

Shale gas, which has only become accessible due to hydraulic fracturing, historically provided

only 1% of U.S. natural gas production. By 2010, however, this increased to 23%, and by

2035 it is predicted to reach 49% of total U.S. natural gas production (Davis 2012).

As the use of hydraulic fracturing becomes common, concerns regarding potential harm to

the environment and health have also been increasing. While hydraulic fracturing can have

adverse impacts that include air pollution, noise pollution, traffic congestion and accidents,

perhaps the most direct impact is to cause ground and surface water pollution (Vidic et al.

2013). Fractures made in the process of mining often create or extend fissures above the

target shale formation; these fissures may join with those from naturally existing fractures.

Osborn et al. (2011) and Warner et al. (2012) find that fracturing fluids can therefore reach

shallow aquifers and degrade groundwater quality through methane contamination. This

results in part because a large portion (as much as 90%) of injected fluid is not recovered

during the flow-back period (Vidic et al. 2013). Myers (2012) further concludes that hy-

draulic fracturing fluids can transport to groundwater aquifers within ten years.

1

Syed Mortuza Asif Ehsan Chapter 1. 2

Discharge into surface waterways is also an issue. In Pennsylvania, for example, oil and gas

wastewater is sometimes treated and discharged directly into local streams. Although some

work shows that waste fluid treatment is effective (Warner et al. 2013), other work such as

Olmstead et al. (2013) shows that both the presence of shale gas wells in a watershed, and

the release of treated water, significantly deteriorate surface water quality. This pollution

of ground and surface water implies that neighboring landowners are not free from damages

due to gas migration, contaminant transport through induced and natural fractures, and

waste-water discharge. Because the public at large is not generally compensated in fractur-

ing contracts, this likely leads to socially inefficient levels of market extraction through time

due to decisions that do not reflect all external and social costs associated with fracturing.

In addition to the extraction rate, it is also likely that contract length set between the private

extractor and the landowner selling the extraction right will not reflect all off-site costs. The

nature of the contract in hydraulic fracturing is noticeably different than in conventional

drilling, reducing the application of Reinganum and Stokey (1985) and Kumar (2002) for

our purposes. In hydraulic fracturing, the initial contract can be renewed provided that

the terms and conditions outlined in the contract are satisfied. In a renewed contract, the

extractor can also negotiate to increase plant size (i.e., lease more land). New royalties and

a new length of contract are also negotiated in this renewal process. Most operators using

hydraulic fracturing are small and medium-sized firms who may value short-run profit more

than goodwill, contract renewal, or sustainable extraction. Thus, they might have incentives

to extract quickly during the initial period, producing more damage to the environment in

the short run.1 This is quite different from conventional drilling.2 In conventional drilling,

most states have adopted compulsory integration statutes, where production of multiple op-

erators is managed by just one (consolidated) operator. This integration policy in theory

ensures that there is no inefficient extraction of resources given the length of contracts. Such

is not the case for hydraulic fracturing, as there are many small extractors who may prefer

short run profit given the contract length. Thus, the current practice of simply adopting a

five-year initial contract length used in conventional drilling may not be socially optimal.

1Majority of the producible natural gas is obtained during the first few years of production (Considine2010). If the shale well has a production life of ten years then 50% of total expected production will occurafter 29 months of production, whereas for the case of twenty years of production 50% of total expectedproduction will occur after 50 months of production(Duman 2012).

2Conventional drilling is the technology of extraction where only the vertical wells are drilled.


Our purpose is to examine contract length and extraction rates for a hydraulic fracturing

firm, and to contrast the market solutions for these choices to the socially optimal ones that

reflect off-site environmental damages. In particular, we show how the extraction path for

a private firm differs from a social planner for a given contract length. We then go further

and treat contract length as endogenously determined along with the extraction rate. We

also compute the extent of social costs associated with these distortions. A simulation is

calibrated using published off-site and on-site drilling costs, with a particular focus of under-

standing costs associated with five year fixed initial fracturing contracts now present in the

industry.

In the general exhaustible resource literature, contract duration for mining is rarely addressed

or is assumed to be given, while in practice hydraulic fracturing firms simply adopt the five-

year contract length that exists in other conventional drilling industries. Thus, our work not

only extends the theoretical literature on fracturing, but our comparison of social and private

decisions can inform future policy concerning contract length and environmental damage in

hydraulic fracturing applications. We also briefly explore how extraction path and initial

contract length change if the extractor responds to some degree to the external damage it

imposes on society. From a policy perspective, while regulations have evolved to manage the

adverse environmental impacts associated with conventional gas and oil production, effective

instruments to reduce environmental harm do not yet exist for hydraulic fracturing (Holahan

and Arnold 2013). The U.S. Environmental Protection Agency (EPA) in collaboration with

states is working to help ensure that excessive use of hydraulic fracturing does not come

at the expense of public health caused by groundwater pollution. Aside from EPA’s imme-

diate endangerment orders, regulations of hydraulic fracturing are largely promulgated on

a state-specific basis (Rahm 2011). A large portion of these regulations are informational

only, requiring release of information concerning the type of chemicals used in the extraction

process. THis chapter demonstrates that new environmental risks associated with the tech-

nology of hydraulic fracturing warrant new institutional policies in addition to the existing

regulations. As we will show, these instruments should target revisions to contract length,

in part because unlike conventional drilling, in hydraulic fracturing an extractor has more

incentive to fracture as this serves to increase the stock of resources accessible and maximize

gas flow (Halliburton 2008).

While there are two exceptions in the exhaustible resource literature that examine contract

length in extraction, these studies have been undertaken assuming there is stock uncertainty


and focus only on conventional drilling. Reinganum and Stokey (1985) examine the extrac-

tion of a common property natural resource in an oligopolistic setup as a non-cooperative

game under an assumption that extraction is costless. They compare aggregate extraction

paths as the length of the period of commitment regarding extraction changes. Kumar

(2002) also discusses the optimal time horizon for an exhaustible resource extractor under

stock uncertainty. His results suggest that uncertainty lengthens the optimal time horizon,

implying that extraction is more conservative over time under uncertainty. Kumar (2002)

also shows that a smaller initial stock might also lead to more conservative extraction over

time as uncertainty extends the optimal mining era. This work is not directly applicable to

hydraulic fracturing, in part because the primary firms involved in fracturing are small and

medium-sized companies who may not benefit as much through contract renewal and thus

might prefer a shorter contract length (Davis 2012).

There have been some articles that have considered various market failures in the extraction

of conventional exhaustible resources. Stiglitz (1976) compares the rate of extraction of ex-

haustible resources by a monopolist to that of a competitive market with positive extraction

costs. His main result implies that a monopolist will conserve more than a competitive firm

in a market where price elasticity is relatively small in earlier periods (see Dasgupta and Heal

(1979)). Investigating incentives of a harvester in a fishery or forestry concession, Costello

and Kaffine (2008) find that simple concession contracts can be designed to induce the first

best harvest path, even in the presence of insecure property rights. Insecurity in their model

comes from the fact that a resource concession is granted for a fixed (and assumed exogenous)

duration, after which it is renewed with some probability only if a target stock is achieved.

Our results for hydraulic fracturing will extend this literature in multiple directions. The

hydraulic fracturing industry is relatively less concentrated (Davis 2012). Thus, the presence

of firms with small market shares implies that extraction rate under this technology will likely

be different and possibly higher than the case where the market is concentrated with one or

a few firms. Further, the optimal contract length in our problem is important to extraction,

and this interrelationship cannot be ignored. We show that the optimal initial contract du-

ration and the extraction rate are closely related. In particular, it may be possible to design

the duration of agreements in a way that induces the private extraction path to converge to

a socially optimal extraction path, even if the private extractor ignores off-site costs.

The rest of this chapter proceeds as follows. In Section 1.2 we briefly discuss the process of


hydraulic fracturing. In section 1.3 we use several stylized facts to develop a theory under-

lying extraction and contract duration decisions for hydraulic fracturing. Both endogenous

and exogenous contract duration and private and social extraction problems are developed.

In Section 1.4 we develop results that help frame the differences across these problems. A

numerical simulation is conducted in Section 1.5. Also in this section we briefly discuss about

severance tax, a potential policy tool to reduce social costs emanating from hydraulic frac-

turing. We offer our conclusions in Section 1.6.

1.2 Background Section

Hydraulic fracturing follows a procedure to access shale reservoirs typically located well be-

low groundwater aquifers (Arthur et al. 2009). First, a vertical well is drilled into the ground

up to 6000 feet. Once the drill reaches the level at which shale is located, the extractor

completes the horizontal section of the well. The horizontal section ranges from 1000 feet

to 5000 feet long and thus can be located outside of the contracting landowner’s property.

Each fracture stage is then performed within an isolated interval (about 500 feet) in which

a cluster of perforations is created. These perforations allow fluids to flow through to the

formation causing artificial fractures and thereby allowing gas to flow back from the ground

into the well-bore during the production phase of the operation. The fracturing fluid is a

mixture of water and sand (about 8 million gallons of water is required to fracture one well)

and is pumped from the surface into the well. The fractures then expand and branch out,

allowing gas resources to be extracted more easily. Plugging is used to isolate each fracture

interval and maintain pressure necessary for gas collection. The extent of plugging in each

period defines the extraction rate in that period. The fracturing and plugging process is

repeated along the entire length of the well bore to maximize eventual extraction efficiency.

Once all fractures are completed the plugs are removed and the extraction of gas begins. The

entire process of developing a well to the gas flow takes four to five months. The well itself

can produce oil or natural gas for 10- 20 years. Each well may be re-stimulated using the

same process up to ten times during its productive life. After recovery of gas, the extractor is

supposed to return the land to its original condition. The extractor also faces the problem of

disposing flow-back fluids. Several methods can be followed, including injecting wastewater

back into the well, recycling the water for future reuse in fracturing treatments, or treating

and then releasing it into the environment.


It is important to know how a hydraulic fracturing contract works to have a better under-

standing of our model. In practice, extractors draft a contract with the owner of the largest

parcel of a given production area, which can be as large as 640 acres. The contract terms

include a bonus payment, rental rate, and a royalty rate for extraction. Because each ex-

tractor competes with other extractors and has incentives to induce other landowners to

rent their land as well, in order to maximize the area available for drilling, it is reasonable

to assume that the companies will give the largest parcel owner the best offer. We define

this landowner as the Contracting Land Owner (CLO) and assume she has less information

about the resource under the ground than does the extractor. The CLO will accept the offer

that maximizes her payoff. Once the owner of the largest parcel makes a contract with an

extractor for a period of time, it automatically prevents others from engaging in production

in that area because of limits on unitization (Weidner 2013).3

To formally investigate contract duration and its effect on hydraulic fracturing, we will make

use of two different assumptions about the type of extractor and the initial contract length.

The type of extractor considered will be either a private firm or a social planner (SP). The

social planner will conventionally be assumed to take into account all costs (onsite and offsite)

associated with fracturing and thus will yield the first best solution. The private firm will

be assumed driven by rent maximization and account only for those costs which it pays to

obtain the contract and extract the resource. For each of these extractors, we consider how

the optimal paths and contract length change for a change in the value of various parame-

ters. Here we will examine two assumptions concerning contract duration. First, we assume

the contract time length is given, so that the extraction rate and the ending stock are the

only choices of interest. The second case is one where contract duration is endogenous and

determined along with the ending in situ stock. Reinganum and Stokey (1985) and Chap-

man (1987) suggest that the extractor finds it optimal to extract at a higher rate when the

production period or contract length is shorter. The inefficient behavior of the extractor in

hydraulic fracturing can also be a result of non-optimal initial contract duration.

3Unitization provides for the exploration and development of an entire geologic structure or area by asingle operator so that drilling and production may proceed in the most efficient and economic manner.Once the largest parcel of extraction is gone, other extractors do not have any incentive to operate in thesmall remaining area as extraction with hydraulic fracturing requires a large unit of land


1.3 Model

Our main objective is to find the optimal extraction paths q(t) under the above mentioned

scenarios. Define l as the leased amount of land, t as time period, x(t) = xt, (t = 0, ....., T )

as the stock of recoverable resource, p as the (assumed constant) price of the resource, R as

the royalty rate, r as rental rate, and v as the market interest rate. We assume the rental

function of land is given by r(l) and increasing in land leased, that is r′(l) > 0. The royalty

function for extraction q at time t is R [q(t)] and is increasing in q(t), i.e., R′ [q(t)] > 0. The

damage function is defined as D [q(t), l]. This refers to the value of damages to the CLO’s

property from the process of fracturing. We make the following assumptions about this func-

tion:

δD(.)

δl= D(.)l > 0,

δ2D(.)

δl2= D(.)ll > 0,

δD(.)

δq= D(.)q > 0,

δ2D(.)

δq2= D(.)qq > 0

andδ2D(.)

δlq= D(.)lq > 0

These assumptions imply that damages imposed by the extractor rise at an increasing rate

if either the leased amount of land or the extraction rate increases. As indicated by the

last term we assume that l reasonably increases the marginal damage of extraction D(.)q.4

We assume the external cost is incurred by public at large (PL). The external cost function

is E[q(t), l] so that, implicitly, the size of l is related to surface externalities, such as air

pollution and accidents, while q(t) is related to external costs, such as ground and surface

water contamination from the extraction process itself; this function is assumed to have the

same derivative properties as the damage function. The extraction cost function is c[q(t)].

We assume l determines the stock of recoverable resource x, but as l defines rented land it is

fixed for rest of the extraction phase and thus is a sunk cost for the extractor. We also assume

that the cost of extraction is convex and increasing in extraction rate, that is c(.)q > 0 and

c(.)qq > 0. Finally, the CLO is assumed to have an initial endowment of L units of land.

1.3.1 Exogenous Contract Duration

We first consider the social planner and then examine how the problem changes for a private

extractor, first under the assumption that the contract duration is exogenously given. A

4A large extraction area (l) might have more wellheads, which means more fractures in shale formationthat require additional fracking fluids that thus produce larger amounts of wastewater. As a result for eachunit of extraction implies an increasing damage.


Social Planner in this model cares about the social damage caused by hydraulic fracturing.

This differs from the two other agents, the representative firm and the contracting landowner,

who seek only to maximize their own private market rents.5

Social Planner

The objective of the SP is to maximize aggregate social welfare. To form such a problem,

we need to specify the external damage to extraction through defining public at large (PL)

who is affected by water pollution, air pollution, damages to the road etc., but who is not

the landowner contracting with the extractor. As we have three agents in this model (CLO,

extractor, PL), the social welfare function will be an aggregation of net payoffs to all. We

assume for now that the SP weights net payoffs equally for these agents initially.

Using our previous definitions, the instantaneous social welfare function is defined as:

{pq(t)− c [q(t)]−D [q(t), l]− E [q(t)]

.

The SP also values a (separable) salvage function which represents the total value of unex-

tracted resources, i.e., land and machinery at the end of the production process; thus, this

salvage function depends on the terminal time period of extraction and the ending stock, T

and xT , respectively.6 As the time of mining increases, damages to future stocks and the

land increases, thus reducing salvage value. On the other hand, the SP is able to capture

higher benefits, one of which is resource security, if a larger stock of the resource is left un-

extracted. It also ensures future resources security which is valued by the SP. We will write

the salvage function as: ϕ (xT , T ) , with ϕT (.) < 0, ϕxT (.) > 0.

The SP’s problem under an exogenous contract length can now be written as follows:

5In our model, D[.] represents the damage imposed by the extractor on the CLO such as damages to theland, whereas E[.] denotes the uncompensated damages to the PL.

6Ikefuji et al. (2010) propose scrap value functions that are appropriate for different types of pay-offfunctions. In all the cases they retain separability as we use here.


maxq(t)

T∫0

e−vt {pq(t)− c [q(t)]−D [q(t), l]− E [q(t)]} dt+ ϕ (xT , T ) e−vT

s.t. x(t) = −q(t), x (0) = x0, x (T ) = xT

0 ≤ q(t) ≤ x(t), x(t) ≥ 0∫ T0q(t)dt = x0 − xT

(1.1)

The social planner’s present value Hamiltonian function is:

H = e−vt {pq(t)− c [q(t)]−D [q(t), l]− E [q(t)]} − λ(t)q(t) + δ(t)q(t) + γ(t)x(t)

The maximum principle requires the following conditions:

δH

δq(t)= 0 ⇒ e−vt [p− c′ [q(t)]−Dq [q(t), l]− Eq [q(t)]]− λ(t) + δ(t) = 0 (1.2)

δH

δx(t)= − λ(t) ⇒ γ(t) = − λ(t) (1.3)

x(t) = −q(t) (1.4)

δ(t)q(t) = 0 ∀t (1.5)

γ(t)x(t) = 0 ∀t (1.6)

Transversality Condition : λ(T ) =δϕ (xT , T )

δxT(1.7)

For q(t) > 0 and x(t) > 0 equation (1.2) becomes;

e−vt [p− c′ [q(t)]−Dq [q(t), l]− Eq [q(t)]] = λ(t)

⇒ λ(t) = e−vt [−c′′ [q(t)]−Dqq [q(t), l]− Eqq [q(t)]] q(t)− ve−vt [p− c′ [q(t)]−Dq [q(t), l]− Eq [q(t)]]

(1.8)

Using equations (1.2)-(1.4), the path of extraction can be shown to equal:

[−c′′ [q(t)]−Dqq [q(t), l]− Eqq [q(t)]] q(t)

p− c′ [q(t)]−Dq [q(t), l]− Eq [q(t)]= v (1.9)

x(t) = −q(t) (1.10)


Equations (1.9), (1.10) give the optimal paths for q(t) and x(t) for a social planner. Notice

that the extraction path depends on changes in damages and extraction costs over time, as

well as the net rents from extraction as a function of prices and stock dependent marginal

costs. We will examine the precise position of this path as a function of these parameters in

the simulation.

We can examine some features of the SP problem as important parameters change using a

dynamic envelope theorem (Caputo 2005). This gives a result concerning how the maximum

present value of social welfare is affected when values of the price of the resource, the initial

stock, the time horizon of mining, and the ending resource stock change. In section A, we

show several results that fit expectations. An increase in the resource price will increase the

maximum present value of social welfare. On the other hand, a larger initial stock means

that the SP has a larger reserve to extract from, which should increase the present value of

the optimal payoff. An increase in the resource stock at the beginning of extraction increases

the maximum present value of social welfare. Leaving resources unextracted reduces the

maximum present value of social welfare.

We also find that an increasing time horizon will increase the SP’s maximum present value,

but this depends on the magnitude of the marginal impact of the ending resource stock on

scrap value. As long as the average discounted social welfare is larger than marginal impact

of the ending stock on the scrap value, the maximized present value of social welfare will

increase over time. This implies that the SP will be better off with a longer contract length

at the optimal level of extraction, if the average discounted payoff for extraction is larger

than marginal value of leaving the resource in situ. In other words, to extend the mining

horizon, the SP would pay up to an amount equal to:

e−vT {pq∗ (T, β)− c [q∗ (T, β)]−D [q∗ (T, β) , l]− E [q∗ (T, β)]} − δϕ (xT, T )

δxTq∗ (T, β)

Private Extraction Firm

The private extractor approaches the landowner with an offer menu for land rent and royalty

payments (r(l), R [q(t)]). The Contracting Land Owner (CLO) then decides on the amount

of leased land, l to give the extractor. The rent paid for this land r(l) is a sunk cost to

the private extractor, because the amount of leased land does not change over the contract’s


duration.

The CLO solves the following dynamic continuous optimal control problem:

maxl

T∫0

e−vt {r(l) +R [q(t)]−D [q(t), l]} dt S.T. l ∈ [0, L] (1.11)

where l is the leased amount of land. Unlike the SP problem, in addition to ignoring exteranl

costs and damages, the private extractor does not value the salvage function given that he has

no vested interest in the land once mining ends. The extractor therefore solves the following

problem:

maxq(t)

T∫0

e−vt {pq(t)− c [q(t)]− r(l)−R [q(t)]} dt

S.T. x(t) = −q(t), x (0) = x0, x (T ) = xT

0 ≤ q(t) ≤ x(t), x(t) ≥ 0 ∀t∫ T0q(t)dt = x0 − xT

(1.12)

The present value Hamiltonian function here is:

H = e−vt{pq(t)− c [q(t)]− r(l)−R [q(t)]

}− λ(t)q(t) + δ(t)q(t) + γ(t)x(t)

which gives the following maximum principle conditions:

δH

δq(t)= 0 ⇒ e−vt [p− c′ [q(t)]−R′ [q(t)]]− λ(t) + δ(t) = 0 (1.13)

δH

δx(t)= − λ(t) ⇒ γ(t) = − λ(t) (1.14)

x(t) = −q(t) (1.15)

δ(t)q(t) = 0 ∀t (1.16)

γ(t)x(t) = 0 ∀t (1.17)

TV C : λ(T ) = 0 (1.18)


If x(t) > 0, then γ(t) = 0 . Therefore λ(t) = 0, which means the marginal benefit of leaving

one unit in situ remains constant over time. Now, for q(t) > 0 and x(t) > 0, equation (1.13)

becomes: 7

e−vt [p− c′ [q(t)]−R′ [q(t)]] = λ(t)

⇒ λ(t) = e−vt [−c′′ [q(t)] q(t)−R′′ [q(t)] q(t)]− ve−vt [p− c′ [q(t)]−R′ [q(t)]](1.19)

Using λ(t) = 0 we have;

−c′′ [q(t)] q(t)−R′′ [q(t)] q(t)p− c′ [q(t)]−R′ [q(t)]

= v (1.20)

x(t) = −q(t) (1.21)

Equation (1.20) will give rise to the optimal path of q(t), and given the optimal path of

q(t), equation (1.21) will give the optimal path of x(t) > 0. Notice that, in contrast to the

SP path in (1.9), damages and external costs are not present in the private extraction rate

path. Comparing these two paths at least highlights the importance of social costs ignored by

the private extractor. If, for example, we assume that second order damage and externality

effects are small, that is, if Eqq = Dqq = 0, and if we consider a constant royalty payment

case, then the private extractor’s optimal path differs from that of the SP only through the

marginal external and damage costs. The SP path becomes:

−c′′ [q(t)] q

p− c′ [q(t)]−Dq [q(t), l]− Eq [q(t)]= v

The private extractor’s path is identical in this case, except that Dq[q(t), l] = Eq[q(t)] = 0.

Thus as long as net benefits from extraction are positive, the SP chooses a lower extraction

path than the private extractor over time given convexity of extraction costs.

Another result follows from the comparative dynamics of the private extractor’s optimal so-

lution. In Section 1.7, we show that resource price and initial stock have positive effects on

the extractor’s profit, and as in the SP problem, leaving resources unextracted (that would

be extracted under the optimal plan) reduces the maximum present value of the extractor’s

7If q(t) = 0 then δ(t) > 0. Therefore e−vt [p− c′ [q(t)]−R′ [q(t)]] < λ(t), which means the extraction rateis zero because, the present value of extracting one unit of resource is less than the marginal benefit of leavingone unit of the resource in situ.


profit - whenever the ending stock increases, the maximum present value of the extractor’s

profit gets smaller. Unlike the SP, however, the maximum present value of the extractor’s

profit will always increase for a longer time horizon. This is an important difference that we

will return to later in the simulation.

1.3.2 Endogenous Contract Duration

The current practice in fracturing of having a given five-year initial contract might not be

optimal. We now examine how optimal contract length differs between the social planner and

the private extractor when it is endogenous, and we assess its impact on their optimal paths

of extraction. We now assume that the SP chooses both terminal time T and the terminal

stock xT along with the extraction rate, q(t) . This leads to an optimal control problem with

a free endpoint and variable time.

Social Planner

The SP problem for an endogenous contract length becomes:

maxq(t), T, xT

∫ T0e−vt {pq(t)− c [q(t)]−D [q(t), l]− E [q(t)]} dt+ ϕ (xT , T ) e−vT

S.T. x(t) = −q(t), x (0) = x0

0 ≤ q(t) ≤ x(t), x(t) ≥ 0∫ T0q(t)dt = x0 − xT

(1.22)

This problem is solved in three stages (Caputo 2005):

Stage-1: We treat T and xT as fixed parameters and solve for x (t;α, η) , q (t;α, η) , λ (t;α, η) , µ (t;α, η)

and V (α, η), where η = (x0, T, xT ), and where α is a vector of time invariant parameters in

the problem.

Stage-2: Find the optimal T and xT by solving V ∗ (θ) = maxT,xT

V (α, η). This gives us

x∗T (α), T ∗(α), and V ∗ (θ) = V (α, x0, T∗, x∗T ), which are functions of the time invariant pa-

rameter vector.


Stage-3: Substituting x∗T (α) and T ∗(α) in x (t;α, η) , q (t;α, η) , λ (t;α, η) , µ (t;α, η) and V (α, η),

we obtain x∗ (t; θ) , q∗ (t; θ) , λ∗ (t; θ) , and µ∗ (t; θ).

The Hamiltonian function is therefore:

H = e−vt {pq(t)− c [q(t)]−D [q(t), l]− E [q(t)]} − λ(t)q(t) + δ(t)q(t) + γ(t)x(t)

The set of time invariant parameters is α = (p, v), and the first order conditions are,

δH

δq(t)= 0 ⇒ e−vt [p− c′ [q(t)]−Dq [q(t), l]− Eq [q(t)]]− λ(t) + δ(t) = 0(1.23)

δH

δx(t)= − λ(t) ⇒ γ(t) = − λ(t)(1.24)

x(t) = −q(t)(1.25)

δ(t)q(t) = 0 ∀t(1.26)

γ(t)x(t) = 0 ∀t(1.27)

T : H [T ∗(α), x∗ (T ∗(α);α) , q∗ (T ∗(α);α) , λ∗ (T ∗(α);α) ;α] = −δϕ (x∗T (α), T ∗(α))

δT(1.28)

⇒ e−vT∗(α) {pq∗ (T ∗(α);α)− c [q∗ (T ∗(α);α)]−D [q∗ (T ∗(α);α) , l]− E [q∗ (T ∗(α);α)]}

−λ∗ (T ∗(α);α) q∗ (T ∗(α);α) = −δϕ (x∗T (α), T ∗(α))

δT

xT : λ∗(T ∗(α);α) =δϕ (x∗T (α), T ∗(α))

δxT(1.29)

Note that (1.28)-(1.29) are transversality conditions for the contact length and the ending re-

source stock respectively . Using these conditions, we can solve for x∗T (α), T ∗(α), x∗ (t;α) , q∗ (t;α) , λ∗ (t;α)

and µ∗ (t;α). As in the preceding case we can note that the transversality condition is dif-

ferent than it is for the private extractor’s problem, as in the latter it must be true that

λ∗(T ;α) = 0. Therefore, we can expect different optimal values of T for the private extractor

and the SP.

For q(t) > 0 and x(t) > 0, condition (1.23) becomes;


e−vt [p− c′ [q(t)]−Dq [q(t), l]− Eq [q(t)]] = λ(t)

⇒ λ(t) = e−vt [−c′′ [q(t)]−Dqq [q(t), l]− Eqq [q(t)]] q(t)− ve−vt [p− c′ [q(t)]−Dq [q(t), l]− Eq [q(t)]]

(1.30)

Using λ(t) = 0 we have a new representation of the SP extraction path under an endogenous

contract length:

[−c′′ [q(t)]−Dqq [q(t), l]− Eqq [q(t)]] q(t)

p− c′ [q(t)]−Dq [q(t), l]− Eq [q(t)]= v (1.31)

x(t) = −q(t) (1.32)

Equations (1.31) and (1.32) give the optimal paths for q(t) and x(t) for the social planner,

which we label as q (t;α) , x (t;α). The form of this path is identical to the exogenous contract

length case in (1.9) although the precise location of the path over time is different. After

deriving V (α, x0, T, xT ), we can use the two transversality conditions (equations 1.28 and

1.29) to obtain x∗T (α) and T ∗(α).8

Comparative dynamics results for price and initial stock of this setup are the same as for

previous cases. The effect of ending stock and time horizon can not be determined analyti-

cally as both are choice variables, and therefore determined simultaneously.

Private Extraction Firm:

The private extractor chooses T and q(t) under the constraint that x∗ (T ∗(α);α) ≥ A, which

says the stock of resources at the end of the production period will be at least equal to A.9 This constraint is often present in contract negotiations with the CLO, particularly under

conditions for renewal. The extractor’s problem now is written:

8Solving maxT,xT

(α, x0, T, xT ), we obtain x∗T (α) and T ∗(α).

9This constraint can result from the contract renewal conditions.


maxq(t),T,xT

∫ T0e−vt

{pq(t)− c [q(t)]− r(l)−R [q(t)]

}dt

S.T. x(t) = −q(t), x (0) = x0, x (T ) ≥ A

0 ≤ q(t) ≤ x(t), x(t) ≥ 0∫ T0q(t)dt = x0 − xT

(1.33)

The present valued Hamiltonian function is given as

H = e−vt {pq(t)− c [q(t)]− r(l)−R [q(t)]} − λ(t)q(t) + δ(t)q(t) + γ(t)x(t)

The first order conditions making use of the same time invariant parameters as the SP

problem are as follows:

δH

δq(t)= 0 ⇒ e−vt [p− c′ [q(t)]−R′ [q(t)]]− λ(t) + δ(t) = 0(1.34)

δH

δx(t)= − λ(t) ⇒ γ(t) = − λ(t)(1.35)

x(t) = −q(t)(1.36)

δ(t)q(t) = 0 ∀t(1.37)

γ(t)x(t) = 0 ∀t(1.38)

free T, e−vT∗(α) {pq∗(T ∗(α);α)− c [q∗(T ∗(α);α)]− r(l)−R [q∗(T ∗(α);α)]}(1.39)

−λ∗(T ∗(α);α)q∗(T ∗(α);α) = 0

xT , λ∗(T ∗(α);α) ≥ 0, [x∗(T ∗(α);α)− A] ≥ 0 ; λ∗(T ∗(α);α) [x∗(T ∗(α);α)− A] = 0(1.40)

Where (1.39) and (1.40) are transversality conditions for the contract length and ending

resource stock respectively. If x(t) > 0 then γ(t) = 0. Therefore λ(t) = 0 , which means that

the marginal benefit of leaving one unit in the ground remains the same over time. On the

other hand, if q(t) > 0 and x(t) > 0, then condition (1.34) becomes;

e−vt [p− c′ [q(t)]−R′ [q(t)]] = λ(t)

⇒ λ(t) = e−vt [−c′′ [q(t)] q(t)−R′′ [q(t)] q(t)]− ve−vt [p− c′ [q(t)]−R′ [q(t)]](1.41)

Using λ(t) = 0 we have;


−c′′ [q(t)] q(t)−R′′ [q(t)] q(t)p− c′ [q(t)]−R′ [q(t)]

= v (1.42)

x(t) = −q(t) (1.43)

Equations (1.42), (1.43) describe the optimal paths for q(t) and x(t) for the private extractor,

which we label as q (t;α) and x (t;α). We can then use the two tranversality conditions (1.39

and 1.40) to obtain x∗T (α) and T ∗(α).

1.4 Model Implications

The path results show that, for both the social planner and private extractor, the optimal

extraction rate for hydraulic fracturing is decreasing over time. This is clear from the path

results in both exogenous and endogenous contract length cases given in equations (1.9),

(1.20), (1.31) and (1.42). In all of these conditions, the denominator on the left side and

the market interest rate v are positive. Thus, the numerators must be positive and this

requires q(t) < 0. This seemingly counter-intuitive result is actually supported by the em-

pirical literature on hydraulic fracturing. For example, Considine (2010) and Duman (2012)

use extraction data to show that the majority of gas volumes are obtained during the first

few years of fracturing. These studies also find evidence concerning higher extraction rates

in initial periods of fracturing contracts compared to later periods.

We can go further and use our conditions to examine the relative magnitude of extraction

rates of change through time for social planner and private firm, [q(t)]. Because the discount

rate v is time invariant, and because the SP and private extractor face the same interest rate

by assumption, we can use (9), (20), (31) and (42) to show:

[c′′ [q(t)] +Dqq [q(t), l] + Eqq [q(t)]] q(t)

p− c′ [q(t)]−Dq [q(t), l]− Eq [q(t)]

∣∣∣∣SP

=[c′′ [q(t)] +R′′ [q(t)]] q(t)

p− c′ [q(t)]−R′ [q(t)]

∣∣∣∣P

(1.44)

where the label “P” refers to the private firm and “SP” refers to the social planner. The left

side of equation (1.44) represents the SP while the right side is for the private extractor. For

each side, both the numerator and the denominator are positive. The denominators represent

the marginal payoff from extracting net of costs important to the type of extractor, while


the numerators show the change in net marginal payoffs for an extra unit of extraction.

We know that the marginal payoff is decreasing in the extraction of resources.10 Now, from

equation (1.44) we can obtain,

[c′′ [q(t)] +Dqq [q(t), l] + Eqq [q(t)]]

p− c′ [q(t)]−Dq [q(t), l]− Eq [q(t)]>

[c′′ [q(t)] +R′′ [q(t)]]

p− c′ [q(t)]−R′ [q(t)]

∣∣∣∣P

⇒ q(t)|SP < q(t)|P(1.45)

Equation (1.45) implies that if the marginal payoff of the SP decreases at a relatively higher

rate compared to that of the private extractor, then the optimal rate of change in the ex-

traction rate for the SP will be smaller than that of the private extractor through time.

Condition (1.45) is expected, because both extractors face the same market price and pro-

duction cost, but the damages that the social planner accounts for include on-site costs to

the CLO and additional off-site costs to the public at large. Thus, total damages for the

social planner likely increase at a higher rate with extraction compared to the increase in

royalty costs paid by the private extractor. Given the empirical result that private extraction

rates under hydraulic fracturing have been observed to decrease over time, we can therefore

conclude from (1.45) that the decreasing rate of extraction observed in practice is higher than

it would be in a first best case, i.e., if a social planner were involved with hydraulic fracturing.

It is important to recall that these results hold only under the restrictive assumption of an

exogenous contract duration, which is the rule in practice. Interestingly, once this assump-

tion is relaxed, an endogenous contract duration does not provide as clear cut of a result,

however. We can no longer easily compare the optimal contract duration for the SP and

the private extractor under full generality because xT and T are determined simultaneously.

However, we can show from equation (1.40) that, should the private extractor face a con-

straint on extraction often set through contract terms, if the private extractor leaves more

resources unextracted than is required, i.e., x∗ (T ∗(α);α) > A, where A is the set level of

maximum extraction, then the marginal net benefit of leaving the resource in the ground is

zero at this point, λ∗ (T ∗(α);α) = 0. On the other hand, from equation (1.29), the marginal

net benefit of leaving resource in situ for the SP is always positive because we have shown

that λ∗ (T ∗(α);α) > 0. We also know that λ∗(t) is decreasing over time.11 Therefore, under

10We need this assumption to get the optimal solution because if the marginal payoff is increasing inextraction we will not have any optimal solution.

11λ∗(t) > 0 when the extractor extracts everything the optimal situation and λ∗(t) = 0 when it is optimalfor the extractor to leave positive amount of resources unextracted.


this scenario we can conclude that, holding other things equal, if the SP induces the private

extractor to keep more than the extractor prefers in situ, then the optimal contract length

will always be longer.

Endogenous Contract Duration Model with policy in-

strument:

It takes years to realize the adverse effects of unsustainable extraction with hydraulic frac-

turing (Myers 2012). US states vary in their views about the potential damages emanated

from hydraulic fracturing (Richardson et al. 2013). States like Texas and North Dakota are

positively disposed to the expansion of this new technology, whereas Pennsylvania takes a

much more cautionary approach. At the extreme end of the spectrum, New York has put

a moratorium on the use of hydraulic fracturing. States that place more weight on external

damages inflicted on society may require the extraction firms to care more about the envi-

ronment through the use of policy instruments, such as bonds, quotas, fines and taxes. In

many states, extractors are required to build waste-water treatment facilities and reuse their

fracturing fluid, whereas in other states these requirements do not exist.

In this section, we introduce a policy instrument for the SP, and we let the SP have differ-

ent weights on the extractor’s rent versus the external damages it imposes. We assume the

weights are determined exogenously through administrative processes and political climate

in a given state that reflect SP’s preference for damages vs. the extraction rent of extractors.

To incorporate this feature in our model we introduce ω ∈ [0, 1] and (1 − ω) that indicate

the weight the regulator assigns to external damages and rent from extraction, respectively.

Whenever ω = 1, SP cares only about the environment and for ω = 0 the private extrac-

tor’s problem remains the same as in the absence of regulation, so the SP cares only about

extraction rent. In this section explore how the optimal private solution paths change with

different relative weights used by the regulator.

If we incorporate the preferences of the regulator into the private extractor’s problem, it

takes the following form:


maxq(t),T,xT

∫ T0e−vt {(1− ω) [pq(t)− c [q(t)]− r(l)−R [q(t)]]− ωE [q(t)]} dt

S.T. x(t) = −q(t), x (0) = x0, x (T ) ≥ A

0 ≤ q(t) ≤ x(t), x(t) ≥ 0∫ T0q(t)dt = x0 − xT

(1.46)

Using the maximum principle theorem and from the necessary conditions, the path of ex-

traction can be shown to equal:

[(1− ω) [−c′′ [q(t)]−R′′ [q(t)]]− ωEqq [q(t)]] q(t)

(1− ω) [p− c′ [q(t)]−R′ [q(t)]]− ωEq [q(t)]= v (1.47)

x(t) = −q(t) (1.48)

The optimal path of extraction q(t) we get from equations (1.47) and (1.48) depends on the

weight assigned by the SP. For different values of ω the private extractor’s problem gives

different level of social welfare. Using this value of social welfare and that for the first best

solution we can derive social cost. The SP then can use a severance tax to reduce the social

cost as much as possible. Another way of looking at this problem is that the SP will use the

tax so that the extraction path corresponding to ω gets as close to the first best extraction

path as possible. Let τ be the tax rate imposed on extraction. The private extractor’s

problem becomes:

maxq(t),T,xT

∫ T0e−vt {(1− ω) [(p− τ)q(t)− c [q(t)]− r(l)−R [q(t)]]− ωE [q(t)]} dt

S.T. x(t) = −q(t), x (0) = x0, x (T ) ≥ A

0 ≤ q(t) ≤ x(t), x(t) ≥ 0∫ T0q(t)dt = x0 − xT

(1.49)

In principle, we can expect the tax to be equal to the value of marginal damage from extrac-

tion. It is evident that the social cost depends on ω. Therefore, the optimal tax will also

depend on the weight assigned by the SP. If ω is higher, i.e. if the SP values the environ-

mental damage relatively more, the required tax will be lower to bring the extraction path

closer to the first best solution. On the other hand, if ω is low we expect the optimal tax

to be higher to reduce social cost. Using the maximum principle theorem and the necessary

conditions we can derive τ ∗ = p− c′ [q(t)]−R′[q(t)] − ω

(1− ω)Eq [q(t)], which supports our


expectation that the optimal tax rate is decreasing in the SP’s preference for environmental

damage.

δτ

δω= − 1

(1− ω)2Eq [q(t)]

This result implies that in order to bring the private extraction path as close to the first

best solution as possible, states having more favorable political climate towards hydraulic

fracturing require a higher severance tax.

1.5 Simulations:

We turn to a simulation in order to investigate the model implications described above.

We will specifically consider the social planner’s and private extractor’s problems under both

fixed and endogenous contract durations. More importantly, we will derive a social cost value

that is conditional on the differences in behavior for both types of extractors in both contract

cases. The simulation will be calibrated using reported extraction and environmental costs

for hydraulic fracturing in the empirical literature to the extent possible.

1.5.1 Function settings

Common parameters

Following the control problems developed in equations (1.1), (1.11), (1.22), and (1.33), and

to allow for a realistically high number of decision periods we choose “days” as our time

period of interest. Thus, the problem with a fixed five-year horizon comprises 1,825 days.

We choose an initial stock of x0 = 1800 million cubic feet (mmcf) for all simulation rounds.

According to Laurenzi and Jersey (2013) this corresponds to the average estimated ultimate

recovery (EUR) per well in the Marcellus shale. For the (time-invariant) discount rate ν

we choose 10% per year, or approximately 0.0274% per day. This figure is cited in Duman

(2012) as the minimum acceptable return in the natural gas industry.12.

12See also Aguilera (2014)


We set market price p = $5000 per mmcf, which is conservatively located between the lower

bound and the average of the predicted price range for natural gas between 2015 and 2040

(EIA 2015). Royalties to land owners are set to 18%, which is towards the upper end of the

typical rate in the Marcellus shale (Green 2012).13

Private extraction firm’s problem

Omitting time subscripts for simplicity, the firm’s instantaneous profit function is given as

P = (1−R)pq − αq2

where extraction q is measured in million cubic feet (mmcf), p is the market price (assumed

constant over time), R is the royalty rate, and α is an extraction cost parameter. Settings

for market price and royalty rate are explained above.

The cost function satisfies the convex properties described in the theoretical section. We

choose α = 500 to achieve the two additional desirable properties: (i) instantaneous profits

are positive for 0 < q ≤ 8.2 mmcf, and maximized at q = 4.1 mmcf, which corresponds well

to the range of typical daily extraction rates (Duman (2012); Vidic et al. (2013)), and (ii)

approximately half of the initial stock is extracted within the first two years in the private

firrm / 5-year fixed horizon problem, which has been widely observed in the field (Considine

2010). The first property is depicted in the left hand panel of Figure-1.1, and the second can

be gleaned from Figure-1.3.

Social Planner (SP) problem

The SP’s instantaneous welfare function for the simulation is given as

W = pq − αq2 − βq3

where βq3 represents the societal damage function. This function satisfied the theoretical

properties outlined earlier in the text. We choose an initial setting for the off-site damage pa-

rameter β of 100, and subsequently perform a sensitivity analysis for three additional settings

(150, 200, 250). The instantaneous welfare functions for all four settings of β are depicted in

13The legal minimum royalty equals 12.5% in Pennsylvania (Green 2012).


Figure-1.1. As would be expected, all four span a narrower range of positive welfare (up to

5.1 mmcf for β = 100), and are maximized at lower extraction points (e.g. 2.75 mmcf for β

= 100) compared to the private firm’s instantaneous profit. From Figure-1.1, it is interesting

to note that as β increases, not only does welfare peak at a much lower extraction level, but

the wedge between profit and welfare as β doubles is about 25% measured in terms of the

firm’s profits - thus, the social costs of private extraction are expected to be large. We return

to this below.

The SP problem also includes a salvage function. Following Ikefuji et al. (2010) we specify

this function to be additively separable from the net present value of W . It takes the following

explicit form:

S = exp(νT ) ∗ γxT

where T is the terminal time period (in days), xT is the stock at the terminal period, and

γ is a salvage function parameter. We set γ equal to 50% of the market price, or $2500 per

mmcf.

1.5.2 Fixed 5-year time horizon

Figure-1.2 shows the optimal extraction and state paths for the firm (solid line) and the

social planner (dashed line) for the fixed, five-year time horizon, and a damage parameter of

β = 200. As prescribed by our theoretical model, the SP extracts a smaller initial amount

and proceeds at a slower rate than the private firm. This leads to a more gradual depletion

of the gas deposit, which can be gleaned from the state trajectory in the second panel of

Figure-1.2, as well as the cumulative extraction paths in Figure-1.3.

Figure-1.4 depicts extraction and state paths for the SP under all four settings of the damage

parameter β. Not surprisingly, extraction proceeds at a slower pace and depletion at a slower

rate with increasing marginal societal damages.


1.5.3 Open time horizon

Extraction paths and state variable trajectories for the open time horizon (endogenous con-

tract duration) scenario are given in Figure-1.5. The most important insight form this figure

is that both the firm and SP extract for a time horizon that is approximately twice as long

as the standard five year contract omnipresent in hydraulic fracturing. Specifically, the firm

operates for 3728 days, while the SP takes 3916 days to deplete the stock for this setting

of β (see Table-1.8). As before, this leads to a more gradual extraction pace for the SP, as

is evident from both panels of figure 5, as well as the comparison of cumulative extraction

shown in Figure-1.6.

Figure-1.7 shows the social planner’s extraction path and state trajectories for all four settings

of the damage parameter β. As is evident from the figure, the extraction horizon increases

with increasing instantaneous externalities, which naturally implies a commensurately slower

pace of extraction.

1.5.4 Social cost

Table-1.8 summarizes our main simulation results. The table shows four pairs of rows, each

corresponding to a different damage parameter β. Within each pair, the first row depicts

results for the five-year time horizon (exogenous contract duration), while the second row

gives results for the open-horizon problem (endogenous contract duration). The first two

numeric columns show extraction duration, in days, for the firm and SP, respectively, the

next two columns indicate profits and, respectively, welfare, in 1000 dollars, and the last col-

umn shows the social cost of private extraction. The latter was computed as the difference

between maximized NPV of welfare for the SP and the NPV of “counterfactual welfare”,

obtained by inserting the private firm’s optimal sequence of daily extraction into the SP’s

welfare function (equation 1.1), then integrating discounted values over the SP’s optimal time

horizon.

The most important result captured in the table is that the NPV of both profits and social

welfare increase substantially (by approximately 20%) when we relax the time horizon for ex-

traction. Second, optimal open duration for extraction exceeds 10 years for the private firm,

and ranges between 10 and 11 years for the SP. Third, social costs increase with marginal


societal damages (higher values of β) for both time horizon scenarios. However, social costs

under endogenous contract duration amount to only a fraction of social costs under a fixed

duration, as is evident by comparing the cost pairs in the last columns of Table-1.8. Another

interesting result from the social cost computations is that the costs associated with the

externalities in terms of off site costs are orders of magnitude higher when the fixed contract

duration present in practice is imposed on our problem, although the difference is reduced

somewhat as the damages become very high. As damages become high, the gap between

social costs closes somewhat (it is still significant), but this is because the damages of any

extraction early in the mining era are high, and discounting tends to reduce these damages

when they occur later in the mining period.

Another result relevant for policy here is that instruments targeting changes to incentives of

private extractors in ways that encourage them to extract more like the social planner are

more important in the current practice of fixed contracts. The marginal net benefit of using

any instrument in terms of reduced social costs is highest for this case relevant to the endoge-

nous contract duration case. In fact, if contract duration was instead endogenous, then for

relatively lower but still significant damages (β= 100 and 150), an alternative to regulation

via a price instrument such as a tax would be to simply allow different length contracts. Such

a policy would reduce social costs at perhaps less cost to firms and landowners.

In summary, across a range of cases we have shown in the simulation and described in our

model, relaxation of the standard five year contract now present in hydraulic fracturing

industry would afford considerable social gains.

1.6 Conclusion:

We have examined the common practice of fixed contracts in hydraulic fracking in a relatively

simple model, both to understand how private extraction paths differ from first best extrac-

tion that internalizes all offsite costs, and to understand the pattern of social costs that can

arise over time in these problems. Path results comparing first best and private extraction

under exogenous and endogenous contract lengths and for private and social extractors are

derived. A simulation calibrated based on published data and results from fracking problems

is used to compare social and private extraction under various parameters, to understand

how optimal contract lengths differ from the common fixed ones found in practice, and to


compute the social costs associated with the wedge between extraction rates and non-optimal

fixed contracts.

In practice, fracturing contracts between landowner and extractor have been fixed at the

historical five year length common in conventional drilling. Our work is among only a few

studies that have examined contract length in exhaustible resource problems, and the first

study of hydraulic fracturing extraction and social costs that we are aware of. Given the in-

creasing and widespread practice of hydraulic fracturing, and the obvious wedge that exists

between private and social mining decisions in the face of potentially high off site costs, our

results will aid future policy development as well as open new avenues for understanding the

exhaustible resource economics behind this important industry.

Contract length and the extraction rates are closely linked; in particular, we show that ex-

traction rates are actually decreasing over time regardless of social costs, for both private

and social decision makers. This result is confirmation of the recent empirical observations

made for hydraulically fractured gas extraction although this is not generally a result found

in conventional mining theories. Interestingly, under a range of conditions where off-site

costs are important through time as gas is extracted, we also show that extraction rates

decline faster under private extraction than under socially best extraction for exogenously

given contract lengths. Once however we allow for endogenous contract duration, this may

not be the case and instead we have a counter example to empirical results found under the

current state of exogenous contract lengths for this industry. There may also be cases where

the optimal contract length is shorter or longer than the current five year practice for the

industry, although we find in plausible cases that optimal contract duration is longer. This

result depends on the in situ resource that may be required at the end of mining: if the

social planner desires a larger stock then a private extractor prefers, then contract lengths

are longer than in the exogenous case.

Our calibrated simulation verifies the theory results and offers additional intuition concern-

ing policy instruments targeting extraction, which are currently absent in the industry. One

goal of policy is always to achieve a reduction in social costs, under either exogenous or

endogenous contract lengths, whereby a private extraction path is distorted in a manner that

converges to the socially optimal one internalizing all costs associated with extraction. Inter-

estingly and somewhat surprisingly, we find that simply allowing a flexible contract length

may reduce social costs more than a specific application of a price instrument, over a range


of parameters defining on- and off-site costs.


1.7 Appendix: A

Comparative Dynamics

Exogenous contract duration and social planner

In this scenario the set of parameters is β = (p, v, x0, T, xT ) and the parametrized optimal

value function is,

V (β) =∫ T0e−vt {pq∗ (t, β)− c [q∗ (t, β)]−D [q∗ (t, β) , l]− E [q∗ (t, β)]} dt+ ϕ (xT , T ) e−vt

Using a dynamic envelope theorem, we can determine how the maximum present value of

social welfare is affected when values of the price of resource, the initial stock, the time

horizon and the ending stock change. We have the following results:

δV (β)

δp=∫ T0

δH(.)

δp

∣∣∣∣Optimal Path

dt

⇒∫ T0q∗(t; β) e−vtdt > 0

(1.50)

δV (β)

δx0= λ (0; β) > 0 (1.51)

δV (β)

δT= e−vT {pq∗ (T, β)− c [q∗ (T, β)]−D [q∗ (T, β) , l]− E [q∗ (T, β)]} − λ(T, β)q∗ (T, β)

⇒ e−vT {pq∗ (T, β)− c [q∗ (T, β)]−D [q∗ (T, β) , l]− E [q∗ (T, β)]} − δϕ (xT , T )

δxTq∗ (T, β) ,

(1.52)

Equation (1.50) states that an increase in the resource price will increase the maximum

present value of social welfare. From the instantaneous welfare function, we also see that as

the price of the resource increases, welfare rises. Thus the result in equation (1.50) is consis-

tent with our expectation. On the other hand, a larger initial stock means that the SP has

a larger reserve to extract from, which should increase the present value of optimal payoff.

Therefore, equation (1.51) shows that an increase in the resource stock at the beginning of

extraction increases the maximum present value of social welfare.

In equation (1.52), the transversality condition that λ(T ) =δϕ (xT , T )

δxThas been used and


defines contract length (this is recursive). This result suggests that the effect of increasing

the horizon on the SP’s maximum present value depends on the magnitude of the marginal

impact of ending stock on scrap value. The first part of equation (1.52) is the discounted

instantaneous social welfare function that must be non-negative if q∗ > 0. Because this part

is negative, we have a trivial case where q∗ = 0. The second part of the equation (1.52) is

the marginal impact of the ending stock on scrap value. From our assumption that ending

stock has a positive impact on the scrap value function (ϕxT (.) > 0), we can conclude from

equation (1.52) that

δV (β)

δT> 0⇔ e−vT {pq∗ (T, β)− c [q∗ (T, β)]−D [q∗ (T, β) , l]− E [q∗ (T, β)]}

q∗ (T, β)>δϕ (xT, T )

δxT(1.53)

That is, if the average discounted social welfare is larger than marginal impact of the ending

stock on the scrap value, for any longer time horizon the maximized present value of social

welfare will increase. This implies that the SP will be better off with a longer contract at the

optimal level if the average discounted payoff for extraction is larger than marginal value of

leaving the resource in situ. In other words, the SP would pay upto an amount

e−vT {pq∗ (T, β)− c [q∗ (T, β)]−D [q∗ (T, β) , l]− E [q∗ (T, β)]} − δϕ (xT, T )

δxTq∗ (T, β) to have

the planning horizon extended, thus,

δV (β)

δxT= −λ (T ; β) < 0 (1.54)

In equation (1.54), xT is the ending stock when SP has an optimal extraction path. Therefore,

leaving resources un-extracted (that would be extracted under the optimal plan), reduces the

maximum present value of social welfare.

Exogenous contract duration and private extractor

In this scenario the set of parameters is β = (p, v, x0, T, xT ) and the parameterized optimal

value function is,

V (β) =∫ T0e−vt {pq∗(t; β)− c [q∗(t; β)]− r(l)−R [q∗(t; β)]} dt

Using the dynamic envelope theorem we explore the effects on the maximum present value


of the extractor’s profit when the values of the price of resource, the initial stock, the time

horizon and the ending stock changes.

δV (β)

δp=∫ T0

δH(.)

δp


dt

⇒∫ T0q∗(t; β) e−vtdt > 0

(1.55)

δV (β)

δx0= λ (0; β) > 0 (1.56)

Equation (1.55) says an increase in the resource price will increase the maximum present

value of extractor’s profit. Similarly, equation (1.56) says, an increase in initial stock at

the starting period increases the maximum present value of extractor’s profit. Using the

transversality condition that λ(T ) = 0, we get the following result

δV (β)

δT= e−vT {pq∗ (T, β)− c [q∗ (T, β)]− r(l)−R [q∗ (T, β)]} − λ (T, β) q∗ (T, β)

⇒ e−vT {pq∗ (T, β)− c [q∗ (T, β)]− r(l)−R [q∗ (T, β)]} > 0,(1.57)

This result suggests that whenever the time horizon increases, the maximum present value

of the extractor’s profit will also increase. In other words, the extractor would pay upto

the amount e−vT {pq∗ (T, β)− c [q∗ (T, β)]− r(l)−R [q∗ (T, β)]} to have the planning horizon

extended.

δV (β)

δxT= −λ (T ; β) < 0 (1.58)

In equation (1.58) xT is the ending stock when the extractor has an optimal extraction path.

As in the SP’s case leaving resources un-extracted (that would be extracted under the opti-

mal plan) reduces the maximum present value of extractor’s profit. Therefore, whenever the

ending stock increases the maximum present value of the extractor’s profit gets smaller.


Endogenous contract duration and social planner

In this case the set of exogenous parameters is β = (p, v, x0), and the parametrized optimal

value function is

V (β) =∫ T ∗(α)0

e−vt {pq∗ (t, β)− c [q∗ (t, β)]−D [q∗ (t, β) , l]− E [q∗ (t, β)]} dt+ϕ (x∗T (α), T ∗(α)) e−vT∗(α)

Using the dynamic envelope theorem we can find how changes in the values of the resource

price and the initial stock affect the maximized present value of social welfare:

δV (β)

δp=∫ T ∗(α)0

δH(.)

δp


dt

⇒∫ T ∗(α)0

q∗(t; β) e−vtdt > 0

(1.59)

δV (β)

δx0= λ (0; β) > 0 (1.60)


present value of social welfare. Similarly, from equation (1.60) we can say an increase in

stock at the starting period increases the maximum present value of social welfare.

Endogenous contract duration and private extractor:

In this problem the set of parameters is β = (p, v, x0). The parameterized optimal value

function is,

V (β) =∫ T ∗(α)0

e−vt {pq∗(t; β)− c [q∗(t; β)]− r(l)−R [q∗(t; β)]} dt

Using the dynamic envelope theorem we now explore how the maximized present value of

the extractor’s profit changes for a change in the values of the resource price and the initial

resource stock.

δV (β)

δp=∫ T ∗(α)0

δH(.)

δp


dt

⇒∫ T ∗(α)0


(1.61)

δV (β)

δx0= λ (0; β) > 0 (1.62)



present value of extractor’s profit. Similarly, equation (1.62) implies that an increase in

stock at the starting period increases the maximum present value of extractor’s profit.14

Endogenous T with policy instrument:

The parametrized optimal value function is,

V (β) =∫ T ∗(α)0

e−vt {pq∗(t; β)− c [q∗(t; β)]− r(l)−R [q∗(t; β)]− ωE [q∗(t; β)]} dt

Using the dynamic envelope theorem, we now explore how the maximized present value of

the extractor’s profit changes for the change in the values of the resource price, the initial

resource stock and the weight put on the externality by the SP.

δV (β)

δp=∫ T ∗(α)0

δH(.)

δp


dt

⇒∫ T ∗(α)0


(1.63)

δV (β)

δx0= λ (0; β) > 0 (1.64)

Equation (1.63) says an increase in the resource price will increase the maximum present

value of extractor’s profit. Similarly, from equation (1.64) we see an increase in stock at the

starting period increases the maximum present value of extractor’s profit.

δV (β)

δω=∫ T ∗(α)0

δH(.)

δω


dt

⇒ −∫ T ∗(α)0

E [q∗ (t; β)] e−vtdt < 0

(1.65)

This shows that when the SP make the private extractor care more about external damages,

her maximum present value of profit decreases.

14under this setup, in both the SP and Private extractor models we do not deriveδV (β)

δTand

δV (β)

δxT,

because T and xT are not fixed parameters. These are decision variables.


Graphs and Table

extraction (mmcf)0 2 4 6 8

prof

its ($

1000

s)

-2

0

2

4

6

8

10

Max. = 8.405

instantaneous profit function

extraction (mmcf)0 2 4 6 8

prof

its ($

1000

s)-2

0

2

4

6

8

10

Max. = 7.889

instantaneous welfare function

β = 100

β = 150

β = 200

β = 250

Figure 1.1: Instantaneous Functions

time (days)0 500 1000 1500

extra

ctio

n (m

mcf

s)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2optimal extraction

privateSP

time (days)0 500 1000 1500

rem

aini

ng re

sour

ce (m

mcf

s)

0

200

400

600

800

1000

1200

1400

1600

1800optimal state path

privateSP

Figure 1.2: Extraction and state, fixed time (β=200)


time (days)0 200 400 600 800 1000 1200 1400 1600 1800

shar

e ex

tract

ed

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1cumulative extraction

privateSP

Figure 1.3: Cumulative extraction, fixed time (β=200)

time (days)0 500 1000 1500

extra

ctio

n (m

mcf

s)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2optimal extraction

β = 100

β = 150

β = 200

β = 250

time (days)0 500 1000 1500

rem

aini

ng re

sour

ce (m

mcf

s)

0

200

400

600

800

1000

1200

1400

1600


β = 100

β = 150

β = 200

β = 250

Figure 1.4: SP extraction and state under different damage parameters, fixed time


time (days)0 1000 2000 3000

extra

ctio

n (m

mcf

s)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2optimal extraction

privateSP

time (days)0 1000 2000 3000

rem

aini

ng re

sour

ce (m

mcf

s)0

200

400

600

800

1000

1200

1400

1600


privateSP

Figure 1.5: Extraction and state, open time (β=200)

time (days)0 500 1000 1500 2000 2500 3000 3500

shar

e ex

tract

ed

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1cumulative extraction

privateSP

Figure 1.6: Cumulative extraction, open time (β=200)


time (days)0 1000 2000 3000 4000

rem

aini

ng re

sour

ce (m

mcf

s)

0

200

400

600

800

1000

1200

1400

1600


β = 100

β = 150

β = 200

β = 250

time (days)0 1000 2000 3000 4000

extra

ctio

n (m

mcf

s)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1optimal extraction

β = 100

β = 150

β = 200

β = 250

Figure 1.7: SP extraction and state under different damage parameters, open time

time periods (days) profit / welfare ($1000s) Soc.Costscenario firm SP firm SP ($1000s)

β = 100T = 5 years 1,825 1825 $5,248 $6,370 $15T = open 3728 3672 $6,243 $7,655 $1

β = 150T = 5 years 1,825 1825 $5,248 $6,272 $43T = open 3728 3800 $6,243 $7,621 $4

β = 200T = 5 years 1,825 1825 $5,248 $6,183 $79T = open 3728 3916 $6,243 $7,590 $11

β = 250T = 5 years 1,825 1825 $5,248 $6,099 $121T = open 3728 4024 $6,243 $7,561 $20

Table 1.8: Summary of results

Chapter 2

Increased Bonding Requirements and

Hydraulic Fracturing:

A Case Study of West Virginia

2.1 Motivation:

In recent years, hydraulic fracturing has been increasingly used in the USA to economically

extract natural gas and oil from newly discovered large reserves of shale-bound gas. Growth

in the production of shale gas over the past few years has led to rapid growth in domestic

natural gas supplies and a significant decrease in prices. U.S. shale gas production was only

5% of the total U.S. natural gas production in 2004, 10% in 2007, and in 2015 it was 56%.

As of 2014, recoverable reserve of shale gas is 45% of the 354 Tcf (Trillion Cubic Feet) of

total US natural gas reserve.1 In the future, shale gas production is expected to increase

even more, while production with other extraction methods are predicted to remain steady

or even decline (PPI-Energy and Team 2013). High production level of natural gas combined

with low prices, has a promising future in the domestic energy market and in expanding the

usage of natural gas for both electricity production and as a transportation fuel. Shale plays

that are reshaping the U.S. natural gas industry include the Marcellus, Barnett, Haynesville,

Fayetteville, Eagle Ford, and Bakken shale. These shale plays include 211 Tcf of proven

reserves. If the annual production rate is 19.3 Tcf, there is enough natural gas supply for the

U.S. for the next 90 years with some estimates extending the supply to 116 years (Kargbo,

1Source: U.S. Energy Information Administration, Form EIA-23L, Annual Survey of Domestic Oil andGas Reserves 2014.

37


Wilhelm, and Cambell 2010). Among these reserves the most expansive shale gas play is the

Marcellus Shale which underlies New York, Pennsylvania, Ohio, West Virginia and Mary-

land, spanning a large area of 95,000 square miles (Roy, Adams, and Robinson 2014). Recent

studies suggest that recoverable reserves from Marcellus Shale can be as large as 489 Tcf

(Engelder and Lash 2008).

However, this expansion of natural gas production resulting from both the usage of hori-

zontal drilling2 and new discovery of shale reserves comes at a cost. Tens of thousands of

new oil and gas wells started production using hydraulic fracturing method from shale plays.

This poses a long term threat to the environment and health. In addition, thousands of

wells are abandoned each year, which can possibly leak pollutants, and this number is rising

over time. Even though all conventional wells pose many risks, potential environmental and

health hazards are significantly higher from unconventional wells due to more stages of well

development process, use of fracturing chemicals, and increased pressure on wells caused by

hydraulic fracturing (Dana and Wiseman 2013). Although environmental damages resulting

from hydraulic fracturing are yet to be fully understood, recent studies suggest that impacts

of shale gas production are different from the impacts of conventional drilling method, and

raise a number of environmental and health concerns including impacts on air quality, water

availability, seismicity, and local communities. The U.S. Environmental Protection Agency

(EPA) and other organizations are trying to have a better understanding of the potential

risks that hydraulic fracturing poses to human health and the environment. However, it

will take more time to have a comprehensive impact analysis, because it takes years for the

impacts to be realized. Potentially the most important environmental impact is groundwater

contamination. Fracturing often creates or extends fissures above the target shale forma-

tion. Osborn et al. (2011) and Warner et al. (2012) argue that fracturing fluids can reach

shallow aquifers and degrade groundwater quality through methane contamination. Myers

(2012) concludes hydraulic fracturing fluids can take as long as ten years to transport to

groundwater aquifers. Also, Pennsylvania and West Virginia regulators suggest that shale

gas production implicates new and long-term environmental concerns (NRDC April 2015).

Potential environmental and health hazards are new, significantly more, and long term for

2A horizontal well is defined as “any well site, other than a coal-bed methane well, drilled using a horizontaldrilling method, and which disturbs three acres or more of surface, excluding pipelines, gathering linesand roads, or utilizes more than two hundred ten thousand gallons of water in any thirty day period.”West Virginia Department of Environmental Protection - http://www.dep.wv.gov/oil-and-gas/Horizontal-Permits/Pages/default.aspx


unconventional drilling of shale gas compared to conventional wells. Hence, it requires more

attention than conventional drilling. Even though there are significant concerns stemming

from unconventional drilling of shale gas, regulatory policies have not been adjusted to ad-

dress these new concerns. Unconventional drilling activities have major exemptions from

the federal environmental statutes. These exemptions include an exemption of non-diesel

hydraulic fracturing from the Safe Drinking Water Act (usc Section 300h) and of oil and

gas production and exploration of wastes from the hazardous waste regulation’s part of the

Resource Conservation and Recovery Act (usc 1988). The regulatory practices in shale gas

development are mostly implemented by states. However, states regulate primarily with

older regulations that were written before unconventional drilling was pervasive. Most of

these policy tools are “command and control” policies that establish specific requirements

drilling operators must follow or technologies they must implement. These policies may fail

to prevent contamination in this scenario because the industry has more technical knowledge

than agencies (Dana and Wiseman 2013). There is noticeable heterogeneity among states in

their approaches to regulate the shale gas industry. Some of the major regulations that are

practiced for unconventional drilling are: fracturing fluid information disclosure requirement,

well spacing rules, and underground injection regulations (Richardson et al. 2013). Other

governing tools include ensuring the cemented well casing is safe to prevent brine and frac-

turing fluid escaping underground, and determining the sources from which operators may

withdraw water to be used in the fracturing process (Wiseman 2014).

West Virginia is one of the top five states that have the largest number of shale gas wells

(Richardson et al. 2013). In West Virginia, inspection of unconventional wells is done by

the West Virginia Department of Environmental Protection. As a policy there is no set fre-

quency of inspection in West Virginia. Proper casing and cement plugging must be done to

protect freshwater, and the are inspected for environmental regulations adherence. Although

in some states such as Oklahoma inspectors oversee casing, cementing of well and many other

key aspects of the production process, no such practice exists for West Virginia. In West

Virginia, operators are required to notify the regulatory agency before they begin the casing

and cementing process. However, the law does not require an inspector to be present on the

site. Inspection during the reclamation phase is mandatory before the permit is released. It

is also reported that environmental regulators prefer to offer compliance assistance instead of

enforcement in West Virginia, hence, avoiding issuing violation notices (NRDC April 2015).

The number of new permit issuance for unconventional wells has increased significantly in

recent years in West Virginia, even though the number of regulatory staffs has not increased.

In 2013 a nationwide investigation found that the ratio of wells to inspectors in West Virginia


remains extremely high. In 2011 West Virginia had 20 enforcement staffs for 56,814 wells

(NRDC April 2015). In 2014 there were a total of 3696 unconventional wells out of 63,210

active wells, whereas the number of inspectors statewide to inspect these wells was 24.3 This

situation results in a lower propensity of a violation being detected (Adair et al. 2011).

Several papers have discussed alternative regulatory approaches for alleviating the environ-

mental concerns in the hydraulic fracturing industry. Due to high monitoring costs, direct

regulation such as severance tax can be imperfect as an effective policy tool. In the oil and

gas industry, there are mandatory insurances that producers have to purchase to ensure that

required funds are available to reclaim the site and to pay for clean-ups if situations arise.

However, this does not address the moral hazard problem arising from producers less concern

about the environment which is to some extent insured by the mandatory insurance (Davis

2015). Limitations of command and control regulations necessitate a market-based approach

of bonding requirements. Dana and Wiseman (2013) claim that without adequate bonding

and insurance requirements there will be risk of widespread unaddressed pollution caused

by hydraulic fracturing. There are various kinds of bonding requirements practiced in the

natural gas industry. The most simple example is an Assurance Bond where the producer

posts upfront funds or other financial resources, which the regulator promises to return once

the producer returns the facility, complying with all regulatory requirements. In the case of

noncompliance, the bond is used to pay for claims made against the producer. Davis (2015)

discusses various bonding requirements as important policy tools to mitigate moral hazard

problems in U.S. natural gas production.

The Mineral Leasing Act of 1960 and its subsequent revisions established a federal minimum

bond amount of $10,000 for an individual lease on federal lands. On average there are about

five wells per lease, which implies a minimum bond per well of $2,000. Alternatively, a pro-

ducer can post a $25,000 bond to cover all of the leases in a given state, or $150,000 to cover

all leases in all states. This amount was set in 1960 and has never been adjusted for inflation

(Davis 2015). Many states are currently considering changes in bonding requirements for

horizontal drilling as an important tool to regulate the shale gas industry. State-level re-

quirements extend bonding requirements to drilling on non-federal lands, and in most cases

exceed the minimum federal requirements. Most states have both individual well bonds and

blanket bonds, but the size of the bonds varies widely. Several states have recently increased

their bonding requirements, while several others are actively considering changes. For exam-

3Source: http://www.fractracker.org/2014/03/active-gas-and-oil-wells-in-us/

http://www.fractracker.org/2014/03/active-gas-and-oil-wells-in-us/


ple, the South Dakota legislature in February 2013 increased bonding requirements to $10,000

for wells drilled below 5,500 feet and $50,000 for wells drilled below 5,500 feet. Maryland in

April 2013 moved to increase to $50,000 the minimum bond required per well. New York

State has a maximum bond amount of $250,000 per deep well, the highest maximum bond

for any state. In addition to stringent water-use restrictions, this requirement has effectively

created a moratorium on hydraulic fracturing in New York (Richardson et al. 2013).

In December 2011, West Virginia introduced the “Natural Gas Horizontal Well Control Act”,

which established a $50,000 bond requirement per well increasing from $5,000 per well and

the blanket bond was increased to $250,000 for all of a producer’s wells in the state from a

$50,000 blanket bond (Natural Gas Horizontal Well Control Act West Virginia 2012). In this

chapter we investigate the impact of this significant bonding requirement change in West

Virginia on the environmental damage resulting from hydraulic fracturing. Reporting on

violations provides an important quantitative indicator of how well companies are managing

environmental risks (NRDC April 2015). Therefore, to see the effect of bonding requirement

change on the environmental risks we use violation data as an indication or proxy. We control

for many factors that can affect violation to separate the impact of change in the bonding

requirement. Increasing the bonding requirement is expected to result in less environmental

damage.

One important reason for considering West Virginia is data availability. In the United States,

neither the state nor federal agencies are providing information on violations in a transpar-

ent, easily accessible, or comprehensive way. To date, information about the frequency and

nature of oil and gas company violations is publicly accessible only in three states: Colorado,

Pennsylvania, and West Virginia. In these states where data are available, there are signifi-

cant violations both in terms of number and severity (NRDC April 2015).

This chapter is structured as follows: Section 2.2 discusses the collection, arrangement, and

summary statistics of datasets used in this paper. Section 2.3 describes the econometric

methodologies used for the analysis. Section 2.4 gives an overview of the results and find-

ings. Section 2.5 discusses conclusions.


2.2 Data and Overview:

The dataset used in our analysis is on horizontal wells of West Virginia that is drawn and

combined from several sources. The West Virginia Department of Environmental Protection

(WVDEP) makes oil and gas well information and production data publicly available at no

charge through the internet. Inspection and violation datasets were collected for horizontal

wells from 2007 to 2015 from the West Virginia Department of Environmental Protection

office. They provided separate datasets on inspection and violation both of which have in-

formation on unique Well API (American Petroleum Institute) numbers, name of operators,

and county of operation. Additionally, the inspection dataset has the year of inspection and

permit issue date for that well, whereas the violation dataset provides information on dates

of violation, abatement due date, abate date, status of the well, and violation code. We

merge these two datasets based on well API numbers and years. Looking at the violation

codes we can see different types of violations were reported for horizontal wells, such as, wa-

ter pollution related violations, drilling leakage, pipeline leakage, underground storage tank

leakage etc. West Virginia has a separate dataset for spill, which we could not use because

spills are reported from phone calls, and corresponding well API numbers are not included

in the spill dataset.

The production dataset of horizontal wells in West Virginia from 2005 to 2014 was collected

from West Virginia Geological and Economic Survey’s oil and gas well database of 2016.

These information was reported to the Office of Oil and Gas at the West Virginia Depart-

ment of Environmental Protection (WVDEP) by West Virginia oil and gas operators. Zeroes

in this dataset represent values submitted by operators as zero values, whereas if some op-

erator did not report production it was left blank. This dataset of production also provides

information on many other factors such as well API number, county name, status of the

well, permit issue date, operator’s name, types of well (gas or oil producing), and completion

zone. The well location dataset collected from the West Virginia Department of Environ-

mental Protection (WVDEP) website provides information on location (longitude-latitude

information), permit ID, operator’s name, well status, and county name of all wells in West

Virginia.

Once collected, these datasets of inspection, violation, production, and geospatial informa-

tion are combined together based on unique well API numbers of horizontal wells and years

of incidence. We have only considered wells that were producing natural gas in any year,


and exclude observations with missing production information. In the production dataset,

productions were reported annually in mcf (1,000 cubic feet) from which we calculated the

average daily rate of production of wells. For each well we calculate the number of years

it continued extraction. If a well was producing in a certain year but did not appear in

the inspection dataset, we assume that the well was not inspected. After merging, if a well

was inspected but no violation information is available, we assume there was no violation

and replace the missing violation as zero. We also calculate the number of violations and

inspections for a well in a specific year. Number of violations and inspections per operator

in a year are calculated by summing over all the violations and inspections reported for wells

owned by that firm. We also calculated the running sum of annual violations and inspections

for wells and for operators. Finally, we drop all observations (64 observations) of 2005 and

2006 because we do not have inspection-violation information before 2007.

We then have an unbalanced and short panel dataset on inspection, violation, ownership,

production, and location information on 1,750 horizontal wells in West Virginia from 2007

until 2014 with a sample size of 6,221 observations. Table-2.1 shows descriptive statistics

for our dataset. We see that horizontal wells in West Virginia are owned by 66 operators or

firms. Each year, operator ownership ranges from 1 to a maximum of 350 wells. On average

in a given year an operator owns 132 wells. The average annual number of inspections per

operator is 11 with an average of 4 violations. While most of the wells were not inspected (as

reflected by the mean) the maximum number of annual inspections and violations is 8. We

have 588 inspected observations out of 6,221 observations for West Virginia. Out of these

inspections there are 99 violations, and 64% are single violations per year. The average daily

production of wells ranges from 0 to 8,706 mcf (1,000 cubic feet) with an average of 871

mcf. The total number of violations and inspections over the previous years for a well ranges

from 0 to 11, whereas the total number of inspections and violations for all previous years

for an operator ranges from 0 to 208 and 0 to 65 respectively. The extraction or production

process of a well ranges from 1 to a maximum of 10 years. An average horizontal well in

West Virginia produces natural gas for about 5 years.

Table-2.8 shows within (over time) and between (across individual wells) variations of obser-

vations from our panel dataset. Minimum and maximum for the within variation is achieved

from the individual well’s deviation from its own average (over time) and adding the overall

mean, whereas subtracting the overall mean from the individual mean (over time) gives us

the minimum and maximum for the between-variation. We can see that all variables have


more between variation than within variation. This implies observations of a well across

years have less variation than the variation of observations across different wells in one year.

This is plausible because there is lot more variation from one well to the next compared to

the variation of the same well across years. Here the “number of operating years” for a well

is a time invariant variable, hence it has zero within variation.

Figures 2.2 - 2.4 give an overall picture of horizontal or unconventional wells in West Virginia.

From Figure-2.2 we can see that the number of horizontal wells, number of operators using

hydraulic fracturing, number of incidents, and the number of violations in West Virginia are

increasing over the years. However, Figure-2.3 suggests that the number of inspections per

operator has remained almost the same. Because inspections are captured at the well level

and not at the operator level, we can have a better picture from Figure-2.4, which implies

that over the years the number of inspections per well has been decreasing. This is expected

because we have seen from Figure-2.2 that the number of horizontal wells has been increas-

ing, even though the number of regulatory stsffs remains the same over the years. In 2013 a

nationwide investigation found that the ratio of wells to inspectors in West Virginia remains

extremely high. Figure-2.4 also suggests that the number of violations per well is rising over

the years and the number of violations done by a well is inversely correlated to the number

of years the well operates.

Using spatial information from the dataset, we generate GIS maps of horizontal wells, inspec-

tions, and violations of these wells in West Virginia for 4 years: 2008, 2010, 2012, and 2014.

These are presented in Figures 2.5 - 2.7. Figure-2.5 shows active horizontal wells in West

Virginia. From these GIS maps we can see that the number of horizontal wells has been

increasing over the years. The same trend can be observed for inspections and violations

from Figure-2.6 and Figure-2.7 respectively.

2.3 Empirical Strategy

This chapter focuses on investigating the impact of the significant change in the bonding

requirement introduced in December, 2011 for unconventional wells in West Virginia that

was on the probability of violation done by horizontal well extractors in that state. Model

specifications here involve assumptions regarding the functional form and distribution of the


probability of violation conditional on relevant explanatory variables.

From the combined and arranged dataset we create a set of variables that is presented in

Table-??. The main variable of interest in our analysis is “treat”, which takes a value equal

to one if the year is after 2011 and zero otherwise. This variable represents the change in

bonding requirements. It is reasonable to assume that the probability of violation will be

largely affected by inspection efforts. Different frequency and time of inspection can affect

the probability of violation in different ways. Therefore, in our analysis we control for “la-

gins” (if the well was inspected in the previous year), “lagnins” (number of inspections done

for a well in the previous year), and “lagtotnins” (total number of inspections conducted

for the well until the previous year). The probability of violation in a certain year can also

be affected if in the previous year the well was reported for violation and by the number of

violations reported in that year. Because the total number of violations done by the well

until the previous year can be an indication if the well is more prone to violation or not, we

include “lagtotnvio” as an explanatory variable.

In addition to these, well-owner attributes can influence an individual well’s likelihood of

violation. An operator that has a large number of wells may have a different level of concern

for violation compared to an operator having only a few number of wells. Therefore, we con-

trol for the number of wells owned by an operator in that year with the variable “numwells”.

To see if an operator’s history of violation has any influence on specific well’s probability of

violation we include “lagtotnvio op”, that is, the total number of violations committed by

an operator until the previous year, as an explanatory variable. We can assume more pro-

duction by a well can have more impact on the environment, hence causes higher likelihood

of violation. Therefore, we control for the average daily “production” of the well. Also, the

number of years a well has been producing can affect its probability of violation, hence we

include “num of yrs produced” as a regressor.

The dependent variable in our analysis is “violation”, which indicates if a well was reported

for violation in a year. If i ∈ {1, 2, ..., N} is the i’th well and t ∈ {1, 2, ..., T} is the t’th

year, then yit shows if the well i was reported for violation in the year t, and xit is the

set of values of predictors for the well i in year t. Hence, the dataset can be written as

{yit, xit}i∈{1,2,..,N}, t∈{1,2,..,T}. We estimate the probability of violation of a well i at time

t, yit as a function of a set of explanatory variables xit from the sub-sample of inspected

observations. The set of values xit includes values of all variables described in Table-?? other


than “violation”. Following classic binary outcome models assumptions, we assume the latent

variable is y∗it such that

y∗it = x′itβ + uit (2.1)

where β is the vector of coefficients associated with xit. The stochastic error term uit of this

latent variable y∗it is not observable. Even though this hypothetical continuous variable y∗it is

not observable, we assume it determines if there is violation reported or not by the following

criteria,

y =

{1 if y∗it > 0

0 if y∗it ≤ 0

We also assume the error term in equation-2.1 follows a normal distribution that is uit ∼N (0, σu

2it). Therefore, the probability of violation conditional on the set of explanatory

variables xit is as follows:

prob [viol = 1|xit] = F (x′itβ) =

∫ ∞−∞

ϕ (x′itβ) dx′itβ ∈ (0, 1) (2.2)

where β is the set of parameters and ϕ (x′itβ) is the normal probability distribution function

of (x′itβ). That is,

ϕ (x′itβ) =1√2πe−(x′itβ)2/

2 (2.3)

The Probit model in this scenario is estimated through the maximum likelihood estimation

(MLE) method which maximizes equation-2.4

lnL (β) =T∑t=1

N∑i=1

(yit lnF (x′itβ) + (1− yit) ln (1− F (x′itβ))) (2.4)

We have a panel dataset of 1,750 horizontal wells, and from table-2.8 we see that for all of the

variables there are more between variation (across wells) compared to the within variation


(over time). This implies that there is a lot more variation from one well to the next com-

pared to the variation of the same well across years. This can be a result of individual well

specific heterogeneity. Therefore, after running basic Probit models we use Random Effect

Probit models to address unobserved heterogeneity of wells. In this case we assume that the

unobserved heterogeneity of a well that affects the probability of violation is not correlated

with any of the predictors. If the stochastic error term in equation-2.1 is uit = λit+αi, where

λit is a random error and αi is the individual specific unobservable effect of the well i, then

this assumption says corr(xit, αi = 0). In the case of Probit random effects (RE) it is assumed

that λit ∼ N (0, σ2λ). In order to marginalize the likelihood, we assume that conditional of

xit, αi ∼ N (0, σ2α). These imply that the correlation between two successive error terms for

the same well is a constant given by corr(uit, uit−1) = σ2α/(σ2

α + σ2λ)

. A simple Probit model

does not allow us to estimate the scale parameter because of the binary nature of the data.

We can only obtain parameter estimates of β/σu. Hence, pooled Probit models parameter

estimates will be equal to RE Probit model parameter estimates only when σ2α = 0, that is

if there is no variation for individual specific unobservable effects of the wells, which means

two successive error terms for the same well are not correlated. This is because in that case

σ2u = σ2

λ. When σ2α 6= 0, the consistent Probit estimates will not look similar to the RE

Probit estimates because of the normalization, and because Probit model does not take the

individual specific unobservable effect of the well i, (αi) into consideration (Arulampalam

1999).

In our sample we get the violation data only for wells that were inspected. Because violation

is not observed if an observation is not in the sample of inspected wells, we may have sample

selection bias in our analysis. This can lead to inconsistent estimation in the Probit and RE

Probit models. Probit and Random Effect Probit models would be sufficient if the missing

violation data were missing completely at random. Because we do not have violation for

those that were not inspected, we have a nonrandom sample. Using zero for violation of

all uninspected wells will likely result in an under-estimation of the probability of violation.

Therefore, we use the Heckman Selection method to correct the bias in sample selections

resulting from individual wells being selected in the group of inspected wells. Different factors

might affect the inspection of a well. In our analysis, the outcome equation (violation) of

the Heckman model has the same set of predictors xit as the Probit and RE Probit model.

However, we add “lagtotnins op” (the total number of inspections done for an operator up to

the preceding year) to the explanatory variable set xit for the selection equation (inspection).

If zit = {xit, lagtotnins op}, then the Heckman Probit model in this case estimates two

log-likelihood functions that use the following two models:


outcome model : prob (violation = 1|xit) = F (x′itβ)

selection model : prob(inspection = 1|zit) = h (z′itµ)(2.5)

where zit is the set of variables used in the selection model of inspection and µ is the cor-

responding set of parameters. If εit is the error term for the latent variable in the selection

model, we assume εit is normally distributed; that is εit ∼ N(0, σ2εit

). Therefore, the selection

model in this case is also a Probit model. When correlation between uit and εit is zero, that

is, corr(uit,εit)=ρ = 0, the Probit and Random Effect Probit regression can provide unbiased

estimation and estimation is biased if otherwise.

Results from Wald statistics in the Heckman Selection model suggest that this method is

appropriate for our analysis, and doing Probit and RE Probit will be inconsistent because

of the positive significant correlation between the error terms of inspection and violation.

Therefore, we also do check potential endogeneity problem by regressing residuals from Pro-

bit and RE Probit on the regressors. In addition, in our dataset, wells that were not inspected

have zero violation; hence the dependent variable “violation” is censored from below at zero.

Therefore, this censored dataset represents the population but does not properly represent

the sample. To address this censoring aspect of the dependent variable, we run a Tobit model,

RE Tobit, and FE Tobit models4. However, for the Tobit model dependent variable yit we

use the “number of violations” instead of “violation”. yit is censored from below at zero, that

is, yit = max(0, y∗it) where y∗it = x′itβ+eit is the latent variable. We assume eit|xit ∼ N(0, σ2),

that is, the error component of the latent variable yit is normally distributed with zero mean

and σ2 variance. The Tobit model uses maximum likelihood method to estimate both β and

σ. The set of explanatory variables xit is a set of all the variables in Table-?? excluding

“vioaltion” and “lagnvio”.

Datasets used in this chapter comprised of observations from horizontal wells across time.

We can expect that there will be various types of cluster effects resulting from unobserved

heterogeneity existing in the dataset. For example, each well’s observations can be correlated

across time, observations from wells having the same ownership can be correlated across time,

and observations from wells having the same ownership can be correlated in a specific year.

4Honore (1992) has developed a semi-parametric estimator for Eixed-Effect Tobit models. We have usedpantob.ado file available at: http://www.princeton.edu/~honore/stata/index.html

http://www.princeton.edu/~honore/stata/index.html


In order to take these cluster effects into consideration, we run each above mentioned model

for three types standard error (or random effect) that are clustered at the well level, operator

level, and operator-year level.

Another possibility that we try to address in our analysis is the endogeneity problem resulting

from the individual specific heterogeneity. Most of the regressors included in our analysis are

decisions of the extractor that can be correlated with the error term. For example, wells are

not operating at the same location. Wells that are in a location with groundwater aquifers or

near the town may have higher probability of violation. Also, frequency of inspection for such

wells might be higher compared to wells that are in remote places or do not have any nearby

groundwater aquifers. In this scenario, due to the endogeneity problem, the Probit and RE

Probit models will lead to inconsistent estimation of the parameters in β because the basic

assumption corr(xit, αi) = 0 that is required for consistency will be violated. Therefore, we

test potential endogeneity problem by regressing residuals from Probit and RE Probit on the

regressors. One way to address the endogeneity arising from individual specific heterogeneity

is to run Fixed Effect models. However, the Fixed Effect model is not available for Probit

models. In practice, even though Logit and Probit models assume different link functions,

they give similar results. Hence, we use the FE Logit model to remove the effect of individual

specific heterogeneity that are time invariant. The FE Logit model uses only the within

variation of observations. In this case, we estimate equation-2.4 assuming the stochastic

error term in equation-2.1 follows a logistic distribution. Hence, the probability of violation

conditional on the set of explanatory variables xit is the following,

prob [viol = 1|xit + αi] = F (x′itβ + αi) =1

1 + e−(x′itβ+αi)

(2.6)

All models other than the Tobit model used in this analysis have the same set of explanatory

variables x that includes all the variables in Table-?? excluding “violation”. Each model is

run for two specifications. In one specification, we use all years (2007-2014) except 2012. In

another specification, assuming that inspection effort and other confounding factors do not

change much in a narrow time frame, we use only 2010, 2011, 2013, and 2014 observations.

In both cases, we exclude 2012, considering this year was the adjustment period.


2.4 Results:

Table-?? shows the results of Probit and Random Effect Probit models under the specifi-

cation of “All years except 2012” and Table-?? shows the results under the specification of

“4-Years only”. In this analysis, both the Probit and Random Effect Probit models we run

are for the subset of inspected wells. The Probit model uses maximum likelihood techniques

to compute estimates of the coefficients (elements in β) in equation-2.4 and their correspond-

ing standard errors that are asymptotically efficient. However, these estimates cannot be

interpreted in the same manner that the normal regression coefficients are. These coeffi-

cients give the impact of the explanatory variables on the latent variable y∗ (in 2.1), not

y itself. Table-?? and Table-?? respectively present estimated Probit coefficients for each

variable under two specifications “All years except 2012” and “4-Years only”. For both “all

years except 2012” and “4-years only” Probit model results show that the effect of “treat”

variable is always statistically insignificant. For the “all years except 2012” specification two

variables that are significant are “numwells” (number of wells per operator per year) and

“lagtotnvio op” (number of all previous violations for that well’s operator up to the preced-

ing year). However, the marginal effect is zero for both of these two variables in all the cases

of cluster effects. Under the “4-years only” specification explanatory variables “numwells”,

“lagins” (=1 if well was inspected in the previous year), “lagvio” (=1 if violation(s) found

in the previous year), “lagtotnvio” (total number of violations found for the well up to the

preceding year), “lagnins” (number of inspections done for the well in the previous year), and

“lagtotnvio op” are statistically significant. Variables “numwells” and “lagtotnvio op” again

have zero marginal impact on the probability of violation. Variables “lagins” and “lagtot-

nvio” have significant negative impact on the probability of violation. Calculating marginal

effects, we see that if a well was inspected in the previous year, it has 83% less probability of

violation and if the sum of all previous violations for that well up to preceding year increases

by one unit, it has 69% less probability of violation. A well that was reported for violation in

the previous year has 96% more probability of violation compared to the well that was not

reported for violation. If the number of inspection for a well increases by one unit, it might

assume that there will be less number of inspections the following year, hence the probability

of violation increases by 66%.

For the Random Effect Probit models, no matter how we specify the models (“all years except

2012” or “4-years only”) and no matter how we address the cluster effect of each model, the

effect of the “treat” variable is always statistically insignificant. Coefficient estimates results

from Table-?? and Table-?? show mixed effects of variables “numwells” (number of wells per


operator per year), “lagins” (=1 if well was inspected in the previous year), “lagvio” (=1 if

violation(s) found in the previous year), and “num of yrs produced” (number of years the

well extracted natural gas). Other variables are not statistically significant under any of the

two specifications.

Most regressors included in our analysis are decision variables of the producer. Therefore,

these variables can be influenced by many unobserved factors that can also affect the prob-

ability of violation. We have discussed some probable reasons in the previous section that

can give rise to several endogeneity problems. If there is individual specific heterogeneity

(FE) of wells which affects its probability of violation, Probit and RE probit model will lead

to inconsistent estimation of parameters. Therefore, we test for consistency and find that

residuals in the Probit and RE probit are strongly correlated with regressors which results

in endogeneity problem in these two models. First, we try to address the potential endo-

geneity problem arising from individual time-invariant effects in our analysis. One way to

address this problem is by running a Fixed Effect model which removes individual specific

heteorgenity that is time invariant. However, the Fixed Effect model is not available for

the Probit model. Assuming Logit and Probit results do not differ much in practice, we

run the FE Logit model to address the endogeneity problem. Results from Table-?? and

Table-?? show no statistically significant impact of “treat” on the probability of violation.

Under both specifications, “numwells” and “lagins” reduce probability of violation, whereas

“lagvio” increases this probability. That is, if an operator has more wells, or if the well was

inspected in the previous year, the probability of violation decreases. However, if the well

was reported for violation in the preceding year the probability of violation will increase.

Now to test which model we should use between RE and FE, we run the Hausman test. Null

hypotheses in the Hausman test assumes that there is no correlation between regressors (xit)

and individual specific effects (αi), and FE and RE both are consistent, but FE is inefficient.

The Hausman test suggests that there is no endogeneity problem in this case that arise from

individual specific time-invariant effects. Therefore, we prefer RE Probit over the FE Logit

model for our conclusion.

As mentioned earlier, in our dataset, because a violation is not observed if an observation

does not appear in the sample of inspected wells, we may have sample selection bias in our

analysis. We use the Heckman selection model to address the sample selection bias in our

dataset. In this analysis, the inspection equation is the selection equation and the violation

equation is the outcome equation. In addition to estimating the sets parameters β and µ in


equation-2.5, the Heckman selection model estimates the correlation of the residuals in these

two equations, that is, it estimates corr(uit,εit)=ρ. If the Wald test indicates ρ is significant,

we should use Heckman’s technique. The results for the Heckman model from Table-?? sug-

gest that for both of the specifications “all years except 2012” and “4-years only”, the variable

“treat” significantly reduces the probability of violation. Calculating the marginal effect of

“treat” we see that after the bonding requirement change (treat=1) wells had 3.2% and 2.6%

less probability of violation in these two specifications respectively. Other explanatory vari-

ables are not significant in the outcome equation of the Heckman model under “all years

except 2012” specification. Table-?? also shows that under the “4-years only” specification,

explanatory variables “lagins” and “lagtotnviol” reduce the probability of violation by 19%

and 18% respectively, whereas “lagvio” and “lagnins” significantly increases the probability

of violation by 23%. and 19% respectively.

For the selection equation, that is, for the inspection equation in all of the six Heckman

models, “lagins” (1-year lagged inspection) has positive effect on the probability of a well

being inspected. This implies that once a well is inspected, the probability of that well being

inspected in the following year increases. “lagvio” (1-year lagged violation) has negative ef-

fect on the probability of inspection. One plausible reason behind this can be that inspectors

assume lasting impact of a fine when a well is reported for violation. One important finding to

note is that the probability of a well being inspected decreases after the bonding requirement

change in 2012. “numwells”, “production”, and “num of yrs produced” all decrease the like-

lihood of a well being inspected, while “lagnins” and “lagtotnvio op” increase the inspection

likelihood. In addition to these, the Wald test indicates that estimated correlation of the

residuals of the two equations is significant in all of these Heckman Probit models. That is,

we reject the null hypotheses that corr(u,ε)=ρ = 0. Since Wald test results are significant,

the Heckman model is the appropriate model here, we shall follow this model’s result for our

conclusion.

In addition, to address the censoring aspect of the dependent variable we run Tobit, RE Tobit

and FE Tobit models. Results of Tobit models for two specifications “all years except 2012”

and “4-years only” and for three types standard error that are clustered at the well level,

operator level, and operator-year level are presented in the Table-??. Estimated coefficients

are to be interpreted as the effect of regressors on the latent variable. Therefore, for each

of the explanatory variables we calculate the marginal effect for the censored sample that is

shown in (dy/dx) columns. We see for all cluster effects and both specifications in the To-


bit model, the “treat” variable is statistically significant. Calculating the marginal effect of

“treat”, we can say that the increase in the bonding requirement in December, 2011 slightly

decreased the number of violations under both specifications (-0.02 and -0.006). Therefore,

we can say that this policy change had little to no effect on reducing violations in horizontal

wells. Another explanatory variable that is statistically significant in both specifications is

“lagtotnvio op”. However, it has zero impact on the number of violations. Variables affecting

“violation”, such as inspection efforts and other confounding factors do not change much in

a narrow time frame compared to the other specification “all years except 2012”. Also, the

number of horizontal wells and the number of violations reported for those wells in West

Virginia before 2010 was very low. Hence, it can be argued that the “4-years only” speci-

fication is more appropriate to get a better picture of the whole scenario. Results for this

specification from Table-?? show that “lagins” and “lagtotnvio” have a negative impact on

the number of violation, whereas “lagvio” and “lagnins” have a slight positive effect on the

violation number. This implies that if a well is inspected one year, the well-owner probably

assumes it will be inspected again in the following year, therefore engage in behavior that

reduces the probability of violation. Once violation is reported, they assume there will be

less possibility of inspection in the following year (we can see this from the selection that is

the inspection equation in the Heckman model). Another reason is that it can be hard to fix

problems that result in violations. Both RE Tobit and FE Tobit model results in Table-??

also suggest that the “treat” variable is statistically significant and causes small reduction

in the number of violations. In summary, addressing the censoring aspect of the dependent

variable by using Tobit, RE Tobit, and FE Tobit models we conclude that changing bond

requirements in West Virginia has a slight statistically significant negative effect on the num-

ber of violations reported for horizontal wells.

2.5 Conclusion

Regulatory policies in the shale gas development are mostly implemented by states. State

regulations are primarily comprised of older command and control policies that were intro-

duced before the widespread practice of horizontal drilling and these policies are inadequate

to address new, significantly more, and long term environmental impacts of unconventional

drilling. Several states are considering an increase in the bonding requirements for unconven-

tional wells as an alternative regulatory policy. Few states such as South Dakota, Maryland,

and New York have recently increased their bonding requirements. In December 2011 West


Virginia, one of the top five states that have the largest number of shale gas wells, introduced

the “Natural Gas Horizontal Well Act”. In this act, bonding requirements for unconventional

or horizontal wells have substantially increased. The objective of this chapter is to explore

if this significant increase in the bonding requirement has any impact on the environmental

damage emanating from horizontal drilling. Reporting on violations provides an important

quantitative indicator of how well companies are managing environmental risks (NRDC April

2015). Therefore, to see the effect of bonding requirement change on the environmental risks

we use violation data as an indication.

From our analysis we conclude that the increase in the bonding requirements for unconven-

tional wells in West Virginia has a small statistically significant impact on the probability of

violation. Our results suggest that this increase in the bonding requirement for horizontal

wells in West Virginia in 2011 has reduced the probability of violation by 2.6 to 3.2 per-

centage points. Assuming inspection efforts and other confounding factors do not change

much in a narrow time-frame, we further conclude from the Heckman model results that if

a well was inspected in the previous year the probability of violation for that well will be

19.4% lower compared to the well that was not inspected. This is reasonable because the

Heckman model suggests that once a well is inspected, it has higher probability of being

inspected again in the following year. Whereas, the number of inspections in the previous

year positively affects the probability of violation, because well operators assume that as the

well has been inspected more there is less possibility that it will be inspected again. These

findings are supported by the selection equation. If a well was reported for violation in the

previous year, it will have higher probability of violation compared to a well that was not

reported for violation. One plausible reason behind this can be seen in the Heckman model

results. Once a well is reported for violation, inspectors assume lasting impact of that re-

port or a fine. Therefore, such wells will have less likelihood of being inspected again in the

following year. Another reason is that it can be hard to fix problems causing repetition of

the violation. Both of these answer why a well that is reported for violation will have higher

likelihood of being reported for violation again in the following year. On the other hand, the

Tobit, RE Tobit, and FE Tobit model results suggest that the “treat” variable is statistically

significant. That is, the increase in the bonding requirements in West Virginia in December

2011 slightly decreased the number of violations reported for the horizontal wells. Assuming

a narrower time-frame (“4-years only” specification) when factors affecting violation do not

change much, Tobit model results suggest “lagins” and “lagtotnvio” have negative impacts

on the number of violations, whereas “lagvio” and “lagnins” have slight positive effects on

the reported violation number.


Even though findings of this chapter is limited by the small sample size of our dataset, we can

conclude that the increase in bonding requirements for horizontal wells in West Virginia has

small negative effect in reducing environmental risks associated with hydraulic fracturing.

There can be several reasons behind this small effect. Bonding requirement as a policy is

efficient and most effective when there are a small number of operators, the time horizon

is well defined, non-compliance is well defined, and there is a high probability of detection

(Gerard and Wilson 2009). Most of these conditions are not satisfied for the case of horizontal

wells. In the United states hydraulic fracturing is conducted by a large number of companies

most of which are small and medium sized. Therefore, the hydraulic fracturing market is

relatively less concentrated (Davis 2015). Another reason behind our result can be very low

likelihood of a violation being detected. In 2013 a nationwide investigation found that the

ratio of wells to inspectors in West Virginia remains extremely high. In 2011 West Virginia

had 20 enforcement stuffs for 56,814 wells (NRDC April 2015). In 2014 there were a total

of 3,696 unconventional wells out of 63,210 active wells, whereas the number of inspectors

statewide to inspect these wells was only 24. This situation results in less likelihood of a

violation being detected (Adair et al. 2011). In addition to this, groundwater contamination,

potentially the most important environmental impact of hydraulic fracturing, is less visible

and takes longer to detect. Myers (2012) concluded hydraulic fracturing fluids can take as

long as ten years to transport to groundwater aquifers. However, on average a horizontal

well in West Virginia operates for 5 years (see Table-2.1). These make it more difficult

to address new environmental and health concerns of hydraulic fracturing with increased

bonding requirements. Nevertheless, these increased bonding requirements are well-suited

for ensuring site remediation by providing a source of funds. This can encourage producers

to internalize the future cost of site reclamation, for example they can choose well locations

where the post-production reclamation costs tend to be smaller. Determining the optimal

bond amount is difficult for the case of horizontal wells because it requires information on

the entire distribution of potential external damages of hydraulic fracturing which is yet be

understood extensively.


2.6 Appendix: B

Tables, figures and maps:

Variables Observations Mean Std. Dev Min Max

Id of Operator 6221 23.639 16.300 1 66

Year 6221 2012.077 1.804 2007 2014

Number of wells per operator per year 6221 131.625 98.576 1 350

Annual number of inspections per op 6221 11.277 22.318 0 84

Annual number of violations per op 6221 3.824 10.937 0 46

Annual number of inspections per well 6221 0.107 0.380 0 8

Annual number of violation per well 6221 0.028 0.276 0 8

Average daily production 6221 870.798 1158.602 0.00548 8706.463

Running sum of annual violations per well 6221 0.084 0.466 0 11

Running sum of annual inspections per well 6221 0.250 0.684 0 11

Running sum of annual violations per operator 6221 7.666 12.450 0 65

Running sum of annual inspections per operator 6221 20.381 36.250 0 208

Number of gas producing years for each well 6221 4.850 2.165 1 10

Table 2.1: Descriptive Statistics


0200400600800

10001200140016001800

Num

ber

of h

oriz

onta

l wel

ls

2007

2008

2009

2010

2011

2012

2013

2014

Year

10

20

30

40

50

60

70

Num

ber

of o

pera

tors

2007

2008

2009

2010

2011

2012

2013

2014

Year

−50

0

50

100

150

200

250

300

350

Num

ber

of in

cide

nts

2007

2008

2009

2010

2011

2012

2013

2014

Year

−100

102030405060708090

Num

ber

of v

iola

tions

2007

2008

2009

2010

2011

2012

2013

2014

Year

Horizontal wells in West Virginia

Figure 2.2: Horizontal wells in West Virginia

0

50

100

150

200

250

300

350

Num

ber

of w

ells

per

ope

rato

r

2007

2008

2009

2010

2011

2012

2013

2014

Year

0

20

40

60

80

100

120

Num

ber

of in

cide

nts

per

oper

ator

2007

2008

2009

2010

2011

2012

2013

2014

Year

0

10

20

30

40

50

Num

ber

of v

iola

tions

per

ope

rato

r

2007

2008

2009

2010

2011

2012

2013

2014

Year

Number of wells, inspections and violations per operator

Figure 2.3: Number of wells, inspections and violations per operator


1

2

3

4

5

6

7

8

Num

ber

of in

cide

nts

per

wel

l

2007

2008

2009

2010

2011

2012

2013

2014

Year

1

2

3

4

5

6

7

8

Num

ber

of v

iola

tions

per

wel

l

2007

2008

2009

2010

2011

2012

2013

2014

Year

0

1

2

3

4

5

6

7

8

Num

ber

of v

iola

tions

by

wel

l

0 2 4 6 8Number of years well operated

Inspections and violations per well

Figure 2.4: Inspections and violations per well


(a) Horizontal Wells in 2008 (b) Horizontal Wells in 2010

(c) Horizontal Wells in 2012 (d) Horizontal Wells in 2014

Figure 2.5: Active Horizontal wells in West Virginia


(a) Inspected wells in 2008 (b) Inspected wells in 2010

(c) Inspected wells in 2012 (d) Inspected wells in 2014

Figure 2.6: Inspected wells in West Virginia


(a) Violating wells in 2008 (b) Violating wells in 2010

(c) Violating wells in 2012 (d) Violating wells in 2014

Figure 2.7: Violating wells in West Virginia


Number of wells per operator overall 131.63 98.58 1.00 350.00 N = 6221

between

101.76 1.00 350.00 n = 1750

within

43.44 -40.88 354.96 T-bar = 3.55486

Number of inspections per op overall 11.28 22.32 0.00 84.00 N = 6221

between

25.25 0.00 84.00 n = 1750

within

11.59 -43.39 65.61 T-bar = 3.55486

Number of violations per op overall 3.82 10.94 0.00 46.00 N = 6221

between

13.52 0.00 46.00 n = 1750

within

6.23 -14.18 38.99 T-bar = 3.55486

Number of inspections per well overall 0.11 0.38 0.00 8.00 N = 6221

between

0.34 0.00 5.50 n = 1750

within

0.26 -2.39 4.77 T-bar = 3.55486

Number of violation per well overall 0.03 0.28 0.00 8.00 N = 6221

between

0.22 0.00 5.50 n = 1750

within

0.22 -2.47 4.69 T-bar = 3.55486

Average daily production overall 870.80 1158.60 0.01 8706.46 N = 6221

between

1313.82 0.70 7452.81 n = 1750

within

449.90 -2612.98 4354.58 T-bar = 3.55486

Total number of violations per well overall 0.08 0.47 0.00 11.00 N = 6221

between

0.44 0.00 9.50 n = 1750

within

0.17 -3.12 4.75 T-bar = 3.55486

Total number of inspections per well overall 0.25 0.68 0.00 11.00 N = 6221

between

0.64 0.00 9.50 n = 1750

within

0.30 -2.95 4.92 T-bar = 3.55486

Total number of violations per operator overall 7.67 12.45 0.00 65.00 N = 6221

between

13.92 0.00 65.00 n = 1750

within

6.04 -19.83 54.67 T-bar = 3.55486

Total number of inspections per operator overall 20.38 36.25 0.00 208.00 N = 6221

between

38.54 0.00 182.00 n = 1750

within

18.86 -76.12 154.38 T-bar = 3.55486

Number of operating years overall 4.85 2.17 1.00 10.00 N = 6221

between

2.16 1.00 10.00 n = 1750

within

0.00 4.85 4.85 T-bar = 3.55486

n=1750, T=8

Variable Mean Std. Dev. Min Max Observations

Table 2.8: Variation Statistics

Syed

Mortu

zaAsif

Ehsan

Chap

ter2.

63

Table 2.9: Specification- All years except 2012

Violation Probability Probit Random Effect Probit FE Logit Variables Op Op-Year Well Op Op-Year Well

treat -0.519 -0.519 -0.519 0.768 -0.972 -1.177 4.040

(-1.25) (-0.90) (-1.91) -0.96 (-0.82) (-1.17) (1.65)

numwells 0.00701*** 0.00701*** 0.00701*** -0.0253* 0.00411 0.0166 -0.104*

-5.89 -3.77 -3.93 (-2.02) -0.6 -1.68 (-2.47)

production 0.000312 0.000312 0.000312 -0.000135 -0.0000264 0.000714 -0.000468

-1.13 -1.06 -1.38 (-0.49) (-0.09) -1.32 (-0.91)

lagprod 0.00032 0.00032 0.00032 0.000337 0.000275 0.000791 0.000433

-1.34 -1.31 -1.63 -1.22 -0.96 -1.26 (0.80)

lagins -1.635 -1.635 -1.635 -3.305* -4.029* -4.602 -5.400*

(-1.11) (-1.46) (-1.70) (-2.49) (-2.31) (-1.29) (-2.30)

lagvio 2.535 2.535* 2.535* 4.083** 4.943* 6.596 6.543*

-1.82 -2.12 -2.35 -2.79 -2.53 -1.47 (2.40)

lagtotnins -0.101 -0.101 -0.101 0.372 0.269 0.274 0.980

(-0.18) (-0.20) (-0.21) -0.55 -0.39 -0.24 (0.70)

lagtotnvio -0.716 -0.716 -0.716 -2.012 -2.529 -2.447 -3.489

(-0.61) (-0.79) (-0.90) (-1.91) (-1.68) (-1.02) (-1.79)

lagnins 0.71 0.71 0.71 1.519 2.17 1.886 2.337

-0.95 -0.99 -1.05 -1.63 -1.54 -0.96 (1.42)

Syed

Mortu

zaAsif

Ehsan

Chap

ter2.

64

lagnvio 0 0 0 0 0 0 0

(.) (.) (.) (.) (.) (.) (.)

num_of_yrs_produced 0.125 0.125 0.125 0.369* 0.291 0.304 0.368

-0.98 -0.81 -1.29 -2.01 -1.29 -1.05 (0.88)

prod_dummy 0 0 0 0 0 0 0

(.) (.) (.) (.) (.) (.) (.)

lagtotnvio_op -0.0458* -0.0458** -0.0458*** 0.00137 0.00105 -0.109 0.0374

(-2.31) (-2.60) (-3.63) -0.04 -0.03 (-1.65) (0.51)

_cons -1.531 -1.531 -1.531* -2.938* -2.277 -3.678

(-1.72) (-1.51) (-2.52) (-2.14) (-1.49) (-1.47)

lnsig2u

_cons

2.738*** 1.671** 1.532

-3.37 -2.59 -1.16

N 304 304 304 304 304 304 243

t statistics in parentheses

="* p<0.05 ** p<0.01 *** p<0.001"

Syed

Mortu

zaAsif

Ehsan

Chap

ter2.

65

Table 2.10: Specification- 4 Years (2010, 2011, 2013, and 2014)

Violation Probability Probit Random Effect Probit FE Logit Variables Op Op-Year Well Op Op-Year

treat -0.477 -0.477 -0.477 -0.42 -0.168 3.118

(-1.04) (-0.89) (-1.63) (-0.69) (-0.18) -1.31

numwells 0.00615*** 0.00615** 0.00615** -0.00167 0.00324 -0.0931*

-4.89 -2.77 -3.23 (-0.25) -0.58 (-2.27)

production 0.000312 0.000312 0.000312 0.0000465 -0.00000925 -0.000506

-1.12 -1.01 -1.33 -0.19 (-0.03) (-0.98)

lagprod 0.000328 0.000328 0.000328 0.000324 0.000333 0.000482

-1.43 -1.4 -1.59 -1.25 -1.18 -0.89

lagins -6.340*** -6.340*** -6.340*** -11.14 -10.22 -27.36***

(-4.49) (-5.47) (-6.70) (-0.01) (-0.01) (-13.82)

lagvio 7.344*** 7.344*** 7.344*** 12.2 11.31 28.28***

-5.33 -5.52 -6.32 -0.01 -0.02 -10.15

lagtotnins 0.0821 0.0821 0.0821 0.279 0.293 1.289

-0.16 -0.16 -0.17 -0.43 -0.45 -0.88

lagtotnvio -5.264*** -5.264*** -5.264*** -9.884 -8.754 -25.09***

(-4.70) (-5.64) (-7.01) (-0.01) (-0.01) (-12.20)

lagnins 5.095*** 5.095*** 5.095*** 9.506 8.39 23.93

-7.26 -6.55 -8.26 -0.01 -0.01 (.)

Syed

Mortu

zaAsif

Ehsan

Chap

ter2.

66

lagnvio 0 0 0 0 0 0

(.) (.) (.) (.) (.) (.)

num_of_yrs_produced -0.0256 -0.0256 -0.0256 0.257 0.145 0.199

(-0.17) (-0.17) (-0.26) -1.46 -0.67 -0.47

prod_dummy 0 0 0 0 0 0

(.) (.) (.) (.) (.) (.)

lagtotnvio_op -0.0326* -0.0326 -0.0326* -0.00601 0.00238 0.0194

(-2.10) (-1.83) (-2.46) (-0.20) -0.08 -0.28

_cons -1.149 -1.149 -1.149 -2.095 -2.256

(-1.29) (-1.16) (-1.89) (-1.81) (-1.55)

lnsig2u

_cons

1.039 1.097

-1.39 -1.57

N 295 295 295 295 295 230


="* p<0.05 ** p<0.01 *** p<0.001"

Syed

Mortu

zaAsif

Ehsan

Chap

ter2.

67

Table 2.11: Heckman Probit Model Results

Violation Probability All years except 2012 4-years only

Variables Op dy/dx Op-Year dy/dx Well dy/dx Op dy/dx Op-Year dy/dx Well dy/dx

treat -0.701** -0.032 -0.701** -0.032 -0.701*** -0.032 -0.656* -0.026 -0.656* -0.026 -0.656** -0.026

(-2.94)

(-2.58)

(-3.88)

(-2.20)

(-2.24)

(-3.09)

numwells -0.00120 0.000 -0.00120 0.000 -0.00120 0.000 -0.00150 0.000 -0.00150 0.000 -0.00150 0.000

(-0.54)

(-0.48)

(-0.49)

(-0.62)

(-0.60)

(-0.72)

production 0.0000701 3.23E-06 0.0000701 3.2E-06 0.0000701 3.23E-06 0.0000863 3.E-06 0.0000863 3.4E-06 0.0000863 3.46E-06

(0.22)

(0.22)

(0.30)

(0.26)

(0.26)

(0.35)

lagprod 0.000253 0.000 0.000253 0.000 0.000253 0.000 0.000264 0.000 0.000264 0.000 0.000264 1.1E-05

(1.30)

(1.24)

(1.40)

(1.24)

(1.20)

(1.30)

lagins -0.336 -0.015 -0.336 -0.015 -0.336 -0.015 -4.837*** -0.194 -4.837*** -0.194 -4.837*** -1.9E-01

(-0.26)

(-0.36)

(-0.44)

(-3.36)

(-4.60)

(-6.06)

lagvio 1.200 0.055 1.200 0.055 1.200 0.055 5.776*** 0.232 5.776*** 0.232 5.776*** 2.3E-01

(0.94)

(1.25)

(1.45)

(4.13)

(5.26)

(6.28)

lagtotnins -0.214 -0.010 -0.214 -0.010 -0.214 -0.010 -0.0457 -0.002 -0.0457 -0.002 -0.0457 -1.8E-03

(-0.43)

(-0.49)

(-0.53)

(-0.10)

(-0.10)

(-0.11)

lagtotnvio -0.316 -0.015 -0.316 -0.015 -0.316 -0.015 -4.580*** -0.184 -4.580*** -0.184 -4.580*** -1.8E-01

(-0.33)

(-0.45)

(-0.53)

(-4.10)

(-5.43)

(-7.05)

Syed

Mortu

zaAsif

Ehsan

Chap

ter2.

68

lagnins 0.702 0.032 0.702 0.032 0.702 0.032 4.817*** 0.193 4.817*** 0.193 4.817*** 1.9E-01

(1.29)

(1.39)

(1.42)

(6.52)

(6.72)

(8.39)

lagnvio 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.0E+00

(.)

(.)

(.)

(.)

(.)

(.)

num_of_yrs_produced 0.0114 0.001 0.0114 0.001 0.0114 0.001 -0.0970 -0.004 -0.0970 -0.004 -0.0970 -3.9E-03

(0.12)

(0.12)

(0.16)

(-1.03)

(-0.99)

(-1.26)

prod_dummy 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.0E+00

(.)

(.)

(.)

(.)

(.)

(.)

lagtotnvio_op 0.0114 0.001 0.0114 0.001 0.0114 0.001 0.0182 0.001 0.0182 0.001 0.0182 7.3E-04

(0.55)

(0.58)

(0.69)

(1.07)

(1.08)

(1.26)

_cons -1.974*** 0.000 -1.974*** 0.000 -1.974*** 0.000 -1.645** 0.000 -1.645** 0.000 -1.645*** 0.0E+00

(-3.29)

(-3.31)

(-4.50)

(-2.86)

(-2.63)

(-3.56)

inspection treat -0.673**

-0.673*

-0.673***

-0.696**

-0.696**

-0.696***

(-2.77)

(-2.48)

(-5.53)

(-2.76)

(-2.59)

(-5.81)

numwells -0.00699*

-0.00699**

-0.00699***

-0.00706*

-0.00706**

-0.00706***

(-2.31)

(-2.65)

(-7.65)

(-2.32)

(-2.63)

(-7.64)

production -0.000175

-0.000175

-0.000175**

-0.000168

-0.000168

-0.000168**

(-1.56)

(-1.75)

(-2.80)

(-1.50)

(-1.68)

(-2.73)

lagprod

-0.0000502

-0.0000502

-0.0000502

-0.0000382

-0.0000382

-0.0000382

Syed

Mortu

zaAsif

Ehsan

Chap

ter2.

69

(-1.25)

(-0.81)

(-1.00)

(-0.95)

(-0.61)

(-0.76)

lagins 0.910**

0.910*

0.910**

1.048**

1.048*

1.048**

(2.70)

(2.30)

(2.59)

(2.82)

(2.44)

(2.76)

lagvio -1.116***

-1.116**

-1.116***

-1.091***

-1.091**

-1.091***

(-3.68)

(-2.98)

(-3.96)

(-3.73)

(-2.88)

(-3.89)

lagtotnins -0.0400

-0.0400

-0.0400

-0.0577

-0.0577

-0.0577

(-0.27)

(-0.29)

(-0.35)

(-0.38)

(-0.41)

(-0.51)

lagtotnvio 0.0492

0.0492

0.0492

0.0487

0.0487

0.0487

(0.29)

(0.24)

(0.37)

(0.28)

(0.24)

(0.36)

lagnins 0.738**

0.738*

0.738*

0.629*

0.629

0.629

(2.63)

(2.29)

(2.54)

(1.98)

(1.73)

(1.92)

lagnvio 0

0

0

0

0

0

(.)

(.)

(.)

(.)

(.)

(.)

num_of_yrs_produced -0.163**

-0.163**

-0.163***

-0.142*

-0.142*

-0.142***

(-2.74)

(-3.04)

(-6.21)

(-2.15)

(-2.37)

(-5.05)

prod_dummy 0

0

0

0

0

0

(.)

(.)

(.)

(.)

(.)

(.)

lagtotnvio_op 0.0369

0.0369

0.0369***

0.0357

0.0357

0.0357***

(1.47)

(1.73)

(5.70)

(1.50)

(1.73)

(5.65)

lagtotnins_op 0.00838

0.00838

0.00838***

0.00872

0.00872

0.00872***

(1.30)

(1.46)

(7.06)

(1.39)

(1.54)

(7.45)

Syed

Mortu

zaAsif

Ehsan

Chap

ter2.

70

_cons -0.137

-0.137

-0.137

-0.210

-0.210

-0.210

(-0.31)

(-0.31)

(-0.65)

(-0.45)

(-0.46)

(-0.97)

athrho _cons 1.387*

1.387*

1.387*

1.256**

1.256**

1.256**

(2.14)

(2.11)

(2.31)

(2.70)

(2.92)

(2.91)

N 3724

3724

3724

3402

3402

3402

Wald test .0324

.0347

.0211

.0069

.0035

.0037


="* p<0.05 ** p<0.01

*** p<0.001"

Syed

Mortu

zaAsif

Ehsan

Chap

ter2.

71

Table 2.12: Tobit Model Results

Violation Number All years except 2012 4-years only

Variables Op dy/dx Op-Year dy/dx Well dy/dx Op dy/dx Op-Year dy/dx Well dy/dx

treat -2.979** -0.02029 -2.979* -0.02029 -2.979*** -0.02029 -2.944*** -0.0062 -2.944*** -0.0062 -2.944*** -0.0062

(-2.81)

(-2.48)

(-3.95)

(-7.30)

(-8.04)

(-12.40)

numwells -0.00749 -5.1E-05 -0.00749 -5.1E-05 -0.00749* -5.1E-05 -0.00484** -1E-05 -0.00484** -1E-05 -0.00484*** -1E-05

(-0.84)

(-0.96)

(-1.98)

(-2.78)

(-3.13)

(-4.91)

production 0.000112 7.66E-07 0.000112 7.66E-07 0.000112 7.66E-07 0.000118 2.49E-07 0.000118 2.49E-07 0.000118 2.49E-07

-0.28

-0.28

-0.36

-0.74

-0.81

-1.25

lagprod 0.000231 1.57E-06 0.000231 1.57E-06 0.000231 1.57E-06 0.000236 4.96E-07 0.000236 4.96E-07 0.000236* 4.96E-07

-1.09

-1.09

-0.85

-1.53

-1.7

-2.53

lagins 0.336 0.002289 0.336 0.002289 0.336 2.29E-03 -26.82*** -0.05645 -26.82*** -0.05645 -26.82*** -0.05645

-0.09

-0.12

-0.16

(-96.98)

(-86.97)

(-105.66)

lagvio 2.494 0.01699 2.494 0.01699 2.494 0.01699 31.00*** 0.06526 31.00*** 0.06526 31.00*** 0.06526

-0.67

-0.8

-0.95

-114.53

-92.13

-103.68

lagtotnins -1.096 -0.00746 -1.096 -0.00746 -1.096 -0.00746 -0.845*** -0.00178 -0.845*** -0.00178 -0.845*** -0.00178

(-0.55)

(-0.60)

(-0.74)

(-5.17)

(-4.21)

(-4.84)

lagtotnvio 0.351 0.002388 0.351 0.002388 0.351 0.002388 -27.03*** -0.0569 -27.03*** -0.0569 -27.03*** -0.0569

-0.14

-0.17

-0.19

(-219.48)

(-174.59)

(-166.25)

lagnins 1.662 0.011322 1.662 0.011322 1.662 0.011322 28.72*** 0.06047 28.72*** 0.06047 28.72*** 0.06047

-1.49

-1.51

-1.63

-174.76

-142.93

-148.94

Syed

Mortu

zaAsif

Ehsan

Chap

ter2.

72

num_of_yrs_produced -0.358 -0.00244 -0.358 -0.00244 -0.358 -0.00244 -0.591*** -0.00125 -0.591*** -0.00125 -0.591*** -0.00125

(-1.60)

(-1.37)

(-1.79)

(-7.93)

(-8.85)

(-15.32)

prod_dummy 0 0 0 0 0 0 0 0 0 0 0 0

(.)

(.)

(.)

(.)

(.)

(.)

lagtotnvio_op 0.109* 0.000745 0.109*** 0.000745 0.109*** 0.000745 0.107*** 0.000225 0.107*** 0.000225 0.107*** 0.000225

-2.43

-3.37

-4.02

-8.91

-9.77

-15.04

_cons -7.637**

-7.637**

-7.637***

-8.522***

-8.522***

-8.522***

(-2.75)

(-2.64)

(-4.35)

(-19.38)

(-21.49)

(-36.12)

sigma _cons 4.649***

4.649***

4.649***

5.148***

5.148***

5.148***

-6.41

-7.14

-9.11

-29.36

-33.03

-56.58

N 3724

3724

3724

3402

3402

3402

t statistics in parentheses ="* p<0.05 ** p<0.01 *** p<0.001"

Syed

Mortu

zaAsif

Ehsan

Chap

ter2.

73

Table 2.13: RE and FE Tobit Model Results

All years except 2012 4-years only

Variables RE Tobit FE Tobit RE Tobit FE Tobit

treat -2.979** -2.944* -2.446* -2.905

(-2.98) (-2.35) (-1.96) (-1.87)

numwells -0.00749 -0.00484 -0.000768 0.0000676

(-1.95) (-1.05) (-0.11) (0.01)

production 0.000112 0.000118 -0.000713 -0.000925

(0.30) (0.28) (-1.32) (-1.44)

lagprod 0.000231 0.000236 -0.0000139 -0.000101

(0.65) (0.60) (-0.03) (-0.19)

lagins 0.336 -18.37 -0.656 -6.058*

(0.12) (-0.03) (-0.35) (-2.01)

lagvio 2.494 22.56 2.777 9.171*

(0.84) (0.04) (1.09) (2.07)

lagtotnins -1.096 -0.844 -0.0581 1.046

(-0.59) (-0.41) (-0.05) (0.78)

lagtotnvio 0.351 -18.59 -0.300 -5.359*

(0.15) (-0.03) (-0.21) (-2.23)

lagnins 1.662 20.28 0.408 4.255**

(1.08) (0.04) (0.59) (2.68)

Syed

Mortu

zaAsif

Ehsan

Chap

ter2.

74

num_of_yrs_produced -0.358 -0.592 -0.665 -1.156

(-1.43) (-1.64) (-1.49) (-1.69)

prod_dummy 0 0 0.0346 0.0297

(.) (.) (.) (.)

lagtotnvio_op 0.109*** 0.107**

(3.59) (3.02)

_cons -7.637*** -8.521**

(-3.72) (-3.20)

sigma_u _cons 9.81e-15 7.02e-14

(0.00) (0.00)

sigma_e _cons 4.649*** 5.148***

(7.24) (6.45)

N 3724 3402 3724 3402


="* p<0.05 ** p<0.01 *** p<0.001"


Variables Description

Variables Description

violation =1 if violation(s) found that year lagtotnins Total number of inspections done for the well up to the preceding year

treat =1 if year is after 2011 lagtotnvio Total number of violations found for the well up to the preceding year

numwells Number of wells per operator per year

lagnins Number of inspections done for the well in the previous year

production Average daily production of natural gas that year (in Mcf)

lagnvio Number of violations found for the well in the previous year

lagprod Average daily production of natural gas in the previous year (in Mcf)

num_of_yrs_produced Number of years the well extracted natural gas

lagins =1 if well was inspected in the previous year

prod_dummy =1 is the well produces in that year

lagvio =1 if violation(s) found in the previous year

lagtotnvio_op Total number of violations found for an operator up to the preceding year

Table 2.14: Model’s Variables

Chapter 3

Increased Unconventional Well Fees in

Pennsylvania:

Impacts on Survival, Production, and

Well Violations

3.1 Motivation:

Shale gas refers to natural gas that is contained inside shale formations deep under the

ground. Unlike conventional natural gas resources, shale gas does not migrate out of the

source rock and permit drillers to gain easy access it. Shale gas wells are also known as

unconventional wells because the gas is extracted from deep (4000-6000 feet) underground

shale plays, using horizontal drilling and hydraulic fracturing which are not traditionally

used for accessing shallower gas formations. Act 13 defines an unconventional gas well as

“A bore hole drilled or being drilled for the purpose of or to be used for the production of

natural gas from an unconventional formation” such as Marcellus shale in Pennsylvania (act

2012). Before the advent of these technologies, most wells used conventional drilling methods

to extract natural gas. Technological advancements in hydraulic fracturing and horizontal

drilling have dramatically increased the accessible shale gas reserves in the US. US shale gas

production was only 5% of the total US natural gas production in 2004, 10% in 2007, and in

2015 it was 56%. In the future, shale gas production is expected to increase even more. As

of 2014, proven reserves of shale gas are 45% of the 354 Tcf (Trillion cubic feet) of the total

76


US natural gas reserve.1

Major shale plays that contain most of the shale gas reserves are the Marcellus, Barnett,

Haynesville, Fayetteville, Eagle Ford, and Bakken shale plays. These shale plays include

technically recoverable natural gas of 1,744 Tcf, which includes 211 Tcf of proven reserves.

With the annual production rate is 19.3 Tcf, there is enough natural gas to supply the U.S.

for the next 90 years, with some estimates extending the supply to 116 years (Kargbo, Wil-

helm, and Cambell 2010). Among these, the Marcellus shale play is the largest, underlying

Pennsylvania, New York, West Virginia, Ohio, and Maryland, and comprising 60.8 million

acres or 95,000 square miles (Adair et al. 2011). This shale formation is found between 4,000

and 8,000 feet below the surface. A report issued by Pennsylvania State University in July

2011 estimates that the Marcellus shale formation will be the largest single gas field in the

country, producing a quarter of the country’s gas by 2020 (Nelson 2013). Recent studies sug-

gest that recoverable reserves from the Marcellus shale can be as large as 489 Tcf (Engelder

and Lash 2008). There was almost no production from the Marcellus shale formation in 2008,

whereas in 2014 there were more than 5,400 unconventional wells and each year about 1,200

new wells are added (Swindell 2016)

For several years there has been significant unconventional oil and gas development in Penn-

sylvania. Currently, Pennsylvania is one of the top five shale gas producing states in terms

of the number of unconventional wells. From 2002 to 2012, the number of unconventional

oil and gas wells drilled in Pennsylvania was 6,283, producing 3.7 Tcf of natural gas. One

estimate indicates that at least 60,000 wells will be drilled to produce oil and gas from the

Marcellus shale

As the usage of hydraulic fracturing becomes more widespread, and new concerns are aris-

ing regarding its environmental and health impacts. Tens of thousands of new oil and gas

wells started production using hydraulic fracturing method from shale plays during last few

years. This poses a long-term threat to the environment and health, resulted from soil and

water contamination (Dana and Wiseman 2013). In addition, thousands of wells are aban-

doned each year. These can leak pollutants, and their number is rising over time. Even

though all conventional wells pose many risks, potential environmental and health hazards

are significantly greater from unconventional wells, due to the increased number of stages of

1Source: U.S. Energy Information Administration, Form EIA-23L, Annual Survey of Domestic Oil andGas Reserves 2014.


the well development process, the use of fracturing chemicals, and the increased pressure on

wells caused by hydraulic fracturing (Dana and Wiseman 2013). While hydraulic fracturing

can have adverse impacts that include air pollution, noise pollution, traffic congestion and

accidents, the most serious impact seems to be ground and surface water pollution (Vidic

et al. 2013). Olmstead et al. (2013) investigate the effect of shale gas development on surface

water quality in Pennsylvania. This paper shows that both the presence of shale gas wells

in a watershed and the release of treated water significantly diminish surface water quality.

Fractures made in the process of mining often create or extend fissures above the target shale

formation; these fissures may join with those from naturally existing fractures. Osborn et al.

(2011) and Warner et al. (2012) find that fracturing fluids can therefore reach shallow aquifers

and degrade groundwater quality through methane contamination. Myers (2012) further con-

cludes that hydraulic fracturing fluids can transport to groundwater aquifers within ten years.

Even though there are several concerns stemming from the unconventional drilling of shale

gas, regulatory policies have not been adjusted to address these new concerns. Unconven-

tional drilling activities have major exemptions from federal environmental statutes. The

U.S. Department of Energy provides several recommendations for reducing the environmen-

tal impact of shale gas production. Some of these recommendations are that state regulators

should adopt requirements for background water-quality measurements prior to shale gas

production and for disclosure of the chemicals that are in fracturing fluid (Adair et al. 2011).

However, regulatory practices in shale gas development are implemented mostly by states.

There is a high degree of heterogeneity among states in their approaches to regulating hy-

draulic fracturing. This heterogeneity results from differences in state’s geology, geography,

history, demographics, economic conditions, and other factors. Richardson et al. (2013) pro-

vide a broad overview of the similarities and differences among state regulations for hydraulic

fracturing. This report analyzes 25 regulations from 31 states. These regulations are pre-

sented in Table-3.1 which is taken from Richardson et al. (2013). From Table-3.1 we can

see that most states rely on command-and-control policies that mandate that drilling oper-

ators follow specific requirements and technologies. States also follow numerical standards,

performance-based approaches, case-by-case permitting, and bans. Many states have several

hydraulic fracturing disclosure rules related to hydraulic fracturing treatment, the volume of

the fluid, chemicals used in the fracking fluid, and the pressure at which fluids are injected.

Richardson et al. (2013) found a strong positive relationship between the level of gas devel-

opment and the number of regulatory practices of a state. States that have rapid growth of

shale gas development are incurring increasing administrative costs for their regulatory ac-

tivities. Lawmakers are trying to finance this increased spending through fees and severance


taxes on the natural gas industry.

Regulators in Pennsylvania, one of the largest shale gas producing states, suggest that envi-

ronmental concerns related to shale gas production are new and expanded. Hence, Pennsyl-

vania has implemented several policies in order to regulate the hydraulic fracturing industry

and shale gas development. Pennsylvania has legislated presumptive liability, meaning that

an operator is legally responsible for water contamination within 2500 feet of a well, if the

pollution happens within twelve months of extraction. The operator must demonstrate that

she is not responsible for contamination to avoid liability.2 This provides an added incentive

for operators to test water supplies within the area of presumptive liability before they start

the extraction process. Pennsylvania’s oil and gas enforcement policy says in DEP (2005),

“An enforcement action is to be taken for each identified violation. No violation is to be ig-

nored.” Pennsylvania also has several disclosure rules and regulations that went into effect in

February, 2011. These rules require disclosure of CAS (Chemical Abstract Service) numbers

for all additives used in the fracking fluid, reporting of the maximum pressure used during

the process of fracking, how flow-back is disposed, and the method of storage at the well site

(McFeeley 2012).

One very important factor that Richardson et al. (2013) discuss is how effectively regulations

are enforced. Many states lack the administrative structures required to enforcement, and

many state oil and gas regulators struggle to retain qualified staff due to higher salaries in

the private sector. Both of these make it difficult to enforce existing regulatory policies.

Between 2008 and 2013 in Pennsylvania, regulatory staffs conducted 44,564 inspections of

unconventional wells and noted 4,655 violations (Dana and Wiseman 2013). Even though

in Pennsylvania the number of inspectors has not kept up with the rising number of uncon-

ventional wells, a substantial number of enforcement staff have been added in recent years.

The Department of Environmental Protection indicates that it hired 37 additional oil and

gas employees in 2009 and 68 more in 2010. The total number of state employees regulating

Pennsylvania’s gas industry now is more than 200 (DEP 2013). In addition to these, Pennsyl-

vania both increased fees for conventional wells and created a new fee that covers horizontal

well applications.3 Pennsylvania also has a setback requirement for unconventional wells,

which states the minimum required distance between a well and municipal water intakes and

2For detail see Title-58, part III, chapter 32. Source: http://www.legis.state.pa.us/cfdocs/legis/

LI/consCheck.cfm?txtType=HTM&ttl=58.3See 25 PA Code 78.19 (2009), Source: http://www.pacode.com/secure/data/025/chapter78/s78.19.

html

http://www.legis.state.pa.us/cfdocs/legis/LI/consCheck.cfm?txtType=HTM&ttl=58


http://www.pacode.com/secure/data/025/chapter78/s78.19.html

http://www.pacode.com/secure/data/025/chapter78/s78.19.html


reservoirs. These requirements have been extended by the Pennsylvania Governor’s Marcellus

Shale Advisory Commission and the Pennsylvania Department of Environmental Protection

(Adair et al. 2011).

With the expansion of shale gas production, it has been increasingly difficult for the states to

finance their regulatory activities. States are financing these increased administrative costs

though fees and severance taxes. States typically charge permit fees for oil and gas activi-

ties. In response to the rapid growth of the Marcellus shale industry, on February 7, 2012,

the Pennsylvania General Assembly introduced comprehensive amendments to Pennsylvania

laws that regulate the oil and gas industry. On February 14, 2012, Act-13, which is also

known as “Impact Fee”, was signed into a law.4 Act-13 was the first comprehensive overhaul

of Pennsylvania’s 1984 Oil and Gas Act. This act amends Title-585 in order to regulate un-

conventional gas development in the Marcellus shale. Through county governments, Act-13

imposes new fees on the unconventional gas wells in Pennsylvania. The legislation provides

for an unconventional gas well impact fee, the administration of that fee, and the distri-

bution of the subsequent fee revenue. Additionally, the legislation addresses the regulation

and permitting of the industry, matters related to local zoning, and improved environmental

safeguards.6 These fees apply to all the existing wells. Hence, any unconventional well con-

structed prior to the enactment of the statute is included in the fee change.

The revised fee amount in Act-13 depends on two factors: years of operation and the average

annual price of natural gas (PennFuture 2012). The fee is assessed annually for fifteen years.

The fee begins between $40,000 and $60,000 per well in year one, and decreases annually

over time until final payments are between $5000 and $10,000 in years 11 to 15. There are a

number of consequences for an operator that fails to pay its fee on time. The DEP is required

to withhold permits if an operator has not paid its fees on time. The DEP is also required

to suspend the permit of any well for which a fee has not been paid. The statute gives the

Public Utility Commission (PUC) broad powers to collect and distribute the fee and imposes

interest charges and monetary penalties for a company’s failure to pay the fee on time.7 The

fee schedule in Act-13 is presented in Table-3.2.

4Please see Public Utility Commission website for details: http://www.puc.state.pa.us/filing_

resources/issues_laws_regulations/act_13_impact_fee_.aspx5For detail of Title-58 that governs the oil and gas industry in Pennsylvania please see: http://www.

legis.state.pa.us/cfdocs/legis/LI/consCheck.cfm?txtType=HTM&ttl=58.6Source: http://www.iop.pitt.edu/shalegas/PDF/unconv_status_oil%26gas_ad.pdf7Source: http://pennfuture.org/UserFiles/File/MineDrill/Marcellus/CitizenGuide_Act13_

2012.pdf

http://www.puc.state.pa.us/filing_resources/issues_laws_regulations/act_13_impact_fee_.aspx

http://www.puc.state.pa.us/filing_resources/issues_laws_regulations/act_13_impact_fee_.aspx



http://www.iop.pitt.edu/shalegas/PDF/unconv_status_oil%26gas_ad.pdf

http://pennfuture.org/UserFiles/File/MineDrill/Marcellus/CitizenGuide_Act13_2012.pdf

http://pennfuture.org/UserFiles/File/MineDrill/Marcellus/CitizenGuide_Act13_2012.pdf


In addition to the significant increase in the unconventional well fee (also called an impact

fee), Act-13 establishes bonding rates dependent on the size of the wells. In Pennsylvania, a

typical horizontal well extends from 2000 to 6000 feet (Arthur, Langhus, and Alleman 2008).

For a horizontal well in Pennsylvania that is less than 6000 feet in length the bond rate is

$4,000. The total bond amount range from $4,000 to $250,000, depending on how many wells

are operated by a firm from one to more than 250 wells. If a well is more than 6,000 feet

in length the rate is $10,000, and the total bond amount ranges from $10,000 to $600,000

depending on how many wells are operated (PennFuture 2012). Bonds are released when the

well is properly plugged and the well site is restored. However, the bond will not be returned

and the amount will be collected in full if the owner of the well fails to restore the site properly.

Pennsylvania is one of the top five largest natural gas producing states (Texas, West Virginia,

Pennsylvania, Ohio, and Oklahoma), producing approximately 7.5% of the total natural gas

production in the US, and this percentage is increasing. Therefore, this major change in

the fee schedule of the unconventional wells will potentially have significant impacts on shale

gas development in the US, potentially reshaping the hydraulic fracturing industry in Penn-

sylvania. This might have further implications for other states, because many states are

considering an increase in the fee and bonding requirements for wells that are hydraulically

fractured (Davis 2015). The increased unconventional well fees that were applied to all new

and existing wells in the Act-13 can have several impacts. For example, this fee will increase

the fixed cost of extraction, which can create more barriers to entry for incumbents. This can

lead to a noticeable reduction in the number of new unconventional wells after 2012. Existing

wells also incur the increased fee which is assessed annually for fifteen years. Coupled with

the fact that most unconventional well extractors are small and medium sized firms (Davis

2015), we can expect existing wells to close or stop their production process earlier than they

otherwise would have.

Another possibility we explore is the effect of the increased fee in Act-13 on the extraction

rate of existing wells after 2012. We know that a fixed cost increment resulting from Act-13

does not change the marginal optimization condition of an individual producer. Hence, de-

cisions about the level of production given that a well stays in production, which depend on

cost at the margin, should not be affected by this policy change. However, one important

thing to note is that the revised fee in the Act-13 is not a one-time fixed cost. The fee is

assessed annually for fifteen years, with the amount depending on the years of operation and


the average annual price of gas. This increases the cost of wells in the long run. (Davis

2015) found that the hydraulic fracturing industry is relatively unconcentrated, and tens of

thousands of new unconventional wells are drilled each year. Therefore, the increasing US

shale gas production level is combined with low natural gas prices (PPI-Energy and Team

2013). Taking these factors into consideration, the increased long-run average cost of ex-

traction accompanied by a decreasing natural gas price can reduce the amount of extraction

done by wells in Pennsylvania.

Numerous sources in existing literature study the impacts of environmental regulations or

taxes from various angles. However, most of these study regulations such as emission stan-

dards. This literature covers a great range of outcomes, such as firm creation (List et al.

2003), productivity (Becker 2011), decisions about firms’ locations (Brunnermeier and Levin-

son 2004), impacts on investment flows (Keller and Levinson (2002), Hanna (2010)), and

welfare impacts (Ryan (2012)). These studies conclude that environmental regulations en-

courage firms to locate elsewhere, and reduce welfare, firm creation, factor productivity, and

investment. Even though a number of papers such as Metcalf (2008) study various types of

environmental and severance taxes theoretically, literature exploring their impacts on firms’

decision-making is very thin. Martin, de Preux, and Wagner (2014) investigate the impact of

a carbon tax on manufacturing plants in the UK. They conclude that the tax would decrease

the energy intensity of production, but would not affect firm size, revenue, or the survival

rate. Another related branch of the literature explores the effects of taxes based on level of

production on exploration and production of firms (Chakravorty, Gerking, and Leach (2010),

Kunce et al. (2003)). Most of this literature suggest that oil and gas production is not af-

fected by marginal changes in tax rates. However, these studies are based on conventional

wells which are fundamentally different than the shale unconventional wells. Hence, their

conclusions do not necessarily hold for unconventional wells. In this chapter we explore im-

pacts of the introduction of Act-13 (Impact Fee) in 2012 on the hydraulic fracturing industry

in Pennsylvania from several angles. We first investigate how the likelihood of the shutting

down or survival of a well has been affected by the major change in the impact fee imposed on

unconventional wells. We also estimate the effect of Act-13 on the extraction rate of existing

unconventional wells. Lastly, we explore whether there is any effect on the probability of

environmental and administrative violations by shale wells in Pennsylvania from the change

in the fee-structure of unconventional wells in 2012.

This chapter is structured as follows: Section 3.2 discusses the collection, arrangement, and


summary statistics of datasets used in this paper. We also briefly discuss several trends of

shale gas development in Pennsylvania in this section. Section 3.3 describes the econometric

methodologies we have used for our analyses. Section 3.4 gives an overview of the results and

findings from these analysis. Section 3.5 discusses conclusions. All Figures, Tables, graphs,

and maps are included in the Appendix section.

3.2 Data and Overview:

For the 36 active oil and gas developing states, most state and federal regulatory agencies

publish little or no information regarding unconventional wells and their compliance with

regulations (NRDC April 2015). However, the Pennsylvania Department of Environmen-

tal Protection keeps records regarding several oil and gas activities occurring in that state.

These records are available to the public in spreadsheet format through their website.8 From

this website we have collected data on permits, inspections, violations, production, and the

amount of waste from unconventional wells in Pennsylvania for 2004 to 2014. Among other

items, the inspection-violation dataset provides information about the operator of well, date

of inspection, the American Petroleum Institute (API) number, type of well (“conventional”

or “unconventional”), county of operation, results from inspection (if “violation” was re-

ported or not), date of violation, and the type of violation. In this dataset, violations are

classified as either administrative or environmental health and safety violations. While it can

be argued that environmental health and safety is more serious of the two categories, some of

the most serious infractions, for example, improperly lined pits or improper casing to protect

fresh groundwater, are often included as administrative violations. In the case of water con-

tamination, the Pennsylvania DEP does not issue a violation notice, or formal determination

if the company has taken voluntary action to restore the water supply or reached a private

legal settlement with water well owners (NRDC April 2015).

On the Pennsylvania DEP website, unconventional well production data are provided seper-

ately for the years from 2004 to 2014. We have collected and combined production data of

individual years. For some years, production data are provided bi-annually and for other

years data on the yearly amount of production are available. We first calculate the average

yearly production amount for all wells in different years. Then we combine all these yearly

production amounts to get the final dataset of unconventional well production in Pennsyl-

8Source: http://www.depreportingservices.state.pa.us/ReportServer?/Oil_Gas

http://www.depreportingservices.state.pa.us/ReportServer?/Oil_Gas


vania from 2004 to 2014. The combined production dataset provides information about the

well permit number, wheather the well produced or not, the amount of production in Mcf

(1,000 cubic feet), location information, and the type of well. In this dataset, production

amounts are reported annually in Mcf (1,000 cubic feet), from which we calculate daily rates

of production of unconventional wells. The combined waste dataset is made in the similar

way. We also collect natural gas price data from the U.S. Energy Information Administration

website.9 This dataset includes the monthly Citygate10 price of natural gas in Pennsylvania

(in dollars per Mcf) from 2004 to 2014. Lastly, we collect the well permit dataset, which pro-

vides information about the type of well, spud date, well API number, location (latitude and

longitude coordinates), depth of the well, name of the well operator, and county of operation.

These datasets on production, inspection, violation, and geo-spatial information are com-

bined, based on unique well API numbers and years of operation. Also, we have assigned the

average price of natural gas for each year, using the price dataset. In this analysis, we have

considered only wells that were producing natural gas with nonzero production amounts. For

each well, we calculate the number of years of its operation or extraction for any given year.

Also, we include only wells that were producing in the consecutive year. A well that has a

break in its years of producing, that is, if a well has a non-producing year between its start

and the end year of production, that well is excluded for consistency with our survival anal-

ysis. If a well was producing in a certain year but did not appear in the inspection-violation

dataset, we assume that the well was not inspected. We also calculate the number of viola-

tions and inspections for each well in each year. The number of violations and inspections per

operator in a year is calculated by summing over all the violations and inspections reported

for wells owned by that firm. In addition to these, we calculate the running sum of annual

violations and inspections for wells and for operators.

This yields an unbalanced panel dataset of production, inspection, violation, ownership, lo-

cation information, and natural gas price for 6,300 unconventional wells (owned by 103 firms

or operators) in Pennsylvania from 2004 to 2014 with a sample size of 19,074 observations.

Table-3.3 shows descriptive statistics of our dataset. We can see that unconventional wells

over the years in Pennsylvania are owned by 103 operators. Each year an operator owns

from 1 to 791 wells, with an average ownership of 273 unconventional wells. The number

9Source: http://www.eia.gov/dnav/ng/hist/n3050pa3m.htm10According to the U.S. Energy Information Administration, Citygate is defined as “A point or measuring

station at which a distributing gas utility receives gas from a natural gas pipeline company or transmissionsystem.”

http://www.eia.gov/dnav/ng/hist/n3050pa3m.htm


of inspections for an operator per year can be as large as 858. On average, an operator is

inspected 294 times in a given year with average number of 23 violations. The maximum

numbers of annual inspections and violations for a well are 58 and 39 respectively, while a well

in given year is inspected on average at least once. In our dataset, we have 10,306 inspected

observations out of 19,074 observations for Pennsylvania. Out of these inspections there are

1,078 violations. The average daily production of a well can be as large as 23,044 Mcf with

an average production rate of 1,803 Mcf. The total numbers of violations and inspections

over the previous years for a well range from 0 to 39 and 0 to 73 respectively, while the total

number of violations and inspections over all previous years for an operator range from 0 to

453 and 0 to 3,309 respectively. We also see that the duration of extraction or production

for a well ranges from 1 year to a maximum of 11 years. An average unconventional well in

Pennsylvania produces natural gas for 4 years.

Because our dataset is a panel dataset, we can get a more interesting overview from different

types of existing variations. Table-3.4 shows within (over time) and between (across wells)

variations of our panel dataset. The minimum and maximum values for the within varia-

tion are derived from the individual well’s deviation from its own average (over time), while

subtracting the overall mean from the individual mean (over time) yield the minimum and

maximum for the between variation. Table-3.4 shows that almost all variables have more

between variation than the within variation. This implies that observations of a well across

years have less variation than the variation of observations across different wells in a given

year. This is reasonable and makes sense, because it simply means that wells are more dif-

ferent from one another than one well from earlier or later versions of itself. However, the

“number of violation” per well has slightly more within variation than between variation.

The “number of operating years” for a well is a time invariant variable, because for this

variable we consider the total number of producing years for each well regardless of their

specific year of operation, hence it has zero within variation.

Figures 3.5 - 3.7 give an overall picture of horizontal and unconventional wells in Pennsylvania

for years 2004 to 2014. From Figure-3.2 we can see that the number of unconventional wells,

the number of operators using hydraulic fracturing, and the number of incidents in Pennsyl-

vania are increasing over the years. While the number of unconventional wells is increasing

exponentially, the number of inspection has decreased slightly since 2011. These two facts

show why the number of violations started to decrease after 2011 (increased number of wells

coupled with fewer inspections). Another possible reason behind this reduction in the num-


ber of violations is Act-13, which was proposed and in process in 2011. The last two panels of

Figure-3.5 show the average number of inspections and violations per well in Pennsylvania.

Even though the number of unconventional wells has been increasing significantly, the size of

the regulatory staff did not increase proportionally. Therefore, the ratio of wells to inspectors

in Pennsylvania remains high, which results in a decreasing average number of inspections per

well after 2008, when shale gas development started expanding. This, coupled with Act-13,

eventually resulted in a decreasing trend in the average number of violations per well. Figure-

3.6 reflects the previous figure, showing that the number of wells and inspections per operator

have both been slightly increasing, while operators that were reported for higher numbers of

violations decreased after 2011. Because inspection is done at the well level and not at the

operator level, we can have a better picture from Figure-3.7, which shows that we have lower

frequency of wells having a high number of inspections and violations after 2011. Therefore,

to separate the causal effect of Act-13 after 2012, we have controlled for the number of in-

spections and violations in our models. Figure-3.7 also suggests that wells that produce for

less than five years have increasingly more violations as they continue to extract. However,

wells that produce for more than five years have a decreasing number of violations as they

produce. This implies that for wells that produce for more than five years, there is an inverse

relationship between their years of operation and the number of violations reported for them.

Using spatial information from our dataset, we generate GIS maps of unconventional wells,

inspections, and violations in Pennsylvania for four years: 2008, 2010, 2012, and 2014. Each

dot in these maps represents one well. These maps are presented in Figures 3.8 - 3.10 and

reflect trends seen in Figure-3.5. The number of unconventional wells and the number of

inspections have been increasing over the years, whereas, Figure-3.10 shows that the number

of violations has decreased since 2012.

3.3 Empirical Strategy:

In this chapter we explore effects of the introduction of Act-13 (Impact Fee) in Pennsylvania

from several perspectives. First, we investigate wheather existing wells shut-down earlier af-

ter 2012, due to the major change in the impact fee imposed on unconventional wells. Even

though on average an unconventional well in Pennsylvania extracts for four years, we cannot

define “early” for a well because it depends on a number of other factors. However, we can

use the calculated “probability of well’s shut-down” as an indicator of a well’s likelihood


of stopping its extraction process in a given year. We try to discern how this likelihood of

a well’s shut-down has been affected by the significant increase in the unconventional well

fee-structure in 2012. Another important thing to note is that the fee is assessed annually for

fifteen years, at a level that depends on the years of operation and the average annual price

of natural gas. This changes the long-run average cost of extraction, hence the extraction

rate can be expected to change. Therefore, we estimate how the extraction rate has changed

after 2012 for existing unconventional wells. We also investigate the impact of this major

increase in unconventional well fees in 2012 on the probability of violation by shale gas pro-

ducers in Pennsylvania. In this section we discuss methodologies that we use for our analysis.

3.3.1 Survival analysis

In order to analyze the effect of the Impact Fee change of Act-13 in Pennsylvania on the

probability of the shutting down of an unconventional well, we use survival analysis. Sur-

vival analysis is also called duration analysis, where subjects are tracked until an “event”

occurs. There are four key factors for survival analysis: states, events, risk period, and dura-

tion or time. “States” are various categories of the dependent variable of interest. A well has

“producing” state beginning at the start-up date, and transit into “shut-down” state once

it stops its production process. The set of possible states is called “state space,” which is

{producing, shut-down} for our analysis. “Event” is the transition from one state to another.

For our analysis, “event” is the transition of a well from producing to shut-down.

Our production data on unconventional wells in Pennsylvania includes wells producing no

natural gas in a year. Therefore, we can assume that the first year of production for any

well in our dataset is the start date of the risk of shutting down. And we assume that the

last date of production for a well in our dataset is when the well did shut-down. We exclude

wells that have unproductive year(s) between the start and end date. Only 190 observations

are excluded this way. “Risk period” means the period of time an individual is at risk of a

particular event. The set of all individuals at risk of an event at a point in time is called

the “risk set”. For this chapter, “risk period” is the period during which a well continues

its extraction process, and the “risk set” in a certain year is the set of all wells producing

in that year. Lastly, “duration” is the time since the start of risk, which in our model is

the time since the well starts to extract. The main feature of survival analysis is that the

dependent variable is a combination of two factors: “risk period” (the length of time until


an event occurs) and “event”. We have a panel dataset of single and non-repeatable events,

because a well is shut-down only once.

In this analysis, we are interested in how long an unconventional well stays in the sample, in

other words, how long it survives and what is the risk (“hazard rate”) of its shut-down. We

model the probability of shut-down of a well at a given time period, conditional on the fact

the well has been producing, hence included in the “risk set.” In the survival analysis, we

assume that the dependent variable “duration (t)” has a continuous probability distribution

f(t) ∈ [0, 1]. Hence, the cumulative probability of an event up to time t, that is, the

probability that a well will have stopped its extraction within t periods is: F (t) =∫ t0f(s)ds.

We assume that the longer the follow-up time is, the greater is the probability that a well

will have shut-down. Another term of importance is the survival probability, which is the

probability that the event will not occur until time t, S(t) = 1 - F(t). Combining these

we get the “hazard function” h(t), which is an instantaneous conditional probability that

an individual well will stop its extraction process after t periods, conditional on the well’s

continued production for t periods. Therefore, h(t) can be expressed as follows,

h(t) =f(t)

S(t)where f(t) ∈ [0, 1] and S(t) = 1− F (t) (3.1)

Different models impose different distributional assumptions on the hazard function in Equation-

3.1. We use three types of hazard models in our analysis. First, we use non-parametric

analysis to gain a general idea about the hazard function. Then we use the Cox proportional

model which is a semi-parametric hazard model, to investigate the effect of the major fee

change on the probability of a well shutting down. Finally, we use parametric analysis with

different distributions for the hazard function in Equation-3.1.

Non-parametric survival analysis

Non-parametric models do not impose any assumption on the structure of the data. Such

a model is useful for descriptive purposes and to map out the shape of the hazard and

survival functions before we estimate semi-parametric and parametric models. However, a

non-parametric model does not allow for the inclusion of regressors. In this model, observa-

tions are sorted based on their duration of survival. For each duration t we determine the

number of events (shut-down) dt and the number of observations at risk nt (observations that


are still in the sample). Then the non-parametric hazard function, Nelson-Aalen estimator of

cumulative hazard function, and Kaplan-Meier estimator of the survival function for duration

t are as follows,

fnp(t) = dt/nt (3.2)

Fnp (T ) =T∑t=0

f (T ) =T∑t=0

dt/nt (3.3)

Snp (T ) =∏T

t=0

(nt − dt)nt

(3.4)

The Kaplan-Meier estimator of the survival function for duration t in equation-3.4 takes

ratios of those wells without events (nt − dt) to those at risk (nt), and multiples them over

time. This gives a decreasing step function with a jump at each discrete event time.

Semi-parametric survival analysis

The most commonly used model for a survival analysis is the semi-parametric model (Guo

and Zeng 2014). The baseline hazard h0(t) in this model is not determined ex-ante, but it

must be positive. An advantage of this model is that regressors can easily be incorporated,

unlike the non-parametric model, and there is less structure than parametric models, because

it does not assume specific functional form for the baseline hazard function h0(t). However,

it does not provide a baseline hazard and we can only interpret results in terms of relative

differentials. In order to use a semi-parametric method for this chapter, we follow the Cox-

proportional hazard model. We can include explanatory variables in this model, the effects

of which come on the hazard rate in a multiplicative way. In this case, the hazard rate of an

event (shut down) at time t, given the effects of explanatory variables is:

hsp (t) = hsp (t|x, β) = h0 (t) exp (x′β) (3.5)

In Equation-3.5, x is the set of explanatory variables used in the survival analysis (shown

in Table-3.11) and h0 (t) is the baseline hazard rate for duration t which is not affected by

the explanatory variables, and this only changes over the duration of survival. One impor-


tant implication of this specification is that the Cox-proportional hazard model assumes the

hazard rate to be the same over time and across groups. This means that hazard will be

proportional, and differences in the covariates simply lead to differences in the relative hazard

rates at a point in time. Vector β comprises the coefficients of the explanatory variables,

which we estimate in the Cox-proportional hazard model.

Parametric survival analysis

Parametric models allow the inclusion of explanatory variables. However, these models im-

pose specific structure on the data. That is, the baseline hazard rates h0(t) in these models

are assumed to vary in a specific manner with time. Different specifications of h0(t) lead to

different parametric survival models. We use three parametric survival models: the Expo-

nential model, the Weibull model, and the Gompertz model. Specifications of these models

are as follows:

Exponential hazard: Fp(t) = 1− e−λt, Sp(t) = e−λt

hp(t) = λ (t) exp (x′β) (3.6)

Weibull hazard: h0(t) = λp(λt)p−1 (3.7)

Gompertz hazard: h0(t) = λert (3.8)

In the case of the exponential hazard model, the baseline hazard rate λ is constant. When-

ever t → 0, F (t) → 0, that is, at the initial periods the cumulative probability of an event

occurring is small. The Weibull distribution leads to hazard rates which either increase or

decrease monotonically over time. If p is greater than one, the hazard rate is increasing, if p

is less than one, the hazard rate is decreasing, and if p = 1, we have a constant hazard rate.

For the Gompertz hazard model, the hazard rate increases or decreases at an exponential

rate. For all of these three models, in Equations 3.6 - 3.8 we estimate the set of parame-

ters β that is, we estimate effects of different explanatory variables on the hazard rates of

unconventional wells. All of the parametric models give nearly identical results. For our pa-

per we compare all the results from different parametric specifications of the hazard function.

We start with uni-variate analysis before we delve into more sophisticated models. Then we

explore whether or not to include certain predictors in the final model based on specification


tests. To validate the inclusion of our regressors in the categorical explanatory variables,

we used the log-rank test of equality across strata, which is a non-parametric test, and for

the continuous predictors we use a uni-variate Cox-proportional hazard regression. Following

the literature, we include a predictor if the significance test has a p-value of 0.2 or less. We

assume that if the predictor has a p-value greater than 0.25 in a uni-variate analysis, it is

unlikely that it will contribute anything to a model that includes other predictors. While

it would be ideal to include interaction terms that are theory driven, we do not have any

prior information about specific interactions that we must include. Therefore, we consider all

possible interactions. Based on the level of significance, we include several interaction terms.

We can compare the model with the interactions to the model without the interactions using

a likelihood-ratio test. The p-value ρ is 0.00 which indicates that we can reject the null

hypothesis that both models fit the data equally well. We conclude that the bigger model,

with the interaction terms, fits the data better than the smaller model which did not include

the interaction terms. The set of explanatory variables (x) used in our survival analysis is

presented in Table-3.11. We also check the proportionality assumption, which is a crucial

assumption for the Cox-proportional hazard model.

One important thing to note is that if a well started its production process before 2004, we

will have a left truncated dataset. To address the left truncation problem, we exclude all

observations from our dataset for wells that produced in 2004 (because some unidentifiable

subset of them presumably started production before 2004). This ensures that wells appear-

ing in the dataset started producing from 2005. We also exclude wells that had a break in

production in any year between their start and end dates, so that the event “shut-down”

appears only once for each well. There are also wells that started producing in years at or

near the end of the observed period, like 2013 and 2014, so that they have only a few years

of production. To address the resulting problem of an element of right censoring, we exclude

wells that started production in 2014. However, this does not cause significant problems for

our analysis, because such wells are few in number, and the hazard rate is low in the early

years for all wells. Therefore, the dataset we have for our survival analysis is for the years

2004 to 2014, while the dataset we use is comprised observations from all wells that started

production process between 2005 and 2013.


3.3.2 Rate of extraction analysis

In order to investigate how the extraction rate changed after 2012 for the existing uncon-

ventional wells due to the increased fees in Act-13, we run several panel data models. Each

model is run for two specifications. In one specification, we use all years 2004-2014 except

2012. In another specification, assuming that the confounding factors which are not included

in our model, do not change much in a short time span, we use observations from 2011 and

2013 (7,239 observations). In both of these cases we exclude 2012, considering it to be the

adjustment period. One thing to note is that each well’s observations can be correlated across

time. To address this fact, in addition to estimating robust estimators, we also obtain cluster

estimators by addressing well level cluster effects. Theoretically, these two yield the same

parameter estimates. First, we run a pooled OLS model. Table-3.4 suggests that for most

variables there are more within variations than between variations. This can be a result of

individual well-specific heterogeneity. Therefore, we run Random Effect models to address

unobserved heterogeneity of wells. For the case of RE, we assume that the unobserved het-

erogeneity is not correlated with any of the predictors. However, this assumption can be

violated for many reasons. In such cases, there will be endogeneity problems in our model,

which lead to inconsistent estimations under both polled OLS and RE models. We address

this problem by running Fixed Effect models that remove time invariant individual specific

effects of wells. Then we run the Hausman test to investigate whether there was indeed any

endogeneity problem. To estimate the change in the rate of extraction due to the change in

the impact fee for unconventional wells, we estimate the following model,

yit = µt + βxit + γzi + αi + εit (3.9)

In Equation-3.9, i is the i’th well and t is the year t. The daily rate of natural gas extraction

of well i at time t is yit. The intercept term µt can be different for each time period t. The

explanatory variables whose values can vary across time are denoted by xit, and zi stands for

the independent variables whose values do not change across time. The coefficient vectors for

xit and zi are respectively β and γ, whereas αi is the error term coming from individual spe-

cific heterogeneity which only varies across individuals but not across time. εit is different for

each individual at each point in time. In order to run pooled OLS and RE models, we assume

that error terms are not correlated with the time varying explanatory variables in the model,

that is, corr(αi, xit) = 0. The fixed effects method controls for time-invariant variables that

have not been measured but affect yit. Therefore, even if the assumption corr(αi, xit) = 0 is

violated, the FE model can consistently estimate β by removing the time invariant effects


or individual specific heterogeneity. The set of explanatory variables x, z used in our rate of

extraction analysis is presented and defined in Table-3.11. We have not included “lag prod”

(average daily production of natural gas in the previous year (in Mcf)) as an explanatory

variable, in order to address potential endogeneity problem.

3.3.3 Probability of violation analysis

For exploring the impact of the unconventional fee change on the probability of violation by

wells, we use classic binary outcome models. Unlike for the probability of shut-down, the

survival analysis is not appropriate to model the probability of violation. This is because

violations can be repeated, whereas shut-down is a non-repeated terminal event, occurring

only once for each well. If i ∈ {1, 2, ..., N} is the i’th well and t ∈ {1, 2, ..., T} is the t’th

year, then yit shows if the well i was reported for violation in the year t, and xit is the

set of values of predictors for the well i in year t. Hence, the dataset can be written as

{yit, xit}i∈{1,2,..,N}, t∈{1,2,..,T}. We estimate the probability of violation of a well i at time t,

with yit as a function of a set of explanatory variables xit from the sub-sample of inspected

observations. Following classic binary outcome models assumptions, we assume the latent

variable is y∗it such that

y∗it = x′itβ + uit (3.10)

where β is the vector of coefficients associated with xit. The stochastic error term uit of this

latent variable y∗it is not observable. Even though this hypothetical continuous variable y∗it

is not observable, we assume it determines whether or not there is violation reported by the

following criteria,

yit =

{1 if y∗it > 0

0 if y∗it ≤ 0

We also assume the error term in Equation-3.10 follows a normal distribution, that is, uit ∼N (0, σu

2it). Therefore, the probability of violation conditional on the set of explanatory

variables x is as follows:

prob [viol = 1|xit] = F (x′itβ) =

∫ ∞−∞

ϕ (x′itβ) dx′itβ ∈ (0, 1) (3.11)


where β is the set of paremeters and ϕ (x′itβ) is the normal probability distribution function

of (x′itβ). That is,

ϕ (x′itβ) = √2πe−(x′itβ)2/

2 (3.12)

The Probit model in this scenario will estimate the following joint log-likelihood function and

it will estimate β using the maximum likelihood estimation (MLE) method which maximizes

Equation-3.13,

lnL (β) =T∑t=1

N∑i=1

(yit lnF (x′itβ) + (1− yit) ln (1− F (x′itβ))) (3.13)

From Table-3.4 we see much more variation from one well to the next compared to the

variation of the same well across years. This can be a result of individual well specific hetero-

geneity. Therefore, after running basic Probit models we use Random Effect Probit models

to address unobserved heterogeneity of wells. Recall that in our sample we obtain the vio-

lation data only for wells that were inspected. A violation is not observed if a well is not in

the sample of inspected wells. Therefore, if unobservables driving inspections are correlated

with unobservables driving violations, we may have sample selection bias in our analysis.

This can lead to inconsistent estimation in the Probit and RE Probit models. Probit and

Random Effect Probit models would be sufficient if the missing violation data were missing

completely at random. Because we do not have violations for those that were not inspected,

we have a nonrandom sample. Using an arbitrary value of zero for violation of all unin-

spected wells may result in an under-estimation of the probability of violation. Therefore,

we use the Heckman Selection method to correct the bias in sample selections, resulting from

individual wells being selected in the group of inspected wells. Different factors might affect

the inspection of a well. The set of explanatory variables used in our violation probability

analysis is presented in Table-3.11.

The main variable of interest in all of our three analyses is “treat,” which takes a value of

1 if the year is after 2012 and takes 0 otherwise. This variable indicates the consequence of

the change in the unconventional fee introduced in Act-13, passed in 2012. In our analysis,

we control for many other important variables. One important factor that can affect the

likelihood of shutting down and the violation probability for a well is the level of production.


Therefore, we include the level of “production” as an explanatory variable in our analysis.

However, we do not include “lagprod” as an explanatory variable in order to address the

potential endogeneity problem. In addition to this, the number of years a well has been

producing can affect a firm’s decisions about extraction and actions affecting the probability

of being cited for a violation. Hence, we include “num of yrs produced” as a regressor in

these analyses. Another important factor that we introduce in our analysis is inspection

effort. Different frequencies and times of inspection can affect our dependent variables (the

likelihood of shutting down, rate of extraction, and violations) in different ways. Therefore,

in our analysis we control for “inswell” (annual number of inspection per well), “lagins”

(whether the well was inspected in the previous year), “lagnins” (number of inspections of a

well in the previous year), and “lagtotnins” (total number of inspections conducted for the

well before that year). The likelihood of shutting down, rate of extraction, and probability

of violation for a well can also be affected if in the previous year the well was reported for

violations, and by the number of violations reported in that year. Because the total number

of violations by a well before that year can be an indication of whether the well is more prone

to violate, or if its owners care more about how much they extract, we include “lagtotnvio”

as an explanatory variable. In addition to these, well-owner attributes can influence an in-

dividual well’s likelihood of shutting down, violation, and rate of extraction. An operator

that has a large number of wells may behave differently than an operator having only a few

wells. Therefore, we control for the number of wells owned by an operator in that year with

the variable “numwells”. To address heterogeneity in the operator’s history of violation, we

include “lagtotnvio op”, that is, the total number of violations done by an operator before

that year, as an explanatory variable in our violation probability analysis. Sets of variables

created from the combined and arranged dataset that are used for different analyses are pre-

sented in Table-3.11 .

3.4 Results:

3.4.1 Survival analysis

First, we see the effect of the fee change in Act-13 on the probability of shut-down of uncon-

ventional wells. We do survival analysis following several duration models to estimate the

probability of a well shut-down. As we discussed in the previous section, the instantaneous

likelihood of shutting down is the hazard function, which is a function of time. And the


survival function captures the probability of continuing the production process after a given

period of time. The idea of survival analysis is to follow an individual well over time and

observe the point in time they experience the traced event, which in this case is the shutting

down of a well. Before we introduce semi-parametric and parametric models with regres-

sors, we use non-parametric estimation for descriptive purposes and to observe the shape of

the hazard and survival functions. In the non-parametric analysis, for each duration t we

determine the number of events (shut down) dt and the number of observations at risk nt

(observations that are still in the sample). Then estimating Equations 3.2 - 3.4, the graphs of

non-parametric hazard function, the Nelson-Aalen estimate of cumulative hazard function,

and the Kaplan-Meier estimate of the survival function for duration t are presented in Figure-

3.12. We know that the hazard function gives the probability of an event, given that the

event has not yet happened. The smoothed non-monotonic hazard estimate shows that over

time the probability of shutting down the production process increases. If a well has been

producing for quite a while then the probability of its shut-down increases. Therefore, the

Nelson-Aalen cumulative hazard estimate from Figure-3.12 indicates that a well producing

for a longer time will have a higher likelihood of shutting down. From the Kaplan-Meier

survival estimate, we can see that in the first period we have 100%, after two periods we have

73%, and this percentage decreases over time. Hence, the Kaplan-Meier survival estimate

shows that the survival probability is decreasing over time, which means all wells have lower

likelihoods of surviving as they produce for longer periods of time.

For the semi-parametric method, we first run the Cox-proportional hazard model. We also

use the Cox-shared frailty model, which is a Cox-proportional model with added group level

random effects, to address the random effect of unconventional well operators. From Kaplan-

Meier survival graphs for different categorical variables, we found that the survival curves are

somewhat parallel. Also, using Schoenfeld and Scaled-Schoenfeld errors to test the propor-

tionality assumption, from Figure-3.13, we can see that this assumption is satisfied. However,

testing proportionality with time-dependent interactions of predictors, we find that for many

explanatory variables, the proportionality assumption is violated. One method found in

the literature for solving this problem is to include time-interacted predictors that violate

proportional assumptions. Therefore, introducing time interacted explanatory variables, we

run the Cox-proportional model and the random effect Cox-proportional model. Results for

these semi-parametric models are presented in Table-??. We can see that all four of these

models yield similar results in terms of the significance of the explanatory variables. The

“Coefficients” columns show whether the explanatory variable has a positive or negative ef-

fect on the probability of shut-down, and the “Hazard rates” columns show the magnitudes


of predictor’s effects on the probability of shut-down.

Our main variable of interest is “treat.” We can see that the variable “treat” is significant

and positive in all four models. This implies that wells, after incurring the unconventional

well fees introduced in Act-13, have a significantly higher likelihoods of being shut-down,

compared to the situation when there was no such fee. Because Cox, Cox RE, Cox-time in-

teracted, and Cox RE-time interacted models give hazard rates for “treat” as 3.84, 3.80, 2.97,

and 3.28 respectively, we can say that unconventional wells incurring the “Impact Fee” are

more than three times likely to shut-down. The increased average price of natural gas reduces

the likelihood of a well’s shut-down, as reflected in the results of the “avg price” variable. A

dollar increase in “avg price” decreases the hazard rate by (100%-11.4%)=88.6%, 86%, 97%

and 94% respectively in our four semi-parametric models. This is because an increased price

will increase the profitability of extraction. Hence, it is reasonable for an increase in price to

increase the probability that a wells will continue producing. Therefore, an increase in the

natural gas price will reduce the probability of shut-down. If “lagtotnvio” increaes by one

unit, it slightly reduces the likelihood of shut down in the four models (respectively 2.4%,

2.3%, 12%, and 13% less). If a well is reported for more violations in all previous years and

still produces, it will have a higher likelihood of producing in the following year.

To discuss the impacts of interaction variables, we need to use the coefficients instead of haz-

ard rates. From the Cox model comparing wells that were not inspected, a unit increase in

price while other variables are held constant yields a hazard ratio equal to exp(-2.17*1)=0.114.

So wells that were not inspected have a (100%-11.4%)=88.6% lower probability of shutting

down when there is a unit increase in the price of natural gas. Now, if wells are inspected

and the price increases by 1 unit, the hazard ratio is exp(-2.17*1+2.04*1)=0.878. Thus,

the probability of shutting down falls by 12.1% with an increase in price for the wells that

were inspected. Therefore, while an increase in price reduces the likelihood that a well stops

producing both when it is inspected and when it is not, uninspected wells have a far lower

likelihood (88.6% vs. 12.1%) of stopping their production. The same can be concluded

from the results of the other three models. Other statistically significant variables in all

four models are “lag prod”, “lagtotnins”, “insp dummy”, “price prod”, and “lagprod treat”.

However, these variables have zero impacts on the probability of shut-down calculated from

the hazard rates. From Table-?? we can see, that conclusions from the parametric models

are the same, and all the explanatory variables have impacts similar to the ones they have

in the semi-parametric models.


3.4.2 Rate of extraction analysis

In order to investigate the impact of Act-13 on the rate of extraction from unconventional

wells, we have used three models: pooled OLS, RE, and FE. Results from these models are

shown in Table-??. For “All years except 2012” we can see that most variables are statis-

tically significant. Our main variable of interest, “treat,” is significant in all three models,

implying the imposition of the “Impact Fee” in the Act-13 in 2012, reduced the rate of natural

gas extraction significantly. The FE model suggests that after the fee is imposed, an uncon-

ventional well produces 346 Mcf less natural gas per day, on average. The variable “inswell”

has a negative impact on the rate of extraction, which means that if a well is inspected more

frequently it will reduce its daily rate of extraction by 56.51 Mcf (from the FE model). If the

number of “viowell” increases by one, the associated daily rate of production will increase by

95.43 Mcf. Variables “lagnins” and “lagtotnvio” increase the daily extraction rate by 64.38

Mcf and 106.9 Mcf respectively in the FE model, while “lagnvio” and “lagtotnins” decrease

the daily extraction rate by 46.26 Mcf and 98.90 Mcf respectively. From the FE model we

can also see that if the price level increases, the extraction rate increases by 28.29 Mcf per

day. Under the “Two-years only” specification, “treat,” is significant and reduces the daily

extraction rate by 506.5 Mcf. This specification also suggests that if a well is inspected more

in the previous year, it may assume it will be inspected less in the following year and hence

increase its extraction rate by 30.81 Mcf.

We also run the Hausman test to see whether the RE or FE model is more appropriate.

The Hausman test suggests that we should use the FE model instead of pooled OLS or RE.

Therefore, based on FE model results, from this analysis we can conclude that the imposition

of the “Impact Fee” on Pennsylvania unconventional wells in 2012 has significantly reduced

the extraction rate of existing wells. This result can be explained as a consequence of the

increase in the long run average cost of extraction that resulted from the fee assessed annually

for fifteen years, coupled with the decreasing price of natural gas.


3.4.3 Probability of violation analysis

We use several binary dependent variable models to analyze the effect of the major fee change

in Act-13 on the probability of violation. Table-?? shows the results of Probit, Random Ef-

fect Probit, and Heckman Probit models under the specification of “All years except 2012”

and “Two-Years (2011,2013)”. We have addressed well-level cluster effects in our analysis.

In this analysis, both the Probit and Random Effect Probit models we run are for the subset

of inspected wells. We estimate the coefficients (elements of β) in Equation-3.13. How-

ever, these estimates cannot be interpreted in the same manner as the normal regression

coefficients. These coefficients give the impact of the explanatory variables on the latent

variable y∗ on y itself. Therefore, in Table-??, in addition to estimates of coefficients, we

present marginal effects of explanatory variables indicated by dy/dx. We can see that in all

the models, under different specifications, the effect of the “treat” variable is always sta-

tistically significant. Under the “All years except 2012” specification, the “treat” variable

significantly decreases the probability of violation by 6% in the Probit model, 5.7% in the

RE Probit model, and 4% in the Heckman Probit model. The variable “avg price” increases

the probability of violation by 2% and 1.6% in Probit and RE Probit models. The variable

“lagvio” is statistically significant for Probit and Heckman Probit models with a marginal

effect of 2% in both models. All other variables under the “All years except 2012” specifi-

cation are either statistically significant with zero marginal effect or statistically insignificant.

The Wald test says that the estimated correlation of the residuals of the two equations (vi-

olation probability and inspection probability) are significant in none of the two Heckman

Probit models. Since the Wald test results suggest that the Heckman model is not appropri-

ate here, we use Probit and RE Probit models (because these will be consistent under this

scenario), and use them for our conclusion. From our analysis, we conclude that the major

increase in unconventional well fees in 2012 reduced the probability of violations by uncon-

ventional wells in Pennsylvania. Models with the two-year specification give the same result.

The results also imply that whenever the price of natural gas increases, it will increase the

violation probability of unconventional wells. In addition to this, if a well was reported for

violation in the previous year, its probability of violation for the following year will be higher.


3.5 Conclusion

Pennsylvania is one of the largest natural gas producing states, producing approximately

7.5% of the total natural gas production in the US, and this percentage is increasing. In

response to the rapid growth of unconventional wells, on February 7, 2012, the Pennsylvania

General Assembly passed comprehensive amendments to the Pennsylvania laws that regulate

the oil and gas industry. On February 14, 2012, Act-13, which is also known as “Impact Fee,”,

was signed into law. Act-13 imposes new and increased fees on the unconventional gas wells

in Pennsylvania. Overnight, the cost of extraction for an unconventional well in Pennsylvania

increased by about 5% (Black 2015). Since Pennsylvania is one of the largest natural gas

producers in the US, this major change in the fee schedule of Pennsylvania unconventional

wells can potentially have a significant impact on shale gas development in the US and can

reshape the hydraulic fracturing industry in Pennsylvania. This might have further implica-

tions for other states, because many states are considering increases in the fee and bonding

requirements for wells that are hydraulically fractured (Davis 2015).

In this chapter we investigated several aspects of Act-13 (Impact Fee) and explored its effects

on the shale gas development and hydraulic fracturing industry in Pennsylvania. From our

analysis, we concluded that this major change in the fee schedule of unconventional wells has

had significant effect on the rate at which unconventional well’s shut-down. Specifically, after

incurring this new unconventional fee, wells have a significantly higher likelihood (more than

three times) of stopping production. This finding is not surprising, since increased unconven-

tional well fees reduce the profitability of the small and medium sized firms that own these

wells. In addition to this, our results imply that the falling natural gas price in the US will

result in an increased likelihood that unconventional wells will shut-down. Therefore, if other

factors remain the same, we can expect shale gas development in Pennsylvania to shrink in

the coming years. Signs of a shrinking unconventional well development can already be seen

in the declining number of new unconventional wells in Pennsylvania after 2012. The number

of newly drilled unconventional wells in Pennsylvania peaked in 2011 and has been decreasing

ever since (Kelso 2015). We also see that while an increase in price reduces the likelihood

of wells stopping production, both when it is inspected and when it is not inspected, unin-

spected wells have a far smaller likelihood of shut-down. One reason behind this result can

be, an increased number of inspections for a well, raises its inspection probability in the

following year (from Heckman model results). Also, an inspected well may incur higher cost

of compliance if inspections are resulted into violations. Hence, if the price of natural gas in-

creases, both of these factors reduces the profitability of inspected wells compared to the wells


that are not inspected. These result into higher probability of surviving for uninspected wells.

From the extraction rate analysis, we can conclude that the imposition of the Impact Fee

on Pennsylvania unconventional wells in 2012 has significantly reduced the extraction rate

of existing wells. The reason for this result could be the increase in the long run average

cost of extraction that resulted from the fee assessed annually for fifteen years, coupled with

decreasing price of natural gas. In addition, our results suggest that this major increase in

unconventional well fees in 2012 reduces the probability of violations by unconventional wells

in Pennsylvania. Even though our results suggest that Act-13 has dampened the shale gas

development growth in Pennsylvania, it has generated significant revenue for the government,

amounting $853.5 million to date (PUC 2015). However, this amount is decreasing every year.


3.6 Appendix: C

Tables, Figures and maps:

Site selection and preparation Excess gas disposal

1. General well spacing rulesa 18. Venting regulations

2. Building setback requirements 19. Flaring Regulations

3. Water setback requirements Production

4. Predrilling water well testing requirements 20. Severance taxesa

Drilling the well Plugging and abandonment

5. Casing/cementing depth regulations 21. Well idle time limits

6. Cement type regulations 22. Temporary abandonment limits

7. Surface casing cement circulation rules Other

8. Intermediate casing cement circulation rules 23. Accident reporting requirements

9. Production casing cement circulation rules 24. State and local bans and moratoriaa

Hydraulic fracturing 25. Number of regulatory agenciesa

10. Water withdrawal limits

11. Fracturing fluid disclosure requirements

Wastewater storage and disposal

12. Fluid storage options

13. Freeboard requirements

14. Pit liner requirements

15. Underground injection regulations

16. Fluid disposal optionsa

17. Wastewater transportation tracking rules aState regulation of this element is described, but the element either does not lend itself to interstate comparisons, or is not tracked in sufficient detail to do so, and is therefore excluded from statistical analysis.

Table 3.1: State Regulations

Year $0-2.25/Mcf $2.26-2.99/Mcf $3.00-4.99/Mcf $5-5.99/Mcf $6/Mcf or higher 1 $40,000 $45,000 $50,000 $55,000 $60,000 2 $30,000 $35,000 $40,000 $45,000 $55,000 3 $25,000 $30,000 $30,000 $40,000 $50,000 4 $10,000 $15,000 $20,000 $20,000 $20,000 5 $10,000 $15,000 $20,000 $20,000 $20,000 6 $10,000 $15,000 $20,000 $20,000 $20,000 7 $10,000 $15,000 $20,000 $20,000 $20,000 8 $10,000 $15,000 $20,000 $20,000 $20,000 9 $10,000 $15,000 $20,000 $20,000 $20,000

10 $10,000 $15,000 $10,000 $10,000 $10,000 11 $5,000 $5,000 $10,000 $10,000 $10,000 12 $5,000 $5,000 $10,000 $10,000 $10,000 13 $5,000 $5,000 $10,000 $10,000 $10,000 14 $5,000 $5,000 $10,000 $10,000 $10,000 15 $5,000 $5,000 $10,000 $10,000 $10,000

Table 3.2: Fee Schedule - Source: Sacavage and Bureau (2014)


Variables Observations Mean Std. Dev. Min Max

Id of Operator 19074.0 48.3 31.9 1.0 103.0

Year 19074.0 2012.5 1.5 2004.0 2014.0

Number of wells per operator per year 19074.0 273.1 220.9 1.0 791.0

Annual number of inspections per op 19074.0 293.8 214.6 0.0 858.0

Annual number of violations per op 19074.0 22.8 21.7 0.0 120.0

Annual number of inspections per well 19074.0 1.4 2.4 0.0 58.0

Annual number of violation per well 19074.0 0.2 1.0 0.0 39.0

Average daily production 19074.0 1557.1 1802.5 0.0 23044.3

Natural gas price 19074.0 6.3 0.7 5.9 11.0

Running sum of annual violations per well 19074.0 0.7 2.2 0.0 39.0

Running sum of annual inspections per well 19074.0 4.0 4.6 0.0 73.0

Running sum of annual violations per operator 19074.0 123.9 106.8 0.0 453.0

Running sum of annual inspections per operator 19074.0 822.5 719.5 0.0 3309.0

Number of gas producing years for each well 19074.0 3.9 1.6 1.0 11.0

Table 3.3: Descriptive Statistics


Variable Variation Mean Std. Dev. Min Max Observations

Number of wells per operator Overall 273.09 220.95 1.00 791.00 N = 19074

between

213.17 1.00 791.00 n = 6300

within

101.47 -150.75 787.99 T-bar = 3.02762

Number of inspections per op overall 293.76 214.58 0.00 858.00 N = 19074

between

191.22 0.00 858.00 n = 6300

within

103.04 -209.90 885.05 T-bar = 3.02762

Number of violations per op overall 22.83 21.72 0.00 120.00 N = 19074

between

15.77 0.00 82.50 n = 6300

within

14.66 -25.67 104.66 T-bar = 3.02762

Number of inspections per well overall 1.39 2.43 0.00 58.00 N = 19074

between

1.91 0.00 34.00 n = 6300

within

1.87 -19.94 35.06 T-bar = 3.02762

Number of violation per well overall 0.17 1.05 0.00 39.00 N = 19074

between

0.58 0.00 12.00 n = 6300

within

0.89 -7.63 31.37 T-bar = 3.02762

Average daily production overall 1557.08 1802.47 0.00 23044.26 N = 19074

between

1779.80 0.03 19348.89 n = 6300

within

873.44 -8675.32 12550.59 T-bar = 3.02762

Total number of violations per well overall 0.66 2.22 0.00 39.00 N = 19074

between

1.77 0.00 39.00 n = 6300

within

0.44 -13.74 7.52 T-bar = 3.02762

Total number of inspections per well overall 3.96 4.63 0.00 73.00 N = 19074

between

4.02 0.00 67.00 n = 6300

within

1.40 -17.04 23.96 T-bar = 3.02762

Total number of violations per operator overall 123.91 106.83 0.00 453.00 N = 19074

between

106.01 0.00 453.00 n = 6300

within

37.17 -115.59 333.91 T-bar = 3.02762

Total number of inspections per operator overall 822.51 719.45 0.00 3309.00 N = 19074

between

671.63 0.00 3309.00 n = 6300

within

397.95 -609.74 3146.95 T-bar = 3.02762

Number of operating years overall 3.88 1.65 1.00 11.00 N = 19074

between

1.61 1.00 11.00 n = 6300

within

0.00 3.88 3.88 T-bar = 3.02762

Natural gas price overall 6.26 0.72 5.89 10.96 N = 19074

between

0.38 5.89 10.96 n = 6300

Within

0.57 3.69 10.08 T-bar = 3.02762

N=6,300; T=11

Table 3.4: Variation Statistics


0

2000

4000

6000

8000

10000N

umbe

r of

unc

onve

ntio

nal w

ells

2004

2006

2008

2010

2012

2014

Year

−10

0

10

20

30

40

50

60

70

80

Num

ber

of o

pera

tors

2004

2006

2008

2010

2012

2014

Year

−2000

0

2000

4000

6000

8000

10000

12000

14000

Num

ber

of in

cide

nts

2004

2006

2008

2010

2012

2014

Year

−400

−200

0

200

400

600

800

1000

1200

1400

1600

Num

ber

of v

iola

tions

2004

2006

2008

2010

2012

2014

Year

1

1.5

2

2.5

3A

vg. n

umbe

r of

insp

ectio

n pe

r w

ell

2004

2006

2008

2010

2012

2014

Year

−.1

0

.1

.2

.3

.4

.5

.6

.7

Avg

. num

ber

of v

iola

tion

per

wel

l

2004

2006

2008

2010

2012

2014

Year

Unconventional wells in Pennsylvania

Figure 3.5: Horizontal wells in Pennsylvania

0

200

400

600

800

Num

ber

of w

ells

per

ope

rato

r

2004

2006

2008

2010

2012

2014

Year

0

500

1000

1500

Num

ber

of in

cide

nts

per

oper

ator

2004

2006

2008

2010

2012

2014

Year

−50

0

50

100

150

200

Num

ber

of v

iola

tions

per

ope

rato

r

2004

2006

2008

2010

2012

2014

Year

Number of wells, inspections and violations per operator

Figure 3.6: Number of wells, inspections and violations per operator


0

10

20

30

40

50

60N

umbe

r of

inci

dent

s pe

r w

ell

2004

2006

2008

2010

2012

2014

Year

0

10

20

30

40

Num

ber

of v

iola

tions

per

wel

l

2004

2006

2008

2010

2012

2014

Year

0

10

20

30

40

Num

ber

of v

iola

itons

by

wel

l

1 2 3 4 5 6 7 8 9 10 11Number of years well operated

Inspections and violations per well

Figure 3.7: Inspections and violations per well

(a) Horizontal Wells in 2008 (b) Horizontal Wells in 2010

(c) Horizontal Wells in 2012 (d) Horizontal Wells in 2014

Figure 3.8: Active Horizontal wells in Pennsylvania


(a) Inspected wells in 2008 (b) Inspected wells in 2010

(c) Inspected wells in 2012 (d) Inspected wells in 2014

Figure 3.9: Inspected wells in Pennsylvania

(a) Violating wells in 2008 (b) Violating wells in 2010

(c) Violating wells in 2012 (d) Violating wells in 2014

Figure 3.10: Violating wells in Pennsylvania


Variables (Survival Analysis) Description avg_price Average annual price of natural gas (per Mcf) lag_prod Average daily production of natural gas in the previous year (in Mcf) lagtotnvio Total number of violations found for the well up to the preceding year lagtotnins Total number of inspections done for the well up to the preceding year insp_dummy =1 if the well was inspected in that year treat =1 if year is after 2012 lag_viol_dummy =1 if the well was reported for violation in the previous year price_prod Interaction of avg_price and lag_prod price_insp_dummy Interaction of avg_price and insp_dummy lagprod_treat Interaction oflag_prod and treat lagtotnvio_insp_dummy Interaction of lagtotnvio and insp_dummy insp_dummy_lagviol_dummy Interaction of insp_dummy and lag_viol_dummy

Variables (Extraction rate analysis) Description

treat =1 if year is after 2012 inswell Number of inspections done for the well in that year viowell Number of violations reported for the well in that year lagnins Number of inspections done for the well in the previous year lagnvio Number of violations found for the well in the previous year lagtotnins Total number of inspections done for the well up to the preceding year lagtotnvio Total number of violations found for the well up to the preceding year avg_price Average annual price of natural gas (per Mcf) num_of_yrs_produced Number of years the well extracted natural gas

Variables (Violation probability analysis) Description

treat =1 if year is after 2012 numwells Number of wells per operator per year production Average daily production of natural gas that year (in Mcf) avg_price Average annual price of natural gas (per Mcf) num_of_yrs_produced Number of years the well extracted natural gas lagprod Average daily production of natural gas in the previous year (in Mcf) lagins =1 if well was inspected in the previous year lagnins Number of inspections done for the well in the previous year lagvio =1 if violation(s) found in the previous year lagnvio Number of violations found for the well in the previous year lagtotnvio Total number of violations found for the well up to the preceding year lagtotnins Total number of inspections done for the well up to the preceding year lagtotnvio_op Total number of violations found for an operator up to the preceding year

Table 3.11: Model’s Variables


.3

.4

.5

.6

.7

0 2 4 6 8 10

Duration

Smoothed hazard estimate

0.00

2.00

4.00

6.00

0 2 4 6 8 10

Duration

Nelson−Aalen cumulative hazard estimate

0.00

0.25

0.50

0.75

1.00

0 2 4 6 8 10

Duration

Kaplan−Meier survival estimate

Non−parametric analysis

Figure 3.12: Non-parametric analysis

−8000

−6000

−4000

−2000

0

2000

scal

ed S

choe

nfel

d −

avg

_pric

e

2 4 6 8 10Time

bandwidth = .8

Test of PH Assumption

−6

−4

−2

0

scal

ed S

choe

nfel

d −

lag_

prod

2 4 6 8 10Time

bandwidth = .8


−30

−20

−10

010

20sc

aled

Sch

oenf

eld

− la

gtot

nvio

2 4 6 8 10Time

bandwidth = .8


−2

0

2

4

6

8

scal

ed S

choe

nfel

d −

lagt

otni

ns

2 4 6 8 10Time

bandwidth = .8


−15000

−10000

−5000

0

5000

scal

ed S

choe

nfel

d −

insp

_dum

my

2 4 6 8 10Time

bandwidth = .8


−6000

−4000

−2000

0

2000

scal

ed S

choe

nfel

d −

trea

t

2 4 6 8 10Time

bandwidth = .8


−100

−50

0

50

100

scal

ed S

choe

nfel

d −

lag_

viol

_dum

my

2 4 6 8 10Time

bandwidth = .8


0

.2

.4

.6

.8

1

scal

ed S

choe

nfel

d −

pric

e_pr

od

2 4 6 8 10Time

bandwidth = .8


−1000

0

1000

2000

scal

ed S

choe

nfel

d −

pric

e_in

sp_d

umm

y

2 4 6 8 10Time

bandwidth = .8


0

.2

.4

.6

.8

scal

ed S

choe

nfel

d −

lagp

rod_

trea

t

2 4 6 8 10Time

bandwidth = .8


−10

0

10

20

scal

ed S

choe

nfel

d −

lagt

otnv

io_i

nsp_

dum

my

2 4 6 8 10Time

bandwidth = .8


−150

−100

−50

0

50

scal

ed S

choe

nfel

d −

insp

_dum

my_

lagv

iol_

dum

my

2 4 6 8 10Time

bandwidth = .8


Figure 3.13: Test of proportional hazard assumption

Syed

Mortu

zaAsif

Ehsan

Chap

ter3.

110

Table 3.14: Survival Analysis (Semi-parametric models)

Cox Cox RE Cox-time interacted Cox RE - time interacted

Coefficients Hazard rates Coefficients Hazard rates Coefficients Hazard rates Coefficients Hazard rates

avg_price -2.168*** 0.1143697 -1.949*** 0.1423682 -3.442*** 0.0319985 -2.760*** 0.0633074

(-4.37)

(-4.09)

(-3.70)

(-3.32)

lag_prod 0.00242*** 1.002419 0.00236*** 1.00E+00 0.00230*** 1.002298 0.00225*** 1.002251

-19.94

-19.73

-15.41

-15.53

lagtotnvio -0.0239* 0.9764272 -0.0224* 0.9778376 -0.129* 0.8791029 -0.121* 0.8863322

(-2.29)

(-2.10)

(-2.47)

(-2.32)

lagtotnins 0.0106** 1.010682 0.00677 1.006795 0.0251 1.025411 0.0284 1.028838

-2.6

-1.55

-1.66

-1.84

1.insp_dummy -12.12*** 5.46E-06 -11.62*** 8.97E-06 -12.95*** 2.37E-06 -12.31*** 4.50E-06

(-5.20)

(-5.10)

(-5.28)

(-5.09)

1.treat 1.345** 3.837236 1.335*** 3.799966 1.091* 2.978508 1.187** 3.276303

-3.21

-3.41

-2.23

-2.68

1.lag_viol_dummy -0.0512 0.9500738 -0.044 0.9569982 0.0305 1.030921 0.019 1.019182

(-0.44)

(-0.37)

-0.25

-0.15

price_prod -0.000361*** 0.9996389 -0.000353*** 0.9996471 -0.000341*** 0.9996588 -0.000334*** 0.9996656

(-20.00)

(-19.79)

(-14.91)

(-15.04)

price_insp_dummy 2.047*** 7.741569 1.959*** 7.090148 2.187*** 8.909198 2.075*** 7.966975

-5.18

-5.07

-5.25

-5.06

Syed

Mortu

zaAsif

Ehsan

Chap

ter3.

111

lagprod_treat -0.000290*** 0.9997103 -0.000283*** 0.9997173 -0.000286*** 0.9997139 -0.000279*** 0.9997209

(-19.34)

(-19.11)

(-18.41)

(-18.28)

lagtotnvio_insp_dummy -0.0149 0.9852142 -0.0131 0.9869598 -0.00518 0.9948382 -0.00619 0.9938334

(-0.86)

(-0.75)

(-0.29)

(-0.34)

insp_dummy_lagviol_dummy -0.311 0.732603 -0.294 0.7449525 -0.326* 0.7217458 -0.304 0.7381235

(-1.90)

(-1.79)

(-1.99)

(-1.85)

price_logt

0.959 2.608565 0.647 1.909281

-1.83

-1.32

lag_prod_logt

0.000116 1.000116 0.000106 1.000106

-1.44

-1.37

lagtotnvio_logt

0.0696* 1.07212 0.0666* 1.06892

-2.07

-1.98

price_prod_logt

-0.0000196 0.9999804 -0.000018 0.999982

(-1.44)

(-1.37)

lagtotnins_logt

-0.0111 0.9889208 -0.0166 0.9834882

(-0.98)

(-1.43)

_cons

ln_p

_cons

Syed

Mortu

zaAsif

Ehsan

Chap

ter3.

112

gamma _cons

N 12609

12609

12609

12609

t statistics in parentheses ="* p<0.05 ** p<0.01

*** p<0.001"

Syed

Mortu

zaAsif

Ehsan

Chap

ter3.

113

Table 3.15: Survival Analysis (Parametric models)

Exponential Weibull Gompertz

Coefficients Hazard rates Coefficients Hazard rates Coefficients Hazard rates

avg_price -2.174*** 0.1136809 127.0*** 1.40E+55 -1.066* 0.3444724

(-4.37)

-70.59

(-2.18)

lag_prod 0.00234*** 1.002344 0.00239*** 1.002393 0.00261*** 1.002614

-16.84

-18.69

-20.14

lagtotnvio -0.130* 0.8779379 -0.115* 0.8914772 -0.0244 0.9758502

(-2.53)

(-2.28)

(-0.61)

lagtotnins 0.024 1.024256 0.0319* 1.032448 0.104*** 1.109895

-1.59

-2.14

-10.15

1.insp_dummy -11.88*** 6.92E-06 -0.988 0.3722072 -11.38*** 0.0000114

(-5.07)

(-0.65)

(-4.87)

1.treat 1.286** 3.618297 1.503*** 4.494908 1.030* 2.801372

-3.05

-4.38

-2.4

1.lag_viol_dummy 0.0403 1.04113 -0.00115 0.9988501 -0.504*** 0.6041678

-0.33

(-0.01)

(-3.98)

price_prod -0.000349*** 0.9996512 -0.000369*** 0.9996309 -0.000393*** 0.9996071

(-16.45)

(-18.34)

(-20.14)

price_insp_dummy 2.006*** 7.432716 0.155 1.168236 1.899*** 6.680862

-5.05

-0.6

-4.79

no caption

Syed

Mortu

zaAsif

Ehsan

Chap

ter3.

114

lagprod_treat -0.000287*** 0.9997132 -0.000217*** 0.9997835 -0.000298*** 0.9997022

(-18.89)

(-16.21)

(-19.07)

lagtotnvio_insp_dummy -0.00537 0.9946408 0.00031 1.00031 0.0154 1.015553

(-0.30)

-0.02

-0.9

insp_dummy_lagviol_dummy -0.334* 0.7163813 -0.295 0.7442459 -0.452** 0.6362123

(-2.04)

(-1.81)

(-2.72)

price_logt 0.0505*** 1.051825 -182.7*** 4.63E-80 -1.741*** 0.1754141

-4.79

(-71.33)

(-76.25)

lag_prod_logt 0.0000669 1.000067 -0.000730*** 0.9992703 -0.000140*** 0.99986

-1.04

(-8.98)

(-3.98)

lagtotnvio_logt 0.0706* 1.073142 0.0634* 1.065432 0.0356 1.036253

-2.14

-1.97

-1.43

price_prod_logt -0.0000114 0.9999886 0.000124*** 1.000124 0.0000241*** 1.000024

(-1.04)

-8.98

-4.02

lagtotnins_logt -0.0104 0.9896969 -0.0187 0.9814843 -0.0754*** 0.9273498

(-0.92)

(-1.67)

(-9.44)

_cons 10.83*** 50648.05 -750.0*** 0 8.296** 4007.219

-3.35

(-70.54)

-2.6

ln_p

_cons

6.981***

-498.22

Syed

Mortu

zaAsif

Ehsan

Chap

ter3.

115

gamma _cons

2.675***

-91.82

N 12609

12609

12609

t statistics in parentheses ="* p<0.05

Syed

Mortu

zaAsif

Ehsan

Chap

ter3.

116

Table 3.16: Effect on the rate of extraction

All years except 2012 Two-years only (2011,2013)

Variables POLS (robust) RE (robust) RE (by well) FE (robust) FE (by well) POLS (robust) RE (robust) RE (by well) FE (robust) FE (by well)

treat -262.6*** -364.6*** -364.6*** -346.0*** -346.0*** -283.4*** -45.13 -45.13 -506.5*** -506.5***

(-4.93) (-12.68) (-12.68) (-11.64) (-11.64) (-5.01) (-1.71) (-1.71) (-16.34) (-16.34)

inswell -28.84 -34.69** -34.69** -56.51*** -56.51*** -76.18*** -53.11*** -53.11*** 12.23 12.23

(-1.92) (-3.17) (-3.17) (-3.57) (-3.57) (-4.00) (-3.62) (-3.62) (0.58) (0.58)

viowell 185.2*** 103.9*** 103.9*** 95.43* 95.43* 311.6*** 194.5*** 194.5*** 83.80 83.80

(4.04) (3.61) (3.61) (2.45) (2.45) (4.45) (4.17) (4.17) (1.45) (1.45)

lagnins 69.11*** 66.93*** 66.93*** 64.38*** 64.38*** 36.26** 37.37*** 37.37*** 30.81* 30.81*

(6.95) (10.53) (10.53) (9.87) (9.87) (2.85) (3.51) (3.51) (2.46) (2.46)

lagnvio -35.00* -46.12*** -46.12*** -46.26*** -46.26*** 16.06 -4.896 -4.896 0.450 0.450

(-2.01) (-4.48) (-4.48) (-4.37) (-4.37) (0.74) (-0.32) (-0.32) (0.03) (0.03)

lagtotnins -55.61*** -75.53*** -75.53*** -98.90*** -98.90*** -38.68*** -43.21*** -43.21*** -4.549 -4.549

(-15.66) (-13.74) (-13.74) (-8.18) (-8.18) (-7.10) (-7.81) (-7.81) (-0.31) (-0.31)

lagtotnvio 76.12*** 103.1*** 103.1*** 106.9** 106.9** 50.34*** 62.74*** 62.74*** 4.982 4.982

(12.99) (9.16) (9.16) (3.26) (3.26) (6.25) (6.72) (6.72) (0.12) (0.12)

avg_price -12.66 29.14* 29.14* 28.29* 28.29* 0 660.9*** 660.9*** 0 0

(-0.50) (2.51) (2.51) (2.23) (2.23) (.) (39.77) (39.77) (.) (.)

num_of_yrs_produced -511.3*** -601.3*** -601.3*** 0 0 -536.1*** -572.4*** -572.4*** 0 0

no caption

Syed

Mortu

zaAsif

Ehsan

Chap

ter3.

117

(-41.26) (-34.55) (-34.55) (.) (.) (-27.63) (-27.75) (-27.75) (.) (.)

_cons 4002.1*** 4274.4*** 4274.4*** 1849.9*** 1849.9*** 4155.0*** 0 0 1788.2*** 1788.2***

(20.86) (39.87) (39.87) (17.14) (17.14) (32.40) (.) (.) (34.47) (34.47)

N 10527 10527 10527 10527 10527 4803 4803 4803 4803 4803


="* p<0.05 ** p<0.01

*** p<0.001"

Syed

Mortu

zaAsif

Ehsan

Chap

ter3.

118

Table 3.17: Effect on the violation probability

All years except 2012 Two-years (2011,2013)

Variable Probit dydx RE Probit dy

dx Heckman dydx Probit dy

dx RE Probit dydx Heckman dy

dx

treat -0.55*** -0.06 -0.60*** -.057 -0.54*** -0.04 -0.54*** -0.05 -0.76** -0.021 -0.51** -0.06

(-4.69)

(-4.58)

(-4.75)

(-4.03)

(-3.06)

(-2.87)

numwells 0.00*** 0.00 0.00*** 0.00 0.00*** 0.00 0.00*** 0.00 0.00*** 0.00 0.00*** 0.00

(-5.17)

(-5.79)

(-5.11)

(-4.51)

(-3.33)

(-4.66)

production 0.00* 4.81E-06 0.00* 0.00 0.00* 3.50E-06 0.00*** 1.21E-05 0.00** 0.00 0.00*** 1.42E-05

(2.06)

(2.17)

(2.10)

(3.37)

(2.83)

(3.40)

avg_price 0.21*** 0.02 0.23*** 0.016 0.21*** 0.02 0 0 0 0 0 0

(3.60)

(3.69)

(3.62)

(.)

(.)

(.)

num_of_yrs_produced -0.03 -0.00 -0.03 0.002 -0.03 -0.00 0.06 0.00 0.09 0.001 0.07 0.01

(-0.66)

(-0.84)

(-0.82)

(0.94)

(0.99)

(0.72)

lagprod -0.00 -6.6E-07 -0.00 0.00 -0.00 -3.3E-07 0.00 6.04E-07 0.00 0.00 0.00 4.01E-07

(-0.27)

(-0.24)

(-0.18)

(0.17)

(0.25)

(0.09)

lagins -0.16 -0.02 -0.16 -0.012 -0.14 -0.01 -0.32** -0.032 -0.46* -0.009 -0.33** -0.04

(-1.93)

(-1.76)

(-1.69)

(-2.85)

(-2.30)

(-2.75)

lagnins 0.00 0.00 0.01 0.00 0.013 0.00 0.02 0.00 0.02 0.00 0.02 0.00

(0.27)

(0.27)

(0.59)

(0.90)

(0.67)

(0.81)

lagvio 0.24* 0.02 0.20 0.016 0.27* 0.02 0.25 0.03 0.36 0.008 0.23 0.03

(1.96)

(1.52)

(2.02)

(1.55)

(1.44)

(1.23)

lagnvio 0.00921 0.000938 0.0174 0.001 -0.00103 -7.6E-05 -0.0184 -0.00188 -0.00263 0.00 -0.0135 -0.0016

(0.26)

(0.43)

(-0.03)

(-0.44)

(-0.03)

(-0.31)

no caption

Syed

Mortu

zaAsif

Ehsan

Chap

ter3.

119

lagtotnvio -0.0454* -0.00463 -0.0530 -0.004 -0.0450* -0.00333 -0.0466 -0.00475 -0.0833 -0.001 -0.0476 -0.00563

(-2.36)

(-1.79)

(-2.37)

(-1.86)

(-1.37)

(-1.91)

lagtotnins 0.0247* 0.002511 0.0256* 0.002 0.0257* 0.001903 0.0152 0.001545 0.0246 0.00 0.0133 0.001573

(2.22)

(2.06)

(2.32)

(0.94)

(0.93)

(0.62)

lagtotnvio_op 0.00267*** 0.000272 0.00282*** 0.00 0.00249*** 0.000184 0.00216** 0.00022 0.00285** 0.00 0.00236** 0.00028

(5.69)

(5.48)

(4.30)

(3.07)

(2.61)

(3.04)

_cons -2.450***

-2.606***

-2.611***

-1.365***

-1.923**

-1.315**

(-5.70)

(-5.60)

(-5.66)

(-3.70)

(-2.81)

(-3.09)

lnsig2u _cons

-1.915*

-0.0279

(-2.24)

(-0.03)

inspection

2206

2206

treat

-0.0599

-0.206***

(-1.14)

(-3.56)

numwells

-0.000120

0.000292*

(-1.51)

(2.37)

production

0.00000500

-0.0000234

(0.50)

(-1.61)

avg_price

0.0187

0

(0.57)

(.)

num_of_yrs_produced

-0.0516***

-0.171***

(-4.31)

(-7.46)

lagprod

0.0000141

0.00000417

(1.39)

(0.28)

lagins

0.0322

0.0535

(0.99)

(1.11)

Syed

Mortu

zaAsif

Ehsan

Chap

ter3.

120

lagnins

0.0830***

0.0393**

(8.79)

(2.67)

lagvio

0.323***

0.259**

(4.71)

(2.67)

lagnvio

-0.0978***

-0.0587*

(-4.77)

(-1.99)

lagtotnvio

-0.00212

0.00382

(-0.24)

(0.26)

lagtotnins

0.0109*

0.0309***

(2.44)

(3.98)

lagtotnvio_op

-0.000934***

-0.00103***

(-5.46)

(-4.15)

_cons

-0.153

0.674***

(-0.65)

(4.75)

athrho

_cons

0.227

-0.114

(0.63)

(-0.19)

N 4232

4232

10508

4795

t statistics in

parentheses ="* p<0.05 ** p<0.01 *** p<0.001"

Chapter 4

References

121

Bibliography

2012. “Act-13 of Feb. 14, 2012.” http://www.legis.state.pa.us/CFDOCS/LEGIS/LI/

uconsCheck.cfm?txtType=HTM&yr=2012&sessInd=0&smthLwInd=0&act=0013.&CFID=

126352892&CFTOKEN=56814378, Accessed: 2010-09-30.

1988. “Regulatory Determination for Oil and Gas and Geothermal Exploration, Development

and Production Rates.” Fed. Regist., pp. 53, 25,446, 25,458.

Section 300h. “Safety of public water systems.” U.S. Code Title 42.

Adair, S.K., B.R. Pearson, J. Monast, and A. Vengosh. 2011. “Considering shale gas extrac-

tion in North Carolina: lessons from other states.” Duke Envtl. L. & Pol’y F. 22:257.

Aguilera, R.F. 2014. “Production costs of global conventional and unconventional petroleum.”

Energy Policy 64:134–140.

Arthur, J.D., B.K. Bohm, B.J. Coughlin, M.A. Layne, D. Cornue, et al. 2009. “Evaluating

the environmental implications of hydraulic fracturing in shale gas reservoirs.” In SPE

Americas E&P environmental and safety conference. Society of Petroleum Engineers.

Arthur, J.D., B. Langhus, and D. Alleman. 2008. “An overview of modern shale gas devel-

opment in the United States.” All Consulting 3:14–17.

Arulampalam, W. 1999. “A note on estimated coefficients in random effects probit models.”

Oxford Bulletin of Economics and Statistics 61:597–602.

Becker, R.A. 2011. “Local environmental regulation and plant-level productivity.” Ecological

Economics 70:2516–2522.

Black, S..W.J., Katie Mccoy. 2015. “WHEN EXTERNALITIES ARE TAXED: THE EF-

FECTS AND INCIDENCE OF PENNSYLVANIA’S IMPACT FEE ON SHALE GAS

WELLS.” Unpublished, www.pitt.edu/~sjm96/working_paper_3_mccoy.pdf.

122

http://www.legis.state.pa.us/CFDOCS/LEGIS/LI/uconsCheck.cfm?txtType=HTM&yr=2012&sessInd=0&smthLwInd=0&act=0013.&CFID=126352892&CFTOKEN=56814378



www.pitt.edu/~sjm96/working_paper_3_mccoy.pdf

Syed Mortuza Asif Ehsan References 123

Brunnermeier, S.B., and A. Levinson. 2004. “Examining the evidence on environmental reg-

ulations and industry location.” The Journal of Environment & Development 13:6–41.

Caputo, M.R. 2005. Foundations of dynamic economic analysis: optimal control theory and

applications . Cambridge University Press.

Chakravorty, U., S. Gerking, and A. Leach. 2010. “State tax policy and oil production.” US

Energy Tax Policy , pp. 759–774.

Chapman, D. 1987. “Computation Techniques for intertemporal allocation of natural re-

sources.” American Journal of Agricultural Economics 69:134–142.

Considine, T.J. 2010. “The Economic Impacts of the Marcellus Shale: Implications for New

York, Pennsylvania, and West Virginia.” report to the American Petroleum Institute,

published by Nat- ural Resource Economics, Inc, Laramie, Wyoming.

Costello, C.J., and D. Kaffine. 2008. “Natural resource use with limited-tenure property

rights.” Journal of Environmental Economics and Management 55(1):20–36.

Dana, D.A., and H.J. Wiseman. 2013. “Market Approach to Regulating the Energy Revo-

lution: Assurance Bonds, Insurance, and the Certain and Uncertain Risks of Hydraulic

Fracturing, A.” Iowa L. Rev. 99:1523.

Dasgupta, P.S., and G.M. Heal. 1979. Economic theory and exhaustible resources . Cambridge

University Press.

Davis, L. 2012. “Modernizing Bonding Requirements for Natural Gas Producers.” The Hamil-

ton Project, Brookings Institution.

Davis, L.W. 2015. “Bonding Requirements for US Natural Gas Producers.” Review of Envi-

ronmental Economics and Policy , pp. reu015.

DEP, P. 2013. “Marcellus Shale: Tough Regulations, Greater Enforcement.” Pennsylvania

Department of Environmental Protection, pp. .

—. 2005. “Pennsylvania Department of Environmental Protection, Oil and Gas Compliance

Report.” Oil and Gas Reporting Web Site, pp. , Bureau of Oil & Gas Management.

Duman, R.J. 2012. “Economic viability of shale gas production in the Marcellus shale; indi-

cated by production rates, costs and current natural gas prices.” PhD dissertation, Michi-

gan Technological University.


EIA. 2015. “Annual energy outlook 2015.”, pp. . U.S. Energy Information Administration,

2015.

Engelder, T., and G. Lash. 2008. “Unconventional natural gas reservoir could boost U.S.

supply.” Penn State Live [Online] 2008, http:// live.psu.edu/story/28116., pp. .

Gerard, D., and E.J. Wilson. 2009. “Environmental bonds and the challenge of long-term

carbon sequestration.” Journal of Environmental Management 90:1097–1105.

Green, E. 2012. “Marcellus shale could be a boon or bane for land owners.” Pittsburgh Post-

Gazette.(Feb. 28, 2010). http://www. post-gazette. com/stories/business/news/marcellus-

shale-could-be-a-boon-or-bane-for-land-owners-235675.

Guo, S., and D. Zeng. 2014. “An overview of semiparametric models in survival analysis.”

Journal of Statistical Planning and Inference 151:1–16.

Halliburton. 2008. “U.S. shale gas: an unconventional resource, unconventional challenges.”

White paper, Halliburton.

Hanna, R. 2010. “US environmental regulation and FDI: evidence from a panel of US-based

multinational firms.” American Economic Journal: Applied Economics 2:158–189.

Holahan, R., and G. Arnold. 2013. “An institutional theory of hydraulic fracturing policy.”

Ecological Economics 94:127–134.

Honore, B.E. 1992. “Trimmed LAD and least squares estimation of truncated and censored

regression models with fixed effects.” Econometrica: journal of the Econometric Society ,

pp. 533–565.

Ikefuji, M., R.J. Laeven, J.R. Magnus, and C. Muris. 2010. “Scrap value functions in dynamic

decision problems.” CentER Discussion Paper Series.

Kargbo, D.M., R.G. Wilhelm, and D.J. Cambell. 2010. “Natural Gas Plays in the Marcel-

lus Shale: Challenges and Potential Opportunities.” Environmental Science & Technology

44:5679–5684.

Keller, W., and A. Levinson. 2002. “Pollution abatement costs and foreign direct investment

inflows to US states.” Review of Economics and Statistics 84:691–703.

Kelso, M. 2015. “Unconventional Drilling Activity Down In Pennsylvania.” Fractracker:

https://www.fractracker.org/2015/07/drilling-activity-down-pa/.

https://www.fractracker.org/2015/07/drilling-activity-down-pa/


Kumar, R.C. 2002. “How long to eat a cake of unknown size? Optimal time horizon under

uncertainty.” Canadian Journal of Economics/Revue canadienne d’economique 35:843–

853.

Kunce, M., S. Gerking, W. Morgan, and R. Maddux. 2003. “State taxation, exploration, and

production in the US oil industry.” Journal of Regional Science 43:749–770.

Laurenzi, I.J., and G.R. Jersey. 2013. “Life cycle greenhouse gas emissions and freshwater

consumption of Marcellus shale gas.” Environmental science & technology 47:4896–4903.

List, J.A., D.L. Millimet, P.G. Fredriksson, and W.W. McHone. 2003. “Effects of environmen-

tal regulations on manufacturing plant births: evidence from a propensity score matching

estimator.” Review of Economics and Statistics 85:944–952.

Martin, R., L.B. de Preux, and U.J. Wagner. 2014. “The impact of a carbon tax on manu-

facturing: Evidence from microdata.” Journal of Public Economics 117:1–14.

McFeeley, M. 2012. State hydraulic fracturing disclosure rules and enforcement: a compari-

son. Natural Resources Defense Council.

Metcalf, G.E. 2008. “Designing a carbon tax to reduce US greenhouse gas emissions.” Review

of Environmental Economics and Policy , pp. ren015.

Myers, T. 2012. “Potential contaminant pathways from hydraulically fractured shale to

aquifers.” Groundwater 50:872–882.

Nelson, L. 2013. “THE FUSS OVER FRACKING: AN EXAMINATION OF THE INSUR-

ANCE ISSUES ASSOCIATED WITH HYDRO-FRACKING.” A Nelson Levine de Luca

& Hamilton White Paper , pp. .

NRDC. April 2015. “Frackings Most Wanted: Lifting the Veil on Oil and Gas Company Spills

and Violations.” NRDC ISSUE PAPER, pp. .

Olmstead, S.M., L.A. Muehlenbachs, J.S. Shih, Z. Chu, and A.J. Krupnick. 2013. “Shale

gas development impacts on surface water quality in Pennsylvania.” Proceedings of the

National Academy of Sciences 110:4962–4967.

Osborn, S.G., A. Vengosh, N.R. Warner, and R.B. Jackson. 2011. “Methane contamination

of drinking water accompanying gas-well drilling and hydraulic fracturing.” proceedings of

the National Academy of Sciences 108:8172–8176.


PennFuture. 2012. “Pennsylvanias New Oil and Gas Law (Act 13): A Plain Language Guide

and Analysis.” Citizens for Pennsylvanias Future, pp. .

PPI-Energy, and C. Team. 2013. “The effects of shale gas production on natural gas prices.”

U.S. Bureau of Labor Statistics 2/13.

PUC. 2015. Public Utility Commission: https://www.act13-reporting.puc.pa.gov/

Modules/PublicReporting/Overview.aspx.

Rahm, D. 2011. “Regulating hydraulic fracturing in shale gas plays: The case of Texas.”

Energy Policy 39:2974–2981.

Reinganum, J.F., and N.L. Stokey. 1985. “Oligopoly extraction of a common property natural

resource: The importance of the period of commitment in dynamic games.” International

Economic Review , pp. 161–173.

Richardson, N., M. Gottlieb, A. Krupnick, and H. Wiseman. 2013. “The State of State Shale

Gas Regulation.” RFF Report 2013.

Roy, A.A., P.J. Adams, and A.L. Robinson. 2014. “Air pollutant emissions from the devel-

opment, production, and processing of Marcellus Shale natural gas.” Journal of the Air &

Waste Management Association 64:19–37.

Ryan, S.P. 2012. “The costs of environmental regulation in a concentrated industry.” Econo-

metrica 80:1019–1061.

Sacavage, K., and L. Bureau. 2014. “Overview of Impact Fee Act, Act 13 of 2012.”

Stiglitz, J.E. 1976. “Monopoly and the rate of extraction of exhaustible resources.” The

American Economic Review , pp. 655–661.

Swindell, G.S. 2016. “Marcellus Shale in Pennsylvania: A 3,800 Well Study of Estimated

Ultimate Recovery (EUR)–March 2016 Update.” In SPE Annual Meeting, Dallas, TX .

Vidic, R., S. Brantley, J. Vandenbossche, D. Yoxtheimer, and J. Abad. 2013. “Impact of

shale gas development on regional water quality.” Science 340.

Warner, N.R., C.A. Christie, R.B. Jackson, and A. Vengosh. 2013. “Impacts of shale gas

wastewater disposal on water quality in Western Pennsylvania.” Environmental science &

technology 47:11849–11857.

https://www.act13-reporting.puc.pa.gov/Modules/PublicReporting/Overview.aspx

https://www.act13-reporting.puc.pa.gov/Modules/PublicReporting/Overview.aspx


Warner, N.R., R.B. Jackson, T.H. Darrah, S.G. Osborn, A. Down, K. Zhao, A. White,

and A. Vengosh. 2012. “Geochemical evidence for possible natural migration of Marcellus

Formation brine to shallow aquifers in Pennsylvania.” Proceedings of the National Academy

of Sciences 109:11961–11966.

Weidner, K. 2013. “Natural Gas Exploration-A Landowner’s Guide to Leasing Land in Penn-

sylvania.” Penn State Extension.

Wiseman, H.J. 2014. “The capacity of states to govern shale gas development risks.” Envi-

ronmental science & technology 48:8376–8387.

three essays on the economics of hydraulic fracturingsyed mortuza asif ehsan abstract hydraulic...

Documents