the local economic impacts of horizontal drilling in …349592/...the local economic impacts of...

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THE LOCAL ECONOMIC IMPACTS OF HORIZONTAL DRILLING IN TEXAS

A thesis presented

by

Jiapei Guo

to The Department of Economics

In partial fulfillment of the requirements for the degree of Master of Arts

in the field of

Economics

Northeastern University Boston, Massachusetts

April, 2015

2

THE LOCAL ECONOMIC IMPACTS OF HORIZONTAL DRILLING IN TEXAS

by

Jiapei Guo

ABSTRACT OF THESIS

Submitted in partial fulfillment of the requirements

for the degree of Master of Arts in Economics

in the College of Social Sciences and Humanities of

Northeastern University

April, 2015

3

ABSTRACT

The combination of horizontal drilling and hydraulic fracturing around 2003 has

generated substantial local economic gains in various areas with unconventional oil and gas

plays across the U.S. This paper estimates the impacts of the horizontal drilling activity on

employment and other labor market outcome measures for counties in Texas from 1995 to 2012.

Results suggest that the boom of horizontal drilling activities has brought increases in

employment and wage and salary income, but decreases in median household income.

Specifically for the period 2003 to 2012, every new horizontal drilling permit issued created on

average 3.5 more jobs in Texas. This paper has two main contributions. First, it includes all 254

counties of Texas into analysis without trimming any metropolitan counties to estimate the

potential different effects of drilling between metropolitan and nonmetropolitan areas. Second, it

employs horizontal drilling permits rather than gas production as the primary measure of

economic outcomes of interest.

4

TABLE OF CONTENTS Abstract 2

Table of Contents 4

Introduction 5

Literature Review 7

Data and Sample Description 12

Empirical Model 18

Results 24

Discussion 35

Conclusion 37

Appendix A 39

References 48

5

Introduction

The combination of horizontal drilling and hydraulic fracturing (fracking) has vastly

expanded oil and natural gas production from low-permeability geologic formations including

shale plays, tight sand plays, and coal bed methane fields, which were previously uneconomic to

drill from (EIA, 2014, September 4). The practice of horizontal drilling can trace back to 1929

and became common at the late 1970s. Horizontal drilling can reach the target geologic layer that

cannot be reached by vertical drilling. It turns horizontally at great depth and is able to reach a

larger area of shale rock than vertical drilling. Thus, horizontal drilling can extract more

hydrocarbons with fewer wells, which reduces the cost of drilling and increases production

(Curtis, 2011). Hydraulic fracturing, a well-stimulation technique, injects pressurized liquid

materials (water, sand and chemicals) into a wellbore to create cracks in the low-permeability

rock formations, which releases gas and oil from tiny pore spaces to flow back to the well. This

technology was commercially used in Texas and Oklahoma in 1949 (Montgomery & Smith,

2010; King, 2012).

Although horizontal drilling costs three times as much per foot as vertical driling, the

combination with hydraulic fracturing can recover the cost by increasing production (King H. ,

n.d.). In addition, the large-scale application of new technologies over the past decade is

stimulated by the markedly increase of wellhead gas prices from $1.92 per thousand cubic feet in

1990s to $5.26 per thousand cubic feet in 2000s.1

1 The natural gas wellhead prices for the U.S. are from the Energy Information Agency (U.S. EIA) at http://www.eia.gov/dnav/ng/hist/n9190us3A.htm.

6

The proliferation of horizontal drilling activities increased the shale gas production in the

U.S. from 1,293 billion cubic feet in 2007 to 11,415 billion cubic feet in 2013.2 The shale gas

production is projected to increase from 10 trillion cubic feet in 2012 to 19.8 trillion cubic feet in

2040, while the share of the total U.S. natural gas production is projected to increase from 40%

in 2012 to 53% in 2040 (EIA, 2014). The long-term projections of shale gas production suggest

that horizontal drilling activities will have a growing influence on local economies in areas with

low-permeability geologic formations.

Texas is the biggest shale gas and crude oil producer in the U.S. It owns one pure tight

sand play, Granite Wash, and four major formations of shale plays and tight sand plays

overlapping with each other which are Barnett Shale, Eagle Ford Shale, Haynesville/Bossier

Shale, and Permian Basin.3 With such a geologic characteristic, Texas accounted one-third of the

increase in shale gas production in the U.S. from 2007 to 2010 and more than half of the increase

in onshore crude oil production from 2000 to 2014.4

2003 is a key time point for Texas or even for the U.S. By 2003 Devon Energy

successfully combined the horizontal drilling with hydraulic fracturing, which led Devon Energy

the largest operator in the Barnett Shale of Texas (Brown, 2014). 182 out of 254 counties in

Texas saw horizontal drilling activities after 2003. As the first mover of applying the two

technologies on gas and oil extraction (Robbins, 2013), Texas’s annual gas production from 2 The shale gas production data are from the Energy Information Agency (U.S. EIA) at http://www.eia.gov/dnav/ng/ng_prod_shalegas_s1_a.htm. 3 Maps of the geographic distribution of shale plays can be found from the Energy Information Agency (U.S. EIA) at http://www.eia.gov/oil_gas/rpd/shale_gas.pdf. For specific information on the five major formations, visit http://www.rrc.state.tx.us/oil-gas/major-oil-gas-formations/. 4 The data of shale gas and crude oil production are from the Energy Information Agency (U.S. EIA) at http://www.eia.gov/dnav/ng/ng_prod_shalegas_s1_a.htm and http://www.eia.gov/dnav/pet/PET_CRD_CRPDN_ADC_MBBL_M.htm.

7

horizontal wells began to exceed that from vertical wells in Barnett Shale from 2006 (EIA, 2011,

July).

What were the local economic impacts of the most recent boom of horizontal drilling in

Texas? This paper aims to use the data of horizontal drilling permits issued and other economic

data to estimate how an increase in permits issued after 2003 can affect total employment, wage

and salary incomes, median household income, and poverty percent in Texas between 1995 and

2012. In addition, I introduce a strong instrument, the percentage coverage of low-permeability

geologic formations in each county, to address the potential endogeneity of drilling at the county

level. The difference-in-difference methodology used by Weber (2012, 2014) is applied in this

paper, and the estimated results can be compared with results of his, which can enrich our

knowledge and understanding of this topic. The following section provides a brief review for

empirical studies concerning the potential economic impacts of previous energy booms.

Literature Review

The innovation of technology for horizontal drilling has contributed to the

unconventional oil and gas boom in the U.S. during the past decade. The boom of drilling

activities has brought an obvious economic impact on areas with unconventional oil and gas

formations such as Texas, Louisiana, Arkansas, Oklahoma, Pennsylvania, and Ohio. Previous

empirical studies focusing on estimating the regional economic impacts of energy booms can

provide valuable insights for my study.

Black et al. (2005) examine the economic impact of coal boom in the 1970s and coal bust

in the 1980s on local employment and earnings in Kentucky, Ohio, Pennsylvania, and West

Virginia. A difference-in-difference method in his study is followed by several subsequent

8

studies such as Weber (2012), Marchand (2012), Weinstein (2014), and Maniloff and

Mastromonaco (2014). This empirical approach is also commonly used in policy evaluation

literature. In the quasi-experiment, both Black et al. (2005) and Marchand (2012) define their

treatment groups as counties that generated at least 10% of total earnings from the sectors of

energy extraction during a specific period of time. Similarly, Weber’s (2012) treatment group is

defined as counties in the top 80% who saw growth in gas production. Weinstein’s (2014)

treatment group is any county that experienced at least a 10% increase in oil and gas employment.

Maniloff and Mastromonaco’s (2014) define three different treatment groups: the first treatment

group is any county lying on shale formations; the second treatment group includes all counties

having shale oil or gas well drilled before 2010; the third treatment group is defined as any

county in the top 25% of shale oil and gas well growth from 2000 to 2010.

In this empirical approach, a binary indicator for whether an observation is in the

treatment group or not is commonly used as the primary variable of interest. A disadvantage of

this approach is that the indicator variable cannot measure the specific magnitude of the impact

of an increase in gas production on outcomes of interest. Weber (2014) and Brisson (2014)

modify this approach by using the continuous detrended gas production as the primary measure

in his empirical models. Thus their studies can estimate how many more jobs and earnings are

created with an increase in each one million cubic feet of gas. Different from them, Maniloff and

Mastromonaco (2014) introduce the continuous count of shale wells as the primary measure to

estimate the growth rates in total employment and average weekly wages. Drilling can be a better

measure than production, since production is more volatile and can significantly lag drilling

which is more labor intensive, so I will use drilling as the primary measure in this paper.

9

Although the empirical methods of these previous studies are similar, the time scope and

regions in samples are different from each other. Weber’s (2012) sample of analysis covers

nonmetropolitan counties from three adjacent states: Colorado, Texas, and Wyoming. The time

period of analysis is from 1993 to 2007, and 1999 is treated as the beginning of boom period

because the three states began to experience a sustained growth in gas production from 1999.

Brisson (2014) focuses on all 273 counties in four main states sitting over the Marcellus Shale

play with the period from 2001 through 2011 in which 2006 is treated as the beginning of

investment and drilling activity. Weinstein (2014) investigates the period of 2001 to 2011 with

the boom period beginning in 2005. Her study is more representative than others by including all

3,060 counties in the lower 48 states of the U.S. Likewise, Maniloff and Mastromonaco (2014)

also investigate the period from 2000 to 2010 by including 3029 counties in the U.S. after

trimming 62 urban counties. Different from studies above, Marchand’s (2012) sample of analysis

focuses on nonmetropolitan areas of Western Canada with a longer period from 1971 to 2006,

during which two booms and one bust of energy extraction are examined and compared.

With regard to the choice of sample, most empirical studies of energy boom and local

labor market drop metropolitan counties by claiming that metropolitan counties’ employment

pattern is different from nonmetropolitan counties (Black, McKinnish, & Sanders, 2005;

Marchand, 2012; Weber, 2012; Maniloff & Mastromonaco, 2014; Weber, 2014; Munasib &

Rickman, 2015). Weber (2012) also suggests that metropolitan counties may exert excessive

increase in employment and income with less drilling than in nonmetropolitan counties without

providing any evidence on it. Although these studies suppose that metropolitan and

nonmetropolitan counties should be treated differently, they neither identify to what extent the

10

difference may exist nor analyze why the potential difference exists, which may simply because

these studies’ instrumental variables are not good enough for their endogenous variables. To

estimate the potential difference, this paper includes all counties in Texas by introducing a

dummy variable for metropolitan counties and the corresponding interaction term with drilling.

Based on these existing empirical studies, the estimated local economic impacts of

energy boom can be various due to differences in the time horizon, the area of analysis, and the

empirical approach. However, they all highlight that the boom of energy is strongly associated

with growth in employment and earnings. Black et al. (2005) suggest that the total employment

of boom counties grows about 2% more than that of non-boom counties, as well as earnings

grow 5% more than earnings in non-boom counties. Marchand (2012) finds that the total

employment grows by 56.6%, and the earnings grow by 83.7% during the two boom periods.

Weber (2012) estimates that 1,780 jobs and $69 million of wage earnings are created in a boom

county during the gas boom period 1999 to 2007, while Brisson (2014) estimates that 1,109 jobs

were created for a drilling county from 2006 to 2011. According to Maniloff and

Mastromonaco’s (2014) estimation, an average county with shale drilling experienced a 2.4%

increase in total employment over the period 2000 to 2010.

In addition to the direct impacts of energy boom on aggregate employment, researchers

also estimate the indirect impacts of energy boom on outcomes of interest in non-energy sectors,

and then confirm somewhat positive spillovers of employment growth generated by energy

sectors. By calculating local job multipliers, Black et al. (2005) find that every ten jobs created in

the coal sector is associated with two jobs created across other local sectors such as construction,

retail, and services in Kentucky, Ohio, Pennsylvania, and West Virginia. Weinstein (2014) finds

11

that every ten jobs created in the oil and gas extraction section, about 4.6 non-oil and gas jobs

can be created in the scope of the U.S. Marchand (2012) suggests that for every ten energy sector

jobs created, 2.9 construction jobs, two retail jobs and 4.6 service jobs will be created in Western

Canada. Weber’s (2014) study shows that 1.4 non-mining jobs are created with each gas-related

mining job in four states, Akansas, Louisiana, Oklahoma, and Texas.

These positive spillovers provide little evidence of “Dutch Disease” in areas with

resource abundance in the short run. However, none of them can confirm the long-run positive

spillovers on other sectors. “Dutch Disease” typically refers to countries with rich natural

resource and a boom in resource extraction suffering a decline in manufacturing sector (Corden,

1984). Resource extraction may crowd out the manufacturing sector by eroding its

competitiveness in the world market (Corden & Neary, 1982). Because the boom of resource

extraction will increase the average wages due to the increase in labor demands, the extra wage

income will be spent on some non-tradable goods, which will in turn increases the prices of these

non-tradable goods. Thus, the manufacturing sector encounters a higher cost of labor and an

appreciation of real exchange rate (Corden & Neary, 1982). However, the boom in extraction

sectors may not necessarily force the manufacturing sector to increase their wages. If the local

labor market has some unemployment or some local workers just commute elsewhere to work,

the boom of extraction may attract many workers without greatly increasing local wages (Weber,

2012). Maniloff and Mastromonaco (2014) find that if a boom county with at least 6.8%

unemployment rate does not have any initial oil and gas workers, the county will not see an

increase in total wages.

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Data and Sample Description

Texas is chosen because it is the biggest on-shore producer of unconventional oil and gas

and the first mover of applying the horizontal drilling with fracking technology. The Railroad

Commission of Texas (Texas RRC) began to implement the Hydraulic Fracturing Disclosure

Rule on February 1, 2012, so the drilling activity with the combination of the two new

technologies can only be implied by the horizontal drilling activity. The data on the horizontal

drilling permit issued are organized at the county level over time and readily available. However,

Texas has its own path on the natural gas production and is quite different from other states in

many aspects, such as population, natural resource, and regulatory framework. Therefore, the

results might not be representative enough to be directly applied to other areas.

The number of the horizontal drilling permits issued in Texas experienced an increasing

trend after 2003 (Figure 1). The horizontal drilling permits issued shot up from holding steady at

854 in 2003 to over 8,252 in 2012. Due to the innovation of technology, the horizontal drilling

permits issued began to boom from 2003 and reached the peak in 2008. The sharp decline in

2009 can be explained by the macroeconomic shock occurred in 2008.

2003 is a critical time point for drilling activity in Texas, because the innovation of

technology remarkably decreased the cost of production and notably increased production

capacities. The annual production from horizontal wells began to exceed the production from

vertical wells at Barnett Shale in 2006, which can account for 90% of total Barnett natural gas

production (EIA, 2011).

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Figure 1. The horizontal drilling permit issued over the period 1995 to 2012 in Texas (N = 254).

Following the success of Barnett Shale, drilling activities occurred in Eagle Ford Shale

from 2008. Since Eagle Ford Shale is a low-permeability geologic play, drilling activities cannot

be played without the combination of hydraulic fracturing. Due to the different development

stages of horizontal drilling and geological conditions in the five geologic formations of Texas, I

will control the fixed effect of geologic formations by including four formation dummies in my

models. Specifically, to regulate and supervise drilling activities, the Texas RRC divides the state

into twelve oil and gas division districts (Figure 2). Therefore, the Granite Wash dummy equals

to one if a county locates in District 10; the Barnett Shale dummy equals to one for counties

locating in District 5, 7B, and 9; the Eagle Ford Shale dummy equals to one for counties locating

in District 1, 2, 3, and 4; the Haynesville/Bossier Shale dummy equals to one if a county locates

in District 6; the Permian Basin equals to one for counties locating in District 7C, 8, and 8A.

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Figure 2. The map of oil and gas division district boundaries5.

Data on the horizontal drilling permit issued at the county level are directly obtained from

the Texas RRC, the state agency regulating oil and gas production. I calculate the sum of

horizontal drilling permits issued from 2004 to 2012 as the primary independent variable. The

period of nine years is chosen because of the estimated lifespan of a natural gas well and the

potential time lag between a permit issued and drilling.

One saying points out that the average lifespan of a natural gas well can reach 20 to 30

years6, while another saying suggests that the productive life of a well in Barnett Shale is only

5 The map is obtained from Texas Railroad Commission at http://www.rrc.state.tx.us/oil-gas/forms/maps/oil-gas-district-boundaries-map/. 6 https://www.encana.com/pdf/communities/usa/LifeOfTheWell2011.pdf.

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BRAZORIA

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TOM GREEN

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DEAF SMITH

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MAVERICK

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COLLIN

MOORE

FANNIN

MOTLEY

MARTIN

LIVE OAK

EL PASO

BAILEY

DALLAS

BOSQUE

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JIM HOGG

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CASTRO

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MATAGORDA

KLEBERG

CROSBY

FAYETTE

BORDEN

WHARTON

SHELBY

NEWTON

MENARD

PARMER

GILLESPIE

WILSON

DICKENS

GRIMES

SCHLEICHER

HASKELL

PANOLA

FOARD

BRISCOE

RANDALL

DAWSON

MIDLAND

HOWARD

MC MULLEN

GONZALES

GRAYSONRED RIVER

SWISHER

ROBERTS

HOCKLEY

TARRANT

ANDERSON

CALHOUN

WALKER

CHEROKEE

LUBBOCK

VICTORIA

BASTROP

SHERMAN

WHEELER

MITCHELL

YOAKUM

STERLINGWINKLER

HEMPHILL

TRINITY

KARNESJACKSON

LIPSCOMB

WILLIAMSON

MC LENNAN

LOVING

AUSTIN

REFUGIO

HOPKINS

EASTLAND

HARRISON

BLANCO

STEPHENS

ANGELINA

CALLAHAN

COLORADO

WILLACY

JEFFERSON

HANSFORD

KAUFMAN

BANDERA

JIMWELLS

PALO PINTO

COMANCHE

WILBARGER

MC CULLOCH

MONTAGUE

OCHILTREE

HAMILTON

COMAL

LIMESTONESABINE

COCHRAN

FORT BEND

CHAMBERS

VAN ZANDT

STONEWALL

JOHNSON

HENDERSON

TITUS

WICHITA

HOOD

FREESTONE

MONTGOMERY

KENDALL

GLASSCOCK

BRAZOS

ARMSTRONG

UPSHUR

ROBERTSON

HUTCHINSON

LAMPASAS

WALLER

CHILDRESS

NACOGDOCHES

BURLESON

SHACKELFORD

HARDEMAN

GUADALUPE

GALVESTON

THROCK-MORTON

MARION

COLLINGSWORTH

CALDWELL

MADISON

SAN PATRICIO

ARANSAS

SANJACINTO

WASHINGTON ORANGE

DELTA

RAINS

GREGG

SAN

AUGUSTINE

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MORRIS

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ROCK-WALL

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Abilene

Houston

Kilgore

San Angelo

San Antonio

Midland

Wichita Falls

Corpus Christi

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8A

8

9

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6

3

2

4

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BREWSTER

HUDSPETH

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VAL VERDE

DUVAL

TERRELL

CROCKETT

FRIO

HARRIS

HILL

BELL

BEE

KENEDY

CLAY

POLK

EDWARDS

JEFF DAVIS

KERR

GAINES

LEON

UVALDE

HALE

DALLAM

IRION

LAMB

KING

DIMMIT

BEXAR

KINNEY

STARR

HALL

WISEJACK

UPTON

SUTTON

HIDALGO

OLDHAM

CASS

ELLIS

MEDINA

KIMBLE

ZAVALA

RUSK

LEE

LYNN KENT

GRAY

COKE

LA SALLE

MILAM

ERATH

HARTLEY

HUNT

SMITH

KNOX

FLOYD

LLANO

ANDREWS

BRAZORIA

TYLER

TRAVISLIBERTY

JONES

REAGAN

BOWIE

WARD

ZAPATA

LAMAR

REAL

NOLAN

NUECES

TERRY GARZA

MILLS

COLEMAN

ECTOR

MASON

TOM GREEN

YOUNG

FALLS

BROWN

HAYS

JASPER

DEAF SMITH

COOKE

BURNET

MAVERICK

HOUSTON

FISHER

LAVACA

COLLIN

MOORE

FANNIN

MOTLEY

MARTIN

LIVE OAK

EL PASO

BAILEY

DALLAS

BOSQUE

HARDIN

JIM HOGG

CAMERON

TAYLOR

COTTLE

POTTER

DONLEY

GOLIAD

SAN SABA

DENTON

CORYELLCRANE

ATASCOSA

CONCHO

BAYLOR

DE WITT

BROOKS

RUNNELS

PARKER

NAVARRO

ARCHER

CARSON

CASTRO

WOOD

SCURRY

MATAGORDA

KLEBERG

CROSBY

FAYETTE

BORDEN

WHARTON

SHELBY

NEWTON

MENARD

PARMER

GILLESPIE

WILSON

DICKENS

GRIMES

SCHLEICHER

HASKELL

PANOLA

FOARD

BRISCOE

RANDALL

DAWSON

MIDLAND

HOWARD

MC MULLEN

GONZALES

GRAYSONRED RIVER

SWISHER

ROBERTS

HOCKLEY

TARRANT

ANDERSON

CALHOUN

WALKER

CHEROKEE

LUBBOCK

VICTORIA

BASTROP

SHERMAN

WHEELER

MITCHELL

YOAKUM

STERLINGWINKLER

HEMPHILL

TRINITY

KARNESJACKSON

LIPSCOMB

WILLIAMSON

MC LENNAN

LOVING

AUSTIN

REFUGIO

HOPKINS

EASTLAND

HARRISON

BLANCO

STEPHENS

ANGELINA

CALLAHAN

COLORADO

WILLACY

JEFFERSON

HANSFORD

KAUFMAN

BANDERA

JIMWELLS

PALO PINTO

COMANCHE

WILBARGER

MC CULLOCH

MONTAGUE

OCHILTREE

HAMILTON

COMAL

LIMESTONESABINE

COCHRAN

FORT BEND

CHAMBERS

VAN ZANDT

STONEWALL

JOHNSON

HENDERSON

TITUS

WICHITA

HOOD

FREESTONE

MONTGOMERY

KENDALL

GLASSCOCK

BRAZOS

ARMSTRONG

UPSHUR

ROBERTSON

HUTCHINSON

LAMPASAS

WALLER

CHILDRESS

NACOGDOCHES

BURLESON

SHACKELFORD

HARDEMAN

GUADALUPE

GALVESTON

THROCK-MORTON

MARION

COLLINGSWORTH

CALDWELL

MADISON

SAN PATRICIO

ARANSAS

SANJACINTO

WASHINGTON ORANGE

DELTA

RAINS

GREGG

SAN

AUGUSTINE

CAMP

MORRIS

FRANKLIN

SOMER-VELL

ROCK-WALL

RAILROAD COMMISSION of TEXASOil and Gas Division

Oil and Gas DivisionDistrict Boundaries

District Office

San Antonio

Houston

Corpus Christi

Kilgore

Abilene

San Angelo

Midland

Wichita Falls

Pampa

1 & 2

3

4

5 & 6

7B

7C

8 & 8A

9

10

15

7.5 years7. Furthermore, Jacquet (2011) suggests that the onsite natural gas extraction workforce

can be categorized into drilling phase jobs and production phase jobs. The Marcellus Shale

Workforce Needs Assessment finds that the drilling phase is estimated to be over 10 years and

can account for over 98% of the workforce needed at the drilling site ((MSETC), 2009).

Although the production phase is less labor-incentive, it can last for 30 years (Jacquet, 2011)

(Figure 3). Actually, the lifespan of a gas well is determined by many different factors, such as

the decline rate of the natural gas production, the unpredictable prices, the production cost versus

the return of natural gas, any regulatory changes, and innovation of technologies. For instance,

the introduction of new technologies may shorten the life of a well by producing more gas within

a shorter period8. On the other hand, there might also be a short time lag between a permit issued

and the beginning of a drilling activity. Therefore, I choose to add up the counts of horizontal

drilling permits issued from 2004 to 2012, which takes into consideration of both the potential

time lag and the estimated lifespan of a well.

With regard to the four economic outcome measures, data on total employment and wage

and salary income for each county are obtained from the Bureau of Economic Analysis9. Data on

poverty percent and median household income are collected from U.S. Census Bureau’s Model-

based Small Area Income & Poverty Estimates program10, which combines administrative and

survey data from various sources to provide more reliable single-year estimates than multi-year

survey estimates. 7 http://energy.wilkes.edu/pages/162.asp. 8 http://www.marcellus-shale.us/Marcellus-production.htm. 9 For more information on the data on employment and wage and salary incomes at the county level: http://www.bea.gov/iTable/index_regional.cfm. 10 For more information on the data on median household income and poverty percent at the county level: http://www.census.gov/did/www/saipe/.

16

Figure 3. The workforce projection from the Jonah natural gas field in Wyoming (Jacquet, 2011).

For control variables, data on income per capita are also from the U.S. Bureau of

Economic Analysis. The population estimates at the county level in 2003 are obtained from

Texas Department of State Health Service11. The data of percentage of people with college

degree or above in 2000 are obtained from the U.S. Department of Agriculture12. The Census

Bureau’s USA counties13database provides the total enrollment of Medicare in 2003, the total

federal government expenditure in FY2003, the total deposits in commercial banks and savings

institutions in 2003, and the housing unit estimates in 2003. The share of government earnings in

2003 is calculated by dividing earnings of government and government enterprises by total 11 For more information on population estimates in 2003: https://www.dshs.state.tx.us/chs/popdat/ST2006.shtm. 12 For more information on the data on educational attainment in 2000: http://www.ers.usda.gov/data-products/county-level-data-sets/education.aspx#.U8MSQY1dXuE. 13 For more information about the data concerning counties in the U.S.: http://censtats.census.gov/usa/usa.shtml.

17

earnings of each county in 2003, the data of which are also gathered from the U.S. Bureau of

Economic Analysis. These variables control various factors that may impact employment,

income, and poverty percent in each county.

The GIS data on low-permeability geologic formations are quite essential for generating

the instrument for the horizontal drilling, which are obtained from the Energy Information

Agency14. The GIS data on the county border of Texas are obtained from Texas Parks and

Wildlife Department15.

The data of metropolitan counties of Texas in 2003 are gathered from Population

Division of U.S. Census Bureau16. According to the Office of Management and Budget, 77 of

254 counties in Texas are treated as metropolitan counties in 2003. The means of horizontal

drilling permits issued between metropolitan and nonmetropolitan counties are not statistically

different in 2003 (Table 1).

Except for the horizontal drilling permits issued in 2003, metropolitan and

nonmetropolitan counties have statistically different means in other dependent and control

variables. Because of the differences, I add the metropolitan dummy and its interaction term to

identify potential different impacts of horizontal drilling activities. In the robustness check, I re-

estimate the model by dropping off the 77 metropolitan counties.

14 The GIS data on low-permeability geologic formations from: http://www.eia.gov/pub/oil_gas/natural_gas/analysis_publications/maps/maps.htm#shaleplay. 15 The shape file of county boundaries is from Texas Parks and Wildlife Department: http://www.tpwd.state.tx.us/gis/data/. 16 http://www.census.gov/population/metro/files/lists/2003/0312mfips.txt.

18

Table 1 Comparing metropolitan and nonmetropolitan counties.

Metropolitan counties (n = 77)

Nonmetropolitan counties (n = 177)

P-value

VARIABLES Mean SD Mean SD Horizontal drilling permits issued, 2003

4.26 13.13 2.97 8.73 0.4323

Employment, 2003 142,459 356,685 8,010 7,714 0.0014 Wage & salary income ($ millions), 2003

4,322 13,000 143 156 0.0060

Median household income, 2003 40,164 9,615 31,078 4,855 0.0000 Poverty percent (%), 2003 14.90 4.86 17.66 4.72 0.0000 Population, 2003 248,826 532,447 16,717 16,161 0.0003 Population density (persons per square mile), 2003

251.75 436.41 18.84 19.28 0.0000

Income per capita, 2003 27,093 4,695 24,741 5.523 0.0013 Educational attainment (percent with a college degree or above), 2000

0.19 0.08 0.14 0.04 0.0000

Enrollment of Medicare, 2003 24,534 46,923 2,771 2,611 0.0001 Federal government expenditure ($ million), 2003

1,463 3,346 99.68 87.40 0.0006

Government share of earnings, 2003 0.21 0.09 0.24 0.10 0.0193 Housing unit, 2003 95,676 204,848 7,392 6,737 0.0003 Deposit ($ million), 2003 3,373 10,566 189 174 0.0100

Empirical Model

Difference-in-difference model is widely applied in estimating the economic effect of

drilling. Black et al. (2005), Marchand (2012), and Weber (2012) design a quasi-experiment by

defining a binary variable indicating whether a county is a boom county (treatment group) or a

non-boom county (control group). However, the binary variable fails to measure the magnitude

of the impact associated with each unit change in natural gas production. To address this issue,

19

Weinstein (2014) introduces a continuous variable, the changes in log of oil and gas employment,

into her model. By interacting the changes in log of oil and gas employment with the boom

county dummy and the after-boom period dummy, she estimates the multiplier effects associated

with an increase in the direct oil and gas employment on non-oil and gas employment. Weber

(2014) and Brisson (2014) use the detrended natural gas production instead of a dummy variable

as the primary measure for local economic outcomes. Likewise, the sum of the horizontal drilling

permits after 2003 is chosen as the primary independent variable for my model.

Black et al. (2005) and Weinstein (2014) apply a panel approach by using year-to-year

employment and income data. Since my instrument is time invariant, I do not use the panel

approach. I detrend dependent variables first to convert the model into a cross-sectional form,

which eliminates any time trend affecting economic outcomes in the pre-drilling period, 1995-

2003. Therefore, the dependent variable 𝑦! is defined as

Yi = (Yi2012 – Yi2003) – (Yi2003 – Yi1995) (1)

where i denotes the specific county in Texas; the dependent variable, Yi denotes total

employment, wage and salary income ($ millions), median household income, and poverty

percent after detrending.

To make the OLS model more robust, I control for some characteristics of counties,

which include a county’s population, population density, income per capita, educational

attainment, enrollment of Medicare, federal government expenditure, deposits in commercial

banks and savings institutions, housing unit estimates, and the share of government earnings. All

these factors possibly affect local economic outcomes and whether a gas company decides to

drill in a certain county.

20

Similar to Weber (2012) and Brisson (2014), the adjacent counties’ characteristics are

also controlled to exclude potential geographic characteristics affecting both local economic

outcomes and the choice of drilling. They are average population, average population density,

and average income per capita of adjacent counties.

To estimate the potential different impacts of drilling on metropolitan and

nonmetropolitan counties, I introduce a dummy variable for metropolitan counties and its

interaction term with drilling. Therefore, the OLS specification is

Yi = β0 + β1 Si2004-2012 + β2 Mi2003 + β3 Mi2003 × Si2004-2012 + β4 Ci2000,2003 + β5 Gd(i) + εi (2)

where 𝑌! is the dependent variable after detrending in equation (1); Si2004-2012 denotes the sum of

horizontal drilling permits from 2004 to 2012; Mi2003 denotes the dummy of metropolitan

counties of Texas in 2003; Ci2000,2003 denotes all controlled variables of the county, which

includes higher education percent in 2000 as well as population, population density, income per

capita, adjacent average population, adjacent average population density, adjacent average

income per capita, total enrollment of Medicare, federal government expenditure, government

share of earnings, housing unit, and total deposits in 2003; Gd(i) are four indicator variables of

geologic formations including Barnett Shale (District 5, 7B, & 9), Eagle Ford Shale (District 1-4),

Haynesvelle/Bossier Shale (District 6), and Permian Basin (District 7C, 8, & 8A).

Although some characteristics of counties have been controlled, other unobservable

factors may affect both the outcome of interest and the decision making process of gas

companies. One major source of endogeneity is that some producers may select counties with

low wages or with large numbers of recently laid off workers, which would bias downwards the

estimate of the effect of drilling on employment outcomes. Another source of endogeneity is that

21

wealthier areas may fight against the drilling activity more strongly and effectively than poor

areas since the gas well may lower the property values in proximity to it (Boxall et al., 2005)

which would also underestimate the effect of drilling on employment outcomes.

To address the confounding endogeneity of drilling, an instrument is introduced.

Following Weber (2012) and Weinstein (2014), I use the percentage coverage of low-

permeability geologic formations for each county as the instrumental variable. The formations

have three different types: the shale play, the tight sand play, and the coalbed methane field.

Specifically, the instrumental variable is calculated by ArcGIS software (Figure 4). The shape

files containing information of county boundaries and unconventional gas play boundaries are

required.

Figure 4. The map of low-permeability geologic formations and the instrument.

Two assumptions are required for a valid instrument. First, the instrumental variable

should be correlated with the variable that it is instrumenting. Second, the instrument variable

should be uncorrelated with other unobserved variables (the error term) that might affect

Adjacent Counties

CBM Field

Shale Play

Tight Play

Counties

Instrument0%-1%

1% - 25%

25%-50%

50%-75%

75%-100%

22

outcome variables. Intuitively, the first assumption is satisfied, because the geologic property17

of a county can decide whether or not oil and gas companies can drill in this county. The larger

the coverage of geologic formations in a county, the greater the potential of drilling this county

may own. However, this condition might be challenged in practice. We cannot expect that the

current knowledge about the geologic property is fully accurate, so a small number of horizontal

drilling activities still happen in some areas where no low-permeability geologic formations are

identified in publicly available map (Maniloff & Mastromonaco, 2014). The second assumption

is also satisfied under most situations, because the subsurface geologic formations would not

directly affect local economic outcomes except through facilitating drilling in this area. However,

it is still correlated with the traditional vertical drilling activity that might have affected the

current changes in employment and wages. Although this instrument strategy has been

implemented by Weber (2012, 2014), Maniloff and Mastromonaco (2014), and Weinstein (2014),

we still bear a few exceptions.

Even so, the geologic formation is strong enough for instrumenting horizontal drilling

based on the first-stage results of instrument. The sum of horizontal drilling permits (Si2004-2012)

and its interaction term with the metropolitan dummy (Mi2003 × Si2004-2012) should be respectively

regressed on their instrumental variables and other control variables in the first-stage regression

models:

Si2004-2012 = π0 + π1 Zi + π2 Mi2003 × Zi + π3 Mi2003 + π4 Ci2000,2003 + π5 Gd(i) + νi (3)

Mi2003×Si2004-2012 = π0 + π1 Zi + π2 Mi2003 × Zi + π3 Mi2003 + π4 Ci2000,2003 + π5 Gd(i) + νi (4)

17 The geologic property here is the percentage coverage of low-permeability geologic formations for each county.

23

where Zi is the percentage of each county locating at the top of low-permeability geologic

formations; Mi2003, Si2004-2012, Ci2000,2003, and Gd(i) are still variables as in equation (2). The

predicted value from the first-stage regression should be and Ŝi2004-2012 and Mi2003 × Ŝi2004-2012.

In the second-stage regression, I regress Yi on the predicted values of the two endogenous

variables, Ŝi2004-2012 and Mi2003 × Ŝi2004-2012:

Yi = δ0 + δ1 Ŝi2004-2012 + δ2 Mi2003 + δ3 Mi2003 × Ŝi2004-2012 + δ4 Ci2000,2003 + δ5 Gd(i) + ei (5)

The rule of thumb for a strong instrument is that the F-statistic in the first-stage

regression should be greater than 10 with the hypothesis that the coefficients on all instruments

should jointly equal to zero (Staiger & Stock, 1997). Stock and Yogo (2005) also suggests the

Cragg-Donald Wald F-statistic can be used to test the weak instruments when there are more

than one endogenous regressor and instrumental variable. The Wald test rejects very often under

weak identification, so the weak instruments will lead to a higher rejection rate given the true

rejection rate is 5% (Baum, Schaffer, & Stillman, 2007). Based on two-stage least squares (TSLS)

size with 5% significance level for two endogenous regressors and two instruments, the critical

value is 7.03 with the rejection rate 10% (Stock & Yogo, 2005). To check the instrument validity,

I run regressions as in equation (3) and (4). The regression results in Table 3 show that the

Cragg-Donald Wald F-statistic is 12.10, which is greater than the threshold suggested by Stock

and Yogo (2005).

I run regressions with the OLS and IV models using two types of samples. The first

sample is the full 254 counties in Texas (Table 2, 3, & 4). The second sample includes 246

counties after trimming the largest metropolitan counties with a population density greater than

24

the 97th percentile in 200318 (Table 5 & 6). The metropolitan dummy and its interaction term are

no longer included in regressions.

Results

With regard to the sample of all counties in Texas, both the OLS and IV regression

results suggest that the sum of horizontal drilling permits is associated with the growth in total

employment in nonmetropolitan counties. However, the effects of drilling are different for

metropolitan counties based on results of OLS and IV models.

The OLS model’s (equation 1 & 2) estimate suggests that the effects of drilling on

employment are different in metropolitan and nonmetropolitan counties. For nonmetropolitan

counties, one drilling permit issued after 2003 is associated with 2.476 more jobs over the period

1995 to 2012. However, metropolitan counties experience a strong negative effect of drilling on

employment, which suggests that one drilling permit issued after 2003 is associated with 2.104

(2.476-4.580) fewer jobs (Table 2).

When I instrument for drilling (equation 1, 3, 4 & 5), one drilling permit issued is

strongly associated with 3.485 more jobs. However, metropolitan counties no longer experience

a strong negative effect of drilling on employment. The effect of drilling on employment in

metropolitan counties is not significant but less than that in nonmetropolitan counties (Table 3).

Although some metropolitan counties own more potentials of drilling (covering a large percent

of the low-permeability geologic formations), they experience a lower employment growth than

nonmetropolitan counties.

18 The largest metropolitan counties in 2003 are: Dallas, Harris, Tarrant, Bexar, Travis, El Paso, Collin, and Galveston.

25

With regard to the wage and salary income, the OLS model’s estimate suggests that one

drilling permit issued after 2003 is associated with an increase of $192 thousand in wage and

salary income, while there is no significantly different effect of drilling between metropolitan

and nonmetropolitan counties (Table 2). Differently, the IV model estimates that drilling has no

significant effect on wage incomes, but metropolitan may have a higher growth in wage incomes

over the period of 1995 to 2012 (Table 4).

The OLS and IV models also provide different estimates on median household income.

According to the OLS results, the effects of drilling on median household income are significant

but different in metropolitan and nonmetropolitan counties. Specifically, every drilling permit

issued after 2003 is associated with $5.533 decreases of median household income in

nonmetropolitan counties but with $2.384 (7.917-5.533) increases of median household income

in metropolitan counties (Table 2). The IV results suggest that the effect of drilling is no longer

significantly different between metropolitan and nonmetropolitan counties (Table 4).

The estimates on poverty percent are basically consistent in both OLS and IV models.

Although the two models have opposite signs on coefficients of drilling and its interaction term,

the estimated effects of drilling are not statistically distinguishable from zero in either OLS or IV

models (Table 2 & 4).

26

Table 2 OLS results with all counties in Texas. (1) (2) (3) (4) VARIABLES Employment Wage &

Salary Incomea Median

Household Income

Poverty Percent

Drilling 2.476*** 0.192** -5.533* 0.00254 (0.546) (0.0780) (3.315) (0.00170)

Metropolitan Dummy 572.4 191.5 -4,123** 1.491 (817.3) (130.8) (2,077) (1.312) Metropolitan Dummy × Drilling -4.580*** -0.277 7.917** -0.00246

(1.453) (0.242) (3.917) (0.00217) Constant -8,061** -1,211** 43,160*** -3.401 (3,869) (523.2) (11,388) (6.992) Controls for geologic formationsb Yes Yes Yes Yes Other control variablesc Yes Yes Yes Yes Observations 254 254 254 254 R-squared 0.896 0.710 0.443 0.173 Adjusted R-squared 0.888 0.687 0.397 0.106 Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. The full regression results are shown in Table A1. Variable definitions: dependent variables, including employment, wage & salary income, median household income, and poverty percent, are detrended by equation (1). Drilling, metropolitan dummy, and their interaction term are main variables of interests. Drilling means the sum of horizontal drilling permits issued from 2004 to 2012. Metropolitan dummy means the metropolitan county of Texas in 2003. aWage and salary income is in terms of millions of 2012 dollars. bThe geologic formation dummy includes Barnett Shale, Eagle Ford Shale, Permian Basin, and Haynesville/Bossier Shale. cAll other control variables include percent with college degree or above in 2000, total population, population density, income per capita, average population, average population density, and average income per capita in adjacent counties, enrollment of Medicare, federal government expenditures ($ million), government share of earnings, housing unit, and total deposit ($ million) in 2003.

27

Table 3 First-stage results with all counties in Texas. (1) (2) VARIABLES Drilling Metropolitan Dummy × Drilling

Percent of low-permeability geologic formations (IV)

305.6*** 33.72 (53.10) (24.82)

Metropolitan Dummy × IV 166.7 423.5** (195.4) (190.1)

Constant 234.8 765.1 (210.5) (293.7) Controls for geologic formationsa Yes Yes Other control variablesb Yes Yes Observations 254 254 F-statistic for excluded instruments 18.29 3.26 Cragg-Donald Wald F-statistic 12.10 12.10 Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. All control variables are suppressed. Variable definitions: Drilling, metropolitan dummy, and their interaction term are main variables of interests. Drilling means the sum of horizontal drilling permits issued from 2004 to 2012. Metropolitan dummy means the metropolitan county of Texas in 2003. aThe geologic formation dummy includes Barnett Shale, Eagle Ford Shale, Permian Basin, and Haynesville/Bossier Shale. bAll other control variables include percent with college degree or above in 2000, total population, population density, income per capita, average population, average population density, and average income per capita in adjacent counties, enrollment of Medicare, federal government expenditures ($ million), government share of earnings, housing unit, and total deposit ($ million) in 2003.

28

Table 4 IV 2sls results with all counties in Texas. (1) (2) (3) (4) VARIABLES Employment Wage & Salary

Incomea Median

Household Income

Poverty Percent

Drilling 3.485** 0.310 -12.68 -0.00105 (1.703) (0.232) (8.040) (0.00440)

Metropolitan Dummy 605.6 225.6* -4,646** 0.726 (824.8) (116.5) (2,204) (1.525) Metropolitan Dummy × Drilling -3.063 -1.002 5.776 0.0114

(4.178) (0.807) (11.64) (0.00781) Constant -10,263** -789.7 52,290*** -10.03 (4,763) (658.6) (12,700) (8.749) Controls for geologic formationsb Yes Yes Yes Yes Other control variablesc Yes Yes Yes Yes Observations 254 254 254 254 Adjusted R-squared 0.882 0.659 0.340 -0.153 Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. The full regression results are shown in Table A2. Variable definitions: dependent variables, including employment, wage & salary income, median household income, and poverty percent, are detrended by equation (1). Drilling, metropolitan dummy, and their interaction term are main variables of interests. Drilling means the sum of horizontal drilling permits issued from 2004 to 2012. Metropolitan dummy means the metropolitan county of Texas in 2003. aWage and salary income is in terms of millions of 2012 dollars. bThe geologic formation dummy includes Barnett Shale, Eagle Ford Shale, Permian Basin, and Haynesville/Bossier Shale. cAll other control variables include percent with college degree or above in 2000, total population, population density, income per capita, average population, average population density, and average income per capita in adjacent counties, enrollment of Medicare, federal government expenditures ($ million), government share of earnings, housing unit, and total deposit ($ million) in 2003.

29

Since most previous empirical studies exclude metropolitan counties from their samples,

I also trim the largest eight metropolitan counties in 2003 to estimate the effects of drilling on the

four economic outcome measures. The corresponding OLS and IV models are also revised by

dropping the metropolitan dummy and its interaction term (Table 5 & 6).

Both OLS and IV regression results suggest that the increase in horizontal drilling permit

issued after 2003 is associated with the growth in total employment and wage and salary incomes.

However, the magnitudes of effects of drilling suggested by the IV regressions are greater than

the OLS estimates. Specifically, one drilling permit issued after 2003 is associated with on

average 0.66 more jobs and $82.7 thousand increases in wage and salary income in the OLS

regression results and with 2.442 more jobs and $211 thousand increases in wage and salary

income in the IV model’s estimates. It is basically consistent with results of Weber’s (2012, 2014)

studies in which the increase of gas production is associated with increases in total employment

and wage incomes after trimming some metropolitan counties.

The estimated effects on median household income and poverty percent are not

statistically significant based on the OLS model. After instrumenting for drilling, the effect of

drilling on median household income becomes significant. One more drilling permit issued is

associated with $13.58 decreases in median household income.

30

Table 5 OLS results with the sample of 246 counties in Texas. (1) (2) (3) (4) VARIABLES Employment Wage & Salary

Incomea Median

Household Income

Poverty Percent

Drilling 0.660* 0.0827*** -2.094 0.00133 (0.384) (0.0232) (1.567) (0.000888)

Constant -5,101 -104.3 41,503*** -2.282 (3,239) (218.6) (11,949) (7.124) Controls for geologic formationsb Yes Yes Yes Yes Other control variablesc Yes Yes Yes Yes Observations 246 246 246 246 R-squared 0.703 0.302 0.392 0.172 Adjusted R-squared 0.681 0.250 0.347 0.111 Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. The full regression results are shown in Table A3. Variable definitions: dependent variables, including employment, wage & salary income, median household income, and poverty percent, are detrended by equation (1). Drilling is the main variable of interests, which means the sum of horizontal drilling permits issued from 2004 to 2012. aWage and salary income is in terms of millions of 2012 dollars. bThe geologic formation dummy includes Barnett Shale, Eagle Ford Shale, Permian Basin, and Haynesville/Bossier Shale. cAll other control variables include percent with college degree or above in 2000, total population, population density, income per capita, average population, average population density, and average income per capita in adjacent counties, enrollment of Medicare, federal government expenditures ($ million), government share of earnings, housing unit, and total deposit ($ million) in 2003.

31

Table 6 IV 2sls results with the sample of 246 counties in Texas. (1) (2) (3) (4) VARIABLES Employment Wage & Salary

Incomea Median

Household Income

Poverty Percent

Drilling 2.442* 0.211** -12.23** 0.00352 (1.251) (0.0823) (6.090) (0.00319)

Constant -6,277* -189.0 48,195*** -3.730 (3,258) (231.5) (12,575) (7.521) Controls for geologic formationsb Yes Yes Yes Yes Other control variablesc Yes Yes Yes Yes F-statistic for IV=0 in the first-stage regression

22.61 22.61 22.61 22.61

Observations 246 246 246 246 R-squared 0.690 0.271 0.330 0.160 Wald 180.86 59.28 175.36 67.82

Note: Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1. The full regression results are shown in Table A4. Variable definitions: dependent variables, including employment, wage & salary income, median household income, and poverty percent, are detrended by equation (1). Drilling is the main variable of interests, which means the sum of horizontal drilling permits issued from 2004 to 2012. aWage and salary income is in terms of millions of 2012 dollars. bThe geologic formation dummy includes Barnett Shale, Eagle Ford Shale, Permian Basin, and Haynesville/Bossier Shale. cAll other control variables include percent with college degree or above in 2000, total population, population density, income per capita, average population, average population density, and average income per capita in adjacent counties, enrollment of Medicare, federal government expenditures ($ million), government share of earnings, housing unit, and total deposit ($ million) in 2003.

32

With regard to the two pairs of OLS and IV models, the pair with all counties is better,

because it identifies the different effects of drilling in metropolitan and nonmetropolitan counties

instead of merely trimming metropolitan counties. In fact, although most metropolitan counties

tend to lack drilling (Black et al., 2005), a few of them still have a great amount of drilling

activities. For example, Tarrant, one of the eight largest counties excluded, owns the most

drilling activities after 2003 in all 254 counties of Texas. Thus, it would be more sophisticated to

analyze all counties in one regression model without losing important information of any large

county with drilling.

Prior studies exclude metropolitan counties without estimating to what extent the effects

of drilling are different, which is similar to the second pair of regressions without eight largest

metropolitan counties into the sample of analysis. The OLS regression model with all counties

estimates the different effects by suggesting that one drilling permit issued is associated with

2.476 more jobs in nonmetropolitan counties, but with 2.104 fewer jobs in metropolitan counties.

After instrumenting for drilling, the estimated effect of drilling is no longer significant but still

negative in metropolitan counties.

Finally, I re-estimate both OLS and IV models for a robustness check. The coefficients

and robust standard errors for the main variables of interest are reported in Table 7. First, I add a

new control variable, the adjacent county dummy, into models above (equation 2, 3, 4, & 5).

Specifically, the adjacent county dummy equals to one if a county shares its borders with any

other counties locating on the surface of the geologic formations (Figure 4). Since the oil and gas

extraction often attracts temporary workers from neighboring counties, especially during the

initial development phase, the adjacent dummy may capture the possible impact of geographical

33

spillovers. Second, instead of just drilling eight metropolitan counties, I trim all 77 metropolitan

counties in 2003. Thus the sample size decreases from 246 to 177. I use the same regression

models with the second pair of regressions above.

After the adjacent county dummy being added, no significant spillovers are captured in

both OLS and IV estimates. The OLS estimates for the four economic variables of interest

remain basically the same as before, except that the effect of drilling on median household

income is no longer statistically significant for nonmetropolitan counties. The IV estimates also

keep consistent as before, except that the effect of drilling on wage and salary income becomes

stronger than before and increases from $231 thousand to $492 thousand.

After trimming all metropolitan counties, the estimates for the effect of drilling on

employment increase from 0.660 to 1.551 in the OLS model and from 2.442 to 2.847 in the IV

model. The level of significance also becomes stronger from 10% to 1%. With regard to the

estimated effect on wage and salary income, the estimates in the OLS model do not see obvious

changes, while the estimate in the IV model decreases from $211 thousand to $144 thousand

with a greater significance level. Furthermore, the estimated effect on median household income

is no longer significance based on both OLS and IV regression results in the robustness check.

Finally, the effect of drilling on poverty percent is not statistically distinguishable from zero

detected by either OLS or IV model, which is the same as before.

34

Table 7 Results from robustness check: coefficients and standard errors for key independent variables. (1) (2) (3) (4) Employment Wage &

Salary Incomea

Median Household

Income

Poverty Percent

Adding adjacent county dummy

OLS Drilling 2.428*** 0.231*** -5.136 0.00244 (0.559) (0.0844) (3.325) (0.00172)

Metropolitan Dummy

580.7 184.9 -4,191** 1.507 (819.2) (130.2) (2,080) (1.318) Metropolitan ×

Drilling -4.557*** -0.296 7.729** -0.00241

(1.466) (0.241) (3.906) (0.00218) Adjacent

Dummy -195.6 156.0 1,615 -0.392

(639.1) (103.1) (1,946) (1.333) IV Drilling 3.742** 0.492* -12.52 -0.000391 (1.872) (0.258) (8.278) (0.00436) Metropolitan

Dummy 615.7 -1.021 -4,640** 0.752

(831.3) (0.787) (2,187) (1.537) Metropolitan ×

Drilling -3.090 232.8** 5.760 0.0113

(4.218) (117.3) (11.57) (0.00794) Adjacent

Dummy 183.8 130.1 107.3 0.472

(661.3) (106.1) (2,176) (1.460) Using all nonmetropolitan counties in 2003

OLS Drilling 1.511*** 0.0896*** -5.404 0.00266 (0.410) (0.0239) (3.512) (0.00189)

IV Drilling 2.847*** 0.144*** -12.17 -0.00102 (0.905) (0.0445) (7.799) (0.00416)

Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. All other control variables are included in regressions but not shown in this table. The full regression results are shown in Table A5-A9. Variable definitions: dependent variables, including employment, wage & salary income, median household income, and poverty percent, are detrended by equation (1). Drilling, metropolitan dummy, and their interaction term are the main variable of interests. Drilling means the horizontal drilling permits issued from 2004 to 2012 and metropolitan dummy means the metropolitan county of Texas in 2003. aWage and salary income is in terms of millions of 2012 dollars.

35

Discussion

The regression results present some interesting findings. First, the horizontal drilling with

technological advancement from 2003 contributed to the local economic growth in Texas by

creating more jobs and increasing wage incomes. This conclusion is basically consistent with

previously published studies.

Second, the effect of drilling on poverty percent is not statistically significant suggested

by all regression models. Since the poverty line is defined in terms of local price levels, it is

possible that although the inflow of drilling workers at the drilling phase increase the living costs

by consuming in local service sectors like house, restaurant, and entertainment, the increase of

local price levels maintains basically consistent with the growth of nominal income.

Third, by introducing the metropolitan dummy into models, I identify and measure the

different effects of drilling which is assumed by previous studies without any specific estimate. I

find some evidence suggesting the different effects on employment and median household

income in metropolitan and nonmetropolitan counties.

With regard to employment, the OLS model with all counties shows that one new well

drilled in nonmetropolitan counties is associated with a greater growth of employment than in

metropolitan counties. Metropolitan counties with drilling even experience decreasing trends in

total employment. One explanation might be that some unobservable factors have dragged the

employment growth in metropolitan areas but are not controlled by the OLS model, so I

introduce the instrument for drilling to avoid this bias. The IV estimates suggest that the

difference of the effect of drilling is no longer significant, but the coefficient on the interaction

dummy is still negative (-3.063), which implies that metropolitan counties with more potential of

36

drilling may experience a less employment growth in Texas. One main reason might be that the

horizontal drilling costs more in metropolitan areas especially after new technologies applied

around 2003. Such a higher cost of drilling can be generated from various facets. On one hand,

hydraulic fracturing can cause potential environmental impacts such as contamination of

groundwater, air pollution, fracking-induced earthquakes, and waste disposal, horizontal drilling

with fracking encounters greater resistance in urban areas. Some cities at the Barnett Shale

region have enacted regulative restrictions on fracking such as Dallas, Southlake and Flower

Mound. In particular, the City of Denton firstly voted for an ordinance of banning fracking in

2014 (Rosendahl, 2015). On the other hand, gas companies may have to pay more for leasing

land or for production royalties in populated areas, because landowner groups are more easily to

be organized to gain bargaining power and negotiating leverage.

Both the OLS and IV models with all counties suggest that drilling is associated with a

greater decrease of median household income in nonmetropolitan counties, which implies that

although the increase of total employment brings the increase in total wage and salary earnings,

most of the salary income is distributed to lower income households in nonmetropolitan counties.

In contrast, drilling is associated with an increase of median household income in metropolitan

counties suggested by the OLS model with all counties, which suggests that the salary income is

skewed away from lower income households in metropolitan areas. However, after

instrumenting for drilling, the difference between metropolitan and nonmetropolitan counties is

no longer significant.

Furthermore, since the sample of analysis only focuses on counties in Texas, it should be

cautious to generalize the results to other areas. The heterogeneity of drilling regulations by

37

states exists in the U.S., which will definitely affect drilling activities (Richardson et al. 2013). In

some states, regulations on oil and gas extraction are very stringent while others are not.

Richardson et al. (2013) measure the stringency of regulatory frameworks across 31 states by

comparing 13 quantitative regulatory elements. Accordingly, Maryland is the most stringent state.

More importantly, the differences in geology, hydrology, weather, and demographics are also

implied by the heterogeneity of regulatory frameworks. Therefore, to gain a further

understanding of the effect of drilling activities we should not only expand the sample size, but

also consider and explore more of those potential differences across areas.

Conclusion

The most recent boom of oil and natural gas drilling has brought a great economic impact

on areas with low-permeability geologic formations. The technology innovation and the raise of

oil and natural gas prices have facilitated the expansion of drilling in Texas since 2003. This

study estimates the effects of horizontal drilling activities on total employment, wage and salary

income, median household income, and poverty percent over the period 1995 to 2012.

The empirical estimation with all counties of Texas (equation 5) is the major contribution

of this paper. The OLS regression results suggest that the impacts of drilling are associated with

significant increases in aggregate employment and wage income, a small decrease in median

household income, and no obvious changes in poverty percent. Moreover, the effects of drilling

on employment and median household income are suggested strongly different between

metropolitan and nonmetropolitan counties. In contrast, the IV regression results only suggest a

significant effect of drilling on aggregate employment. Since 137,047 horizontal drilling permits

38

were issued in Texas from 2004 to 2012, on average 539 more jobs were created for each county

of Texas while 753 more jobs were created for a county with drilling.

The potential differences of drilling effects between metropolitan and nonmetropolitan

areas are measured, which improves previous research on the impact of energy boom. Although

a nonmetropolitan county may experience a greater impact of drilling on employment than a

metropolitan county, a nonmetropolitan county that is a new entry to the mining industry might

bear higher risks, because the economy in rural areas might be less diverse and highly dependent

on drilling. Furthermore, the drilling permit, as a measure to estimate the job creation, is better

than energy production, because the capacity of production of each well can be affected by

geology, hydrology, and other factors, but the demand of new workers for each well remains

relatively stable during the drilling phase.

Overall, the study complements previous research by estimating the potential effects of

horizontal drilling with technological innovation in 2003 on the local labor market of Texas, but

it still has a few aspects that can be studied latter. First, it only captures the effect of drilling due

to the technology innovation without considering other traditional drilling activities. Second,

beside the effect of drilling on total employment and salary income, it does not estimate the

creation of jobs and earnings by industry. Third, it does not predict a long-term effect of drilling

in the future for Texas. Even so, this study still facilitates policy makers to better understand the

cost and benefit of the energy boom that Texas experiences.

39

Appendix A Table A1 OLS results with all counties in Texas. (1) (2) (3) (4) VARIABLES Employment Wage & Salary

Incomea Median Household

Income Poverty Percent

The sum of horizontal drilling permits issued, 2004-2012

2.476*** 0.192** -5.533* 0.00254 (0.546) (0.0781) (3.315) (0.00170)

Metropolitan dummy 572.4 191.5 -4,123** 1.491 (817.3) (130.8) (2,077) (1.312) Metropolitan dummy × Sum of drilling permits

-4.580*** -0.277 7.917** -0.00246 (1.453) (0.242) (3.917) (0.00217)

Percentage with college degree or above, 2000

13,724** 1,350 -42,185** 13.88 (6,628) (1,137) (18,546) (11.37)

Population, 2003 0.0899** 0.0106 -0.0238 1.38e-05 (0.0413) (0.00891) (0.0576) (2.95e-05) Population density, 2003 -38.25*** -12.77*** 14.57** -0.00633* (9.118) (2.748) (6.006) (0.00368) Income per capita, 2003 0.105 0.0151* -0.734*** 0.000204 (0.0655) (0.00908) (0.195) (0.000172) Average population in adjacent counties, 2003

0.0139* 0.00244 0.00507 1.21e-05 (0.00827) (0.00254) (0.0123) (9.05e-06)

Average population density in adjacent counties, 2003

-15.02 -1.751 -36.24** -0.00655 (10.85) (3.032) (15.95) (0.0126)

Average income per capita in adjacent counties, 2003

0.146 0.0200 -0.378 0.000132 (0.102) (0.0140) (0.381) (0.000184)

Enrollment of Medicare, 2003 -0.531*** -0.00180 -0.174 1.47e-05 (0.113) (0.0186) (0.161) (0.000101) Federal government expenditure ($ million), 2003

-0.815 -0.243 2.054 0.000964 (1.350) (0.227) (1.885) (0.00109)

Government share of earnings, 2003 -5,519** -46.56 1,386 -6.133 (2,657) (447.4) (9,848) (5.951)

Housing unit, 2003 0.203** 0.0157 0.00984 -7.81e-05 (0.102) (0.0263) (0.139) (7.99e-05) Deposit ($ million), 2003 -3.068*** -0.276** 0.808 0.000566 (0.530) (0.124) (0.662) (0.000347) Barnett Shale 660.4 125.8 -1,602 -0.675 (727.1) (81.96) (3,098) (1.967) Eagle Ford Shale 1,288 106.2 -1,345 -1.768 (847.1) (114.2) (3,622) (2.180) Permian Basin 2,541*** 217.4** 660.6 -1.844 (953.5) (99.72) (3,336) (2.091) Haynesville/Bossier Shale 2,153 498.3** 2,104 -4.245* (1,380) (243.2) (4,165) (2.292) Constant -8,061** -1,211** 43,160*** -3.401 (3,869) (523.2) (11,388) (6.992) Observations 254 254 254 254 R-squared 0.896 0.710 0.443 0.173 Adjusted R-squared 0.888 0.687 0.397 0.106 Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Dependent variables are detrended by equation (1). aWage and salary income is in terms of millions of 2012 dollars.

40

Table A2 IV 2sls results with all counties in Texas. (1) (2) (3) (4) VARIABLES Employment Wage & Salary

Incomea Median

Household Income

Poverty Percent


3.485** 0.310 -12.68 -0.00105 (1.703) (0.232) (8.040) (0.00440)

Metropolitan dummy 605.6 225.6* -4,646** 0.726 (824.8) (116.5) (2,204) (1.525) Metropolitan dummy × Sum of drilling permits

-3.063 -1.002 5.776 0.0114 (4.178) (0.807) (11.64) (0.00781)


16,911** 679.7 -54,815*** 24.78* (7,470) (1,188) (19,650) (13.19)

Population, 2003 0.0767* 0.0139 0.0240 -4.23e-05 (0.0456) (0.0105) (0.0746) (5.39e-05) Population density, 2003 -39.75*** -12.45*** 20.45** -0.0116 (8.780) (2.603) (9.232) (0.00749) Income per capita, 2003 0.109* 0.0135 -0.746*** 0.000233 (0.0641) (0.00912) (0.189) (0.000162) Average population in adjacent counties, 2003

0.0184* 0.00138 -0.0119 2.99e-05** (0.0106) (0.00266) (0.0167) (1.29e-05)


-23.01 0.127 -6.487 -0.0381* (17.05) (3.585) (25.14) (0.0210)


0.191 0.0118 -0.571 0.000259 (0.117) (0.0155) (0.402) (0.000215)

Enrollment of Medicare, 2003 -0.462*** -0.0182 -0.432 0.000290 (0.162) (0.0252) (0.305) (0.000254) Federal government expenditure ($ million), 2003

-1.783 -0.00491 5.566 -0.00307 (2.063) (0.360) (3.804) (0.00268)

Government share of earnings, 2003 -4,709 -343.9 -612.3 -0.652 (3,007) (523.6) (10,713) (6.967) Housing unit, 2003 0.235** 0.00777 -0.101 5.71e-05 (0.119) (0.0305) (0.192) (0.000150) Deposit ($ million), 2003 -2.985*** -0.295** 0.494 0.000884* (0.529) (0.115) (0.737) (0.000521) Barnett Shale 786.9 123.8 -2,338 -0.768 (727.3) (82.48) (3,045) (2.007) Eagle Ford Shale 1,706 20.53 -3,022 -0.387 (1,066) (154.9) (3,577) (2.314) Permian Basin 2,769*** 200.5* -536.6 -1.728 (968.8) (105.4) (3,280) (2.101) Haynesville/Bossier Shale 2,568* 424.5 329.7 -3.115 (1,510) (266.8) (4,172) (2.361) Constant -10,263** -789.7 52,290*** -10.03 (4,763) (658.6) (12,700) (8.749) Observations 254 254 254 254 Adjusted R-squared 0.882 0.659 0.340 -0.153 Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Dependent variables are detrended by equation (1). aWage and salary income is in terms of millions of 2012 dollars.

41

Table A3 OLS results with the sample of 246 counties in Texas. (1) (2) (3) (4) VARIABLES Employment Wage & Salary

Incomea Median

Household Income

Poverty Percent


0.660* 0.0827*** -2.094 0.00133 (0.384) (0.0232) (1.567) (0.000888)


3,039 -504.4 -46,041** 19.46 (4,559) (307.0) (20,452) (11.95)

Population, 2003 -0.0253 -0.00667 -0.0262 6.56e-05 (0.0541) (0.00471) (0.111) (5.76e-05) Population density, 2003 16.38** 1.182** 1.981 -0.00919 (7.563) (0.465) (20.03) (0.0120) Income per capita, 2003 0.0714 0.00443 -0.721*** 0.000203 (0.0583) (0.00464) (0.186) (0.000171) Average population in adjacent counties, 2003

0.0206* 0.00146** -0.0149 4.99e-06 (0.0106) (0.000578) (0.0189) (1.03e-05)


-25.21** -1.624*** -13.55 -0.000837 (11.93) (0.618) (20.82) (0.0132)


0.129 0.00373 -0.369 9.73e-05 (0.0816) (0.00477) (0.399) (0.000189)

Enrollment of Medicare, 2003 -0.783*** -0.0201* -0.406 0.000163 (0.193) (0.0118) (0.311) (0.000168) Federal government expenditure ($ million), 2003

-3.417 -0.0806 -1.198 -0.000301 (3.413) (0.202) (3.141) (0.00172)

Government share of earnings, 2003 -2,895 -220.5 4,398 -8.037 (1,996) (155.1) (10,535) (6.348) Housing unit, 2003 0.380** 0.0230* 0.0193 -0.000161 (0.159) (0.0121) (0.296) (0.000158) Deposit ($ million), 2003 0.147 0.0613 6.091* -0.00293* (1.937) (0.192) (3.530) (0.00154) Barnett Shale 177.5 -19.57 -1,560 -0.585 (582.1) (33.40) (3,199) (1.953) Eagle Ford Shale 1,039 17.91 -1,717 -1.181 (694.9) (38.39) (3,723) (2.145) Permian Basin 1,667** 93.90 855.0 -1.847 (719.1) (57.07) (3,391) (2.072) Haynesville/Bossier Shale 735.6 -13.62 1,977 -4.008* (984.9) (57.41) (4,229) (2.313) Constant -5,101 -104.3 41,503*** -2.282 (3,239) (218.6) (11,949) (7.124) Observations 246 246 246 246 R-squared 0.703 0.302 0.392 0.172 Adjusted R-squared 0.681 0.250 0.347 0.111 Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Dependent variables are detrended by equation (1). aWage and salary income is in terms of millions of 2012 dollars.

42

Table A4 IV 2sls results with the sample of 246 counties in Texas. (1) (2) (3) (4) VARIABLES Employment Wage & Salary

Incomea Median

Household Income

Poverty Percent


2.442* 0.211** -12.23** 0.00352 (1.251) (0.0823) (6.090) (0.00319)


4,446 -403.1 -54,042*** 21.19* (4,565) (303.8) (19,895) (11.08)

Population, 2003 -0.0294 -0.00696 -0.00303 6.06e-05 (0.0538) (0.00459) (0.110) (5.43e-05) Population density, 2003 17.28** 1.246*** -3.120 -0.00809 (7.287) (0.433) (17.08) (0.0105) Income per capita, 2003 0.0677 0.00417 -0.700*** 0.000198 (0.0555) (0.00438) (0.177) (0.000162) Average population in adjacent counties, 2003

0.0252** 0.00180*** -0.0411* 1.07e-05 (0.0113) (0.000613) (0.0241) (1.25e-05)


-31.96** -2.110*** 24.80 -0.00914 (13.48) (0.707) (30.64) (0.0168)


0.163** 0.00618 -0.563 0.000139 (0.0812) (0.00503) (0.422) (0.000197)


-3.174 -0.0631 -2.581 -1.33e-06 (3.247) (0.190) (3.120) (0.00161)

Government share of earnings, 2003 -3,510* -264.7* 7,895 -8.794 (1,993) (154.5) (10,378) (5.975) Housing unit, 2003 0.385** 0.0233** -0.00404 -0.000156 (0.159) (0.0118) (0.317) (0.000152) Deposit ($ million), 2003 -0.178 0.0380 7.939** -0.00333** (1.900) (0.182) (3.600) (0.00155) Barnett Shale 376.5 -5.250 -2,692 -0.341 (586.0) (34.50) (3,165) (1.916) Eagle Ford Shale 1,307* 37.19 -3,241 -0.851 (719.3) (42.36) (3,695) (2.142) Permian Basin 1,942** 113.6* -705.0 -1.509 (765.1) (62.62) (3,360) (2.019) Haynesville/Bossier Shale 1,088 11.72 -25.72 -3.575 (949.1) (57.54) (4,230) (2.298) Constant -6,277* -189.0 48,195*** -3.730 (3,258) (231.5) (12,575) (7.521) Observations 246 246 246 246 F-statistic for IV=0 in first-stage regression 22.61 22.61 22.61 22.61 R-squared 0.690 0.271 0.330 0.160 Wald 180.86 59.28 175.36 67.82 Note: Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1. Dependent variables are detrended by equation (1). aWage and salary income is in terms of millions of 2012 dollars.

43

Table A5 Robustness check: OLS results with adjacent dummy including all counties in Texas. (1) (2) (3) (4) VARIABLES Employment Wage & Salary

Incomea Median

Household Income

Poverty Percent


2.428*** 0.231*** -5.136 0.00244 (0.559) (0.0844) (3.325) (0.00172)

Metropolitan dummy 580.7 184.9 -4,191** 1.507 (819.2) (130.2) (2,080) (1.318) Metropolitan dummy × Sum of drilling permits

-4.557*** -0.296 7.729** -0.00241 (1.466) (0.241) (3.906) (0.00218)

Adjacent dummy -195.6 156.0 1,615 -0.392 (639.1) (103.1) (1,946) (1.333) Percentage with college degree or above, 2000

13,634** 1,422 -41,441** 13.70 (6,688) (1,141) (18,678) (11.53)

Population, 2003 0.0905** 0.0101 -0.0288 1.50e-05 (0.0416) (0.00893) (0.0598) (3.01e-05) Population density, 2003 -38.28*** -12.75*** 14.79** -0.00638* (9.171) (2.715) (6.021) (0.00371) Income per capita, 2003 0.105 0.0151* -0.735*** 0.000204 (0.0660) (0.00890) (0.198) (0.000173) Average population in adjacent counties, 2003 0.0138* 0.00248 0.00551 1.20e-05

(0.00834) (0.00253) (0.0124) (9.06e-06) Average population density in adjacent counties, 2003

-14.90 -1.852 -37.29** -0.00630 (10.92) (2.993) (16.25) (0.0125)


0.147 0.0194 -0.385 0.000134 (0.103) (0.0143) (0.384) (0.000186)

Enrollment of Medicare, 2003 -0.533*** -0.000601 -0.162 1.17e-05 (0.112) (0.0185) (0.163) (0.000102) Federal government expenditure ($ million), 2003

-0.797 -0.257 1.908 0.001000 (1.342) (0.220) (1.873) (0.00108)

Government share of earnings, 2003 -5,548** -23.48 1,626 -6.191 (2,666) (443.8) (9,827) (5.938) Housing unit, 2003 0.202* 0.0170 0.0237 -8.15e-05 (0.103) (0.0264) (0.143) (8.04e-05) Deposit ($ million), 2003 -3.063*** -0.280** 0.767 0.000576* (0.530) (0.123) (0.658) (0.000345) Barnett Shale 646.0 137.3* -1,483 -0.703 (713.3) (82.26) (3,066) (1.962) Eagle Ford Shale 1,277 115.4 -1,250 -1.791 (832.8) (113.0) (3,595) (2.175) Permian Basin 2,538*** 220.3** 690.1 -1.851 (947.6) (100.6) (3,327) (2.094) Haynesville/Bossier Shale 2,129 517.3** 2,300 -4.293* (1,366) (244.1) (4,162) (2.302) Constant -8,017** -1,246** 42,796*** -3.313 (3,811) (524.4) (11,408) (6.988) Observations 254 254 254 254 R-squared 0.896 0.712 0.444 0.174 Adjusted R-squared 0.887 0.688 0.396 0.103 Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Dependent variables are detrended by equation (1). aWage and salary income is in terms of millions of 2012 dollars.

44

Table A6 Robustness check: first-stage results with adjacent dummy including all counties in Texas. (1) (2) VARIABLES The sum of horizontal

drilling permits, 2004-2012

Metropolitan dummy × Sum of horizontal drilling

permits Percent of low-permeability geologic formations (IV) 315.5*** 34.45 (57.85) (29.94) Metropolitan dummy × IV 170.7 423.7**

(197.2) (191.9) Constant 917.3** 764.7** (358.8) (294.0) Observations 254 254 F-statistic for excluded instruments 15.82 2.76 Cragg-Donald Wald F-statistic 10.62 10.62 Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. All other control variables are suppressed and the same as that in Table A5.

45

Table A7 Robustness check: IV 2sls results with adjacent dummy all counties in Texas. (1) (2) (3) (4) VARIABLES Employment Wage & Salary

Incomea Median

Household Income

Poverty Percent


3.742** 0.492* -12.52 -0.000391 (1.872) (0.258) (8.278) (0.00436)

Metropolitan dummy 615.7 -1.021 -4,640** 0.752 (831.3) (0.787) (2,187) (1.537) Metropolitan dummy × Sum of drilling permits

-3.090 232.8** 5.760 0.0113 (4.218) (117.3) (11.57) (0.00794)

Adjacent dummy 183.8 130.1 107.3 0.472 (661.3) (106.1) (2,176) (1.460) Percentage with college degree or above, 2000

17,291** 948.6 -54,593*** 25.75* (7,703) (1,223) (21,004) (13.75)

Population, 2003 0.0750 0.0128 0.0230 -4.65e-05 (0.0461) (0.0105) (0.0789) (5.77e-05) Population density, 2003 -39.86*** -12.53*** 20.39** -0.0118 (8.771) (2.570) (9.369) (0.00787) Income per capita, 2003 0.109* 0.0136 -0.746*** 0.000234 (0.0640) (0.00897) (0.189) (0.000161) Average population in adjacent counties, 2003

0.0189* 0.00169 -0.0116 3.10e-05** (0.0107) (0.00265) (0.0179) (1.33e-05)


-23.79 -0.431 -6.946 -0.0401* (16.95) (3.607) (27.75) (0.0217)


0.195* 0.0145 -0.569 0.000269 (0.113) (0.0153) (0.404) (0.000215)


-1.877 -0.0715 5.511 -0.00331 (2.039) (0.346) (4.091) (0.00285)

Government share of earnings, 2003 -4,653 -303.8 -579.3 -0.507 (2,975) (507.0) (10,825) (7.106) Housing unit, 2003 0.239** 0.0106 -0.0987 6.73e-05 (0.120) (0.0303) (0.201) (0.000159) Deposit ($ million), 2003 -2.983*** -0.293** 0.495 0.000890* (0.532) (0.114) (0.737) (0.000535) Barnett Shale 821.0 147.9* -2,318 -0.681 (704.3) (83.17) (3,011) (2.003) Eagle Ford Shale 1,756* 56.14 -2,992 -0.258 (1,032) (157.9) (3,542) (2.343) Permian Basin 2,804*** 225.7** -515.8 -1.637 (942.5) (106.8) (3,277) (2.134) Haynesville/Bossier Shale 2,634* 471.0* 368.0 -2.946 (1,501) (275.3) (4,185) (2.404) Constant -10,524** -974.2 52,137*** -10.70 (4,528) (652.2) (13,060) (8.949) Observations 254 254 254 254 Adjusted R-squared 0.880 0.670 0.339 -0.182 Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Dependent variables are detrended by equation (1). aWage and salary income is in terms of millions of 2012 dollars.

46

Table A8 Robustness check: OLS results with nonmetropolitan counties in Texas. (1) (2) (3) (4) VARIABLES Employment Wage & Salary

Incomea Median

Household Income

Poverty Percent


1.511*** 0.0896*** -5.404 0.00266 (0.410) (0.0239) (3.512) (0.00189)


-338.6 -166.6* -28,469 16.66 (1,824) (87.40) (24,112) (14.32)

Population, 2003 0.0595 0.00409 0.0249 0.000152 (0.0468) (0.00299) (0.264) (0.000154) Population density, 2003 -1.148 -0.188 -129.5 -0.00373 (19.89) (1.069) (97.89) (0.0478) Income per capita, 2003 -0.0326** -0.00133* -0.594*** 0.000131 (0.0147) (0.000698) (0.181) (0.000172) Average population in adjacent counties, 2003

-0.0140 -0.000926 0.0716 -3.46e-05 (0.00992) (0.000731) (0.0571) (2.72e-05)


14.77 1.068 -107.0* 0.0545* (10.87) (0.811) (56.73) (0.0301)


0.0175 -0.00225 -0.0461 -1.74e-05 (0.0420) (0.00227) (0.416) (0.000231)

Enrollment of Medicare, 2003 0.180 0.00145 -2.065** 0.000492 (0.184) (0.00971) (0.838) (0.000534) Federal government expenditure ($ million), 2003

-3.153 -0.174 17.99 -0.0256 (5.349) (0.260) (26.00) (0.0192)

Government share of earnings, 2003 -2,337** -252.5*** 3,933 -11.18 (1,133) (82.45) (13,218) (6.819) Housing unit, 2003 -0.202 -0.00478 0.564 -0.000340 (0.162) (0.00870) (0.644) (0.000299) Deposit ($ million), 2003 -0.478 -0.130 2.459 0.00119 (1.851) (0.115) (11.72) (0.00677) Barnett Shale -515.2 -33.32* 1,495 -1.564 (367.4) (19.98) (3,863) (2.261) Eagle Ford Shale -39.94 -12.61 1,873 -1.814 (442.1) (22.21) (4,804) (2.676) Permian Basin -40.73 3.363 587.6 -1.212 (318.6) (17.65) (4,059) (2.365) Haynesville/Bossier Shale -788.7 -58.08 6,203 -4.504 (666.1) (35.64) (4,786) (2.750) Constant 1,923 206.0** 28,263** 3.212 (1,650) (85.91) (12,904) (8.255) Observations 177 177 177 177 R-squared 0.218 0.217 0.208 0.162 Adjusted R-squared 0.134 0.133 0.124 0.072 Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Dependent variables are detrended by equation (1). aWage and salary income is in terms of millions of 2012 dollars.

47

Table A9 Robustness check: IV 2sls results with the nonmetropolitan counties in Texas. (1) (2) (3) (4) VARIABLES Employment Wage & Salary

Incomea Median

Household Income

Poverty Percent


2.847*** 0.144*** -12.17 -0.00102 (0.905) (0.0445) (7.799) (0.00416)


461.0 -133.9 -32,520 14.46 (1,808) (90.74) (23,085) (13.31)

Population, 2003 0.0656 0.00434 -0.00591 0.000135 (0.0448) (0.00282) (0.254) (0.000151) Population density, 2003 -4.639 -0.330 -111.8 0.00587 (17.30) (0.927) (104.1) (0.0457) Income per capita, 2003 -0.0335** -0.00137* -0.589*** 0.000133 (0.0144) (0.000703) (0.173) (0.000161) Average population in adjacent counties, 2003

-0.0149 -0.000966 0.0765 -3.20e-05 (0.00912) (0.000663) (0.0561) (2.58e-05)


16.33* 1.132 -114.9** 0.0502* (9.823) (0.729) (56.59) (0.0281)


0.0278 -0.00183 -0.0983 -4.57e-05 (0.0424) (0.00222) (0.412) (0.000235)

Enrollment of Medicare, 2003 0.178 0.00136 -2.053*** 0.000498 (0.168) (0.00898) (0.778) (0.000521) Federal government expenditure ($ million), 2003

-2.275 -0.139 13.54 -0.0280 (5.143) (0.248) (25.26) (0.0183)

Government share of earnings, 2003 -2,927** -276.5*** 6,921 -9.559 (1,210) (83.61) (12,860) (6.347) Housing unit, 2003 -0.211 -0.00516 0.611 -0.000314 (0.150) (0.00815) (0.584) (0.000298) Deposit ($ million), 2003 -0.765 -0.141 3.912 0.00198 (1.769) (0.111) (11.11) (0.00649) Barnett Shale -381.1 -27.85 816.0 -1.932 (357.8) (19.07) (3,712) (2.262) Eagle Ford Shale 8.864 -10.62 1,626 -1.948 (438.0) (21.98) (4,500) (2.602) Permian Basin 124.3 10.10 -248.7 -1.665 (296.3) (16.02) (3,860) (2.355) Haynesville/Bossier Shale -668.4 -53.17 5,593 -4.834* (596.8) (32.47) (4,506) (2.729) Constant 1,440 186.3** 30,707** 4.538 (1,613) (80.76) (13,008) (8.535) Observations 177 177 177 177 F-statistic for IV=0 in first-stage regression 34.93 34.93 34.93 34.93 R-squared 0.158 0.186 0.189 0.143 Wald 35.69 55.83 75.41 33.78 Note: Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1. Dependent variables are detrended by equation (1). aWage and salary income is in terms of millions of 2012 dollars.

48

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